pid for pri

long: preserve smooth stoppingdeacelrate.
Merge remote-tracking branch 'origin/deep-rl-maybe' into tn
2026-08-07 06:35:41 +08:00 · 2026-08-06 12:51:14 -07:00 · 2026-08-06 10:37:04 -07:00 · 2026-08-05 10:52:53 -07:00 · 2026-08-05 10:51:46 -07:00 · 2026-08-05 10:49:01 -07:00
118 changed files with 8947 additions and 4529 deletions
@@ -2,6 +2,5 @@ Wen
 REGIST
 PullRequest
 cancelled
-indeces
 FOF
 NoO
@@ -12,11 +12,11 @@ on:
        required: false
        type: string
      recompiled_dir:
-        description: 'Existing recompiled directory number (e.g. 3 for recompiled3)'
+        description: 'Existing recompiled directory number (e.g. 1 for recompiled1)'
        required: true
        type: string
      json_version:
-        description: 'driving_models version number to update (e.g. 5 for driving_models_v5.json)'
+        description: 'driving_models version number to update (e.g. 18 for driving_models_v18.json)'
        required: true
        type: string
      artifact_suffix:
@@ -63,12 +63,11 @@ on:
        default: 'None'
        options:
          - None
-          - Simple Plan Models
-          - Space Lab Models
-          - TR Models
-          - DTR Models
+          - Master Models
+          - Release Models
+          - 2025 World Models
+          - 2026 World Models
          - Custom Merge Models
-          - FOF series models
          - Other
      custom_model_folder:
        description: 'Custom model folder name (if "Other" selected)'
@@ -1,39 +0,0 @@
-name: prebuilt
-on:
-  schedule:
-    - cron: '0 * * * *'
-  workflow_dispatch:
-
-env:
-  DOCKER_LOGIN: docker login ghcr.io -u ${{ github.actor }} -p ${{ secrets.GITHUB_TOKEN }}
-  BUILD: release/ci/docker_build_sp.sh
-
-jobs:
-  build_prebuilt:
-    name: build prebuilt
-    runs-on: ubuntu-latest
-    if: github.repository == 'sunnypilot/sunnypilot'
-    env:
-      PUSH_IMAGE: true
-    permissions:
-      checks: read
-      contents: read
-      packages: write
-    steps:
-    - name: Wait for green check mark
-      if: ${{ github.event_name != 'workflow_dispatch' }}
-      uses: lewagon/wait-on-check-action@ccfb013c15c8afb7bf2b7c028fb74dc5a068cccc
-      with:
-        ref: master
-        wait-interval: 30
-        running-workflow-name: 'build prebuilt'
-        repo-token: ${{ secrets.GITHUB_TOKEN }}
-        check-regexp: ^((?!.*(build master-ci|create badges).*).)*$
-    - uses: actions/checkout@v6
-      with:
-        submodules: true
-    - run: git lfs pull
-    - name: Build and Push docker image
-      run: |
-        $DOCKER_LOGIN
-        eval "$BUILD"
@@ -30,6 +30,11 @@ on:
        required: false
        type: string
        default: ''
+      target_hardware:
+        description: 'Hardware target to compile for (qcom or usbgpu)'
+        required: false
+        type: string
+        default: 'qcom'
  workflow_dispatch:
    inputs:
      upstream_branch:
@@ -46,6 +51,14 @@ on:
        required: false
        type: boolean
        default: true
+      target_hardware:
+        description: 'Hardware target to compile for'
+        required: true
+        type: choice
+        options:
+          - qcom
+          - usbgpu
+        default: 'qcom'


 run-name: Build model [${{ inputs.custom_name || inputs.upstream_branch }}] from ref [${{ inputs.upstream_branch }}]
@@ -161,19 +174,30 @@ jobs:
          name: models-${{ env.REF }}${{ inputs.artifact_suffix }}
          path: ${{ env.MODELS_DIR }}
      - run: |
-          rm -f ${{ env.MODELS_DIR }}/{dmonitoring_model,big_driving_policy,big_driving_vision}.onnx
+          rm -f ${{ env.MODELS_DIR }}/{dmonitoring_model,big_driving_policy,big_driving_vision,big_driving_supercombo}.onnx

      - name: Build Model
        run: |
          source /etc/profile
          export UV_PROJECT_ENVIRONMENT=${HOME}/venv
          export VIRTUAL_ENV=$UV_PROJECT_ENVIRONMENT
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
          export PYTHONPATH="${PYTHONPATH}:${{ env.TINYGRAD_PATH }}:${{ github.workspace }}"

          COMPILE_MODELD="${{ github.workspace }}/openpilot/sunnypilot/modeld_v2/compile_modeld.py"
          MODEL_SIZE=$(python3 -c "from openpilot.common.transformations.model import MEDMODEL_INPUT_SIZE as s; print(f'{s[0]}x{s[1]}')")
          CAMERA_RES=$(python3 -c "from openpilot.common.transformations.camera import _ar_ox_fisheye as a, _os_fisheye as o; print(f'{a.width}x{a.height} {o.width}x{o.height}')")
-          TG_FLAGS="DEV=QCOM IMAGE=1 FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 OPENPILOT_HACKS=1"
+
+          if [ "${{ inputs.target_hardware }}" == "usbgpu" ]; then
+            echo "USBGPU build"
+            export USBGPU=1
+            TG_FLAGS="DEV=AMD USBGPU=1 IMAGE=1 FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 OPENPILOT_HACKS=1"
+            OUTPUT_PKL="${{ env.MODELS_DIR }}/big_driving_tinygrad.pkl"
+          else
+            echo "QCOM build"
+            TG_FLAGS="DEV=QCOM IMAGE=1 FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 OPENPILOT_HACKS=1"
+            OUTPUT_PKL="${{ env.MODELS_DIR }}/driving_tinygrad.pkl"
+          fi

          # Generate metadata for all ONNX files
          find "${{ env.MODELS_DIR }}" -maxdepth 1 -name '*.onnx' | while IFS= read -r onnx_file; do
@@ -186,7 +210,13 @@ jobs:
          POLICY_ONNX="${{ env.MODELS_DIR }}/driving_policy.onnx"
          OFF_POLICY_ONNX="${{ env.MODELS_DIR }}/driving_off_policy.onnx"
          ON_POLICY_ONNX="${{ env.MODELS_DIR }}/driving_on_policy.onnx"
-          SUPERCOMBO_ONNX="${{ env.MODELS_DIR }}/supercombo.onnx"
+          SUPERCOMBO_ONNX=""
+          for f in "${{ env.MODELS_DIR }}/supercombo.onnx" "${{ env.MODELS_DIR }}/driving_supercombo.onnx"; do
+            if [ -f "$f" ]; then
+              SUPERCOMBO_ONNX="$f"
+              break
+            fi
+          done

          MODEL_TYPE="" ONNX_ARGS="" OUTPUT_NAME=""
          if [ -f "$VISION_ONNX" ]; then
@@ -207,24 +237,15 @@ jobs:
          fi

          if [ -n "$MODEL_TYPE" ]; then
-            echo "Detected: $MODEL_TYPE -> driving_tinygrad.pkl"
+            echo "Detected: $MODEL_TYPE -> $OUTPUT_PKL"
            env ${TG_FLAGS} python3 "$COMPILE_MODELD" \
              --model-type $MODEL_TYPE \
              --model-size $MODEL_SIZE \
              --camera-resolutions $CAMERA_RES \
              $ONNX_ARGS \
-              --output "${{ env.MODELS_DIR }}/driving_tinygrad.pkl"
+              --output "$OUTPUT_PKL"
          fi

-      - name: Validate Model Outputs
-        run: |
-          source /etc/profile
-          export UV_PROJECT_ENVIRONMENT=${HOME}/venv
-          export VIRTUAL_ENV=$UV_PROJECT_ENVIRONMENT
-          python3 "${{ github.workspace }}/release/ci/model_generator.py" \
-            --validate-only \
-            --model-dir "${{ env.MODELS_DIR }}"
-
      - name: Prepare Output
        run: |
          sudo rm -rf ${{ env.OUTPUT_DIR }}
@@ -4,6 +4,7 @@
 [submodule "opendbc"]
  path = opendbc_repo
  url = https://github.com/sunnypilot/opendbc.git
+  branch = tn
 [submodule "msgq"]
  path = msgq_repo
  url = https://github.com/sunnypilot/msgq.git
@@ -21,12 +21,5 @@
        </clean>
      </configuration>
    </target>
-    <target id="f2590b2b-9b93-49f9-8510-da3f3724a2ae" name="replay" defaultType="TOOL">
-      <configuration id="d475264f-6f4c-4092-9b4e-6773309f38b7" name="replay" toolchainName="Default">
-        <build type="TOOL">
-          <tool actionId="Tool_External Tools_uv build tools replay" />
-        </build>
-      </configuration>
-    </target>
  </component>
 </project>
@@ -20,11 +20,4 @@
      <option name="WORKING_DIRECTORY" value="$ProjectFileDir$" />
    </exec>
  </tool>
-  <tool name="uv build tools replay" showInMainMenu="false" showInEditor="false" showInProject="false" showInSearchPopup="false" disabled="false" useConsole="true" showConsoleOnStdOut="false" showConsoleOnStdErr="false" synchronizeAfterRun="true">
-    <exec>
-      <option name="COMMAND" value="bash" />
-      <option name="PARAMETERS" value="-c &quot;source .venv/bin/activate &amp;&amp; scons -u -j$(nproc) tools/replay/&quot;" />
-      <option name="WORKING_DIRECTORY" value="$ProjectFileDir$" />
-    </exec>
-  </tool>
 </toolSet>
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="Build Debug" type="CLionExternalRunConfiguration" factoryName="Application" REDIRECT_INPUT="false" ELEVATE="false" USE_EXTERNAL_CONSOLE="false" EMULATE_TERMINAL="false" WORKING_DIR="file://$ProjectFileDir$/selfdrive/ui" PASS_PARENT_ENVS_2="true" PROJECT_NAME="openpilot-special" TARGET_NAME="uv Scons Build Debug" CONFIG_NAME="uv Scons Build Debug" RUN_PATH="ui">
+  <configuration default="false" name="Build Debug" type="CLionExternalRunConfiguration" factoryName="Application" REDIRECT_INPUT="false" ELEVATE="false" USE_EXTERNAL_CONSOLE="false" EMULATE_TERMINAL="false" WORKING_DIR="file://$ProjectFileDir$/selfdrive/ui" PASS_PARENT_ENVS_2="true" PROJECT_NAME="sunnypilot" TARGET_NAME="uv Scons Build Debug" CONFIG_NAME="uv Scons Build Debug" RUN_PATH="ui">
    <envs>
      <env name="QT_DBL_CLICK_DIST" value="150" />
    </envs>
@@ -1,27 +0,0 @@
-<component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="Debug Route Controls" type="PythonConfigurationType" factoryName="Python">
-    <module name="openpilot-special" />
-    <option name="ENV_FILES" value="" />
-    <option name="INTERPRETER_OPTIONS" value="" />
-    <option name="PARENT_ENVS" value="true" />
-    <envs>
-      <env name="PYTHONUNBUFFERED" value="1" />
-      <env name="FINGERPRINT" value="KIA_EV9" />
-      <env name="SKIP_FW_QUERY" value="1" />
-    </envs>
-    <option name="SDK_HOME" value="" />
-    <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/selfdrive/car" />
-    <option name="IS_MODULE_SDK" value="true" />
-    <option name="ADD_CONTENT_ROOTS" value="true" />
-    <option name="ADD_SOURCE_ROOTS" value="true" />
-    <EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
-    <option name="SCRIPT_NAME" value="$PROJECT_DIR$/selfdrive/car/card.py" />
-    <option name="PARAMETERS" value="" />
-    <option name="SHOW_COMMAND_LINE" value="false" />
-    <option name="EMULATE_TERMINAL" value="true" />
-    <option name="MODULE_MODE" value="false" />
-    <option name="REDIRECT_INPUT" value="false" />
-    <option name="INPUT_FILE" value="" />
-    <method v="2" />
-  </configuration>
-</component>
@@ -1,7 +0,0 @@
-<component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="Replay for controls + ui" type="Multirun" separateTabs="false" reuseTabsWithFailures="false" startOneByOne="true" markFailedProcess="true" hideSuccessProcess="false" delayTime="0.0">
-    <runConfiguration name="replay for controls" type="Native Application" />
-    <runConfiguration name="Build Debug" type="Custom Build Application" />
-    <method v="2" />
-  </configuration>
-</component>
@@ -1,7 +0,0 @@
-<component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="replay for controls" type="CLionNativeAppRunConfigurationType" focusToolWindowBeforeRun="true" PROGRAM_PARAMS="&quot;$Prompt$&quot; --block &quot;sendcan,carState,carParams,carOutput,liveTracks,carParamsSP,carStateSP,bookmarkButton&quot;" REDIRECT_INPUT="false" ELEVATE="false" USE_EXTERNAL_CONSOLE="false" EMULATE_TERMINAL="true" WORKING_DIR="file://$ProjectFileDir$/tools/replay" PASS_PARENT_ENVS_2="true" PROJECT_NAME="openpilot-special" TARGET_NAME="replay" CONFIG_NAME="replay" version="1" RUN_PATH="replay">
-    <method v="2">
-      <option name="CLION.COMPOUND.BUILD" enabled="true" />
-    </method>
-  </configuration>
-</component>
@@ -69,6 +69,8 @@ struct LeadData {
 struct SelfdriveStateSP @0x81c2f05a394cf4af {
  mads @0 :ModularAssistiveDrivingSystem;
  intelligentCruiseButtonManagement @1 :IntelligentCruiseButtonManagement;
+  buttonsPressed @2 :UInt16;
+  buttonsReleaseToggle @3 :UInt16;

  enum AudibleAlert {
    none @0;
@@ -137,10 +139,16 @@ struct ModelManagerSP @0xaedffd8f31e7b55d {
    eta @2 :UInt32;
  }

+  struct Chunk {
+    fileName @0 :Text;
+    sha256 @1 :Text;
+  }
+
  struct Artifact {
    fileName @0 :Text;
    downloadUri @1 :DownloadUri;
    downloadProgress @2 :DownloadProgress;
+    chunks @3 :List(Chunk);
  }

  struct Model {
@@ -155,6 +163,7 @@ struct ModelManagerSP @0xaedffd8f31e7b55d {
      policy @3;
      offPolicy @4;
      onPolicy @5;
+      chunked @6;
    }
  }

@@ -194,6 +203,7 @@ struct LongitudinalPlanSP @0xf35cc4560bbf6ec2 {
  aTarget @5 :Float32;
  events @6 :List(OnroadEventSP.Event);
  e2eAlerts @7 :E2eAlerts;
+  accelController @8 :AccelController;

  struct DynamicExperimentalControl {
    state @0 :DynamicExperimentalControlState;
@@ -296,6 +306,35 @@ struct LongitudinalPlanSP @0xf35cc4560bbf6ec2 {
    greenLightAlert @0 :Bool;
    leadDepartAlert @1 :Bool;
  }
+
+  struct AccelController {
+    enabled @0 :Bool;
+    active @1 :Bool;
+    shadowOnlyDEPRECATED @2 :Bool;
+    profile @3 :Profile;
+    state @4 :State;
+
+    enum Profile {
+      eco @0;
+      normal @1;
+      sport @2;
+    }
+
+    enum State {
+      inactive @0;
+      free @1;
+      restrict @2;
+      hold @3;
+      release @4;
+      stopHold @5;
+    }
+  }
+
+  enum AccelerationPersonality {
+    eco @0;
+    normal @1;
+    sport @2;
+  }
 }

 struct OnroadEventSP @0xda96579883444c35 {
@@ -342,6 +381,7 @@ struct OnroadEventSP @0xda96579883444c35 {
    speedLimitChanged @21;
    speedLimitPending @22;
    e2eChime @23;
+    laneChangeRoadEdge @24;
  }
 }

@@ -448,6 +488,8 @@ struct LiveMapDataSP @0xf416ec09499d9d19 {

 struct ModelDataV2SP @0xa1680744031fdb2d {
  laneTurnDirection @0 :TurnDirection;
+  leftLaneChangeEdgeBlock @1 :Bool;
+  rightLaneChangeEdgeBlock @2 :Bool;

  enum TurnDirection {
    none @0;
@@ -179,12 +179,20 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {
    {"QuickBootToggle", {PERSISTENT | BACKUP, BOOL, "0"}},
    {"QuietMode", {PERSISTENT | BACKUP, BOOL, "0"}},
    {"RainbowMode", {PERSISTENT | BACKUP, BOOL, "0"}},
+    {"RoadEdgeLaneChangeEnabled", {PERSISTENT | BACKUP, BOOL, "0"}},
    {"RocketFuel", {PERSISTENT | BACKUP, BOOL, "0"}},
    {"ShowAdvancedControls", {PERSISTENT | BACKUP, BOOL, "0"}},
    {"ShowTurnSignals", {PERSISTENT | BACKUP, BOOL, "0"}},
    {"StandstillTimer", {PERSISTENT | BACKUP, BOOL, "0"}},
    {"TrueVEgoUI", {PERSISTENT | BACKUP, BOOL, "0"}},

+    // toyota specific params
+    {"ToyotaAutoHold", {PERSISTENT | BACKUP, BOOL, "0"}},
+    {"ToyotaEnhancedBsm", {PERSISTENT | BACKUP, BOOL, "0"}},
+    {"ToyotaTSS2Long", {PERSISTENT | BACKUP, BOOL, "0"}},
+    {"ToyotaDriveMode", {PERSISTENT | BACKUP, BOOL, "0"}},
+    {"ToyotaPriusTss2Pid", {PERSISTENT | BACKUP, BOOL, "0"}},
+
    // MADS params
    {"Mads", {PERSISTENT | BACKUP, BOOL, "1"}},
    {"MadsMainCruiseAllowed", {PERSISTENT | BACKUP, BOOL, "1"}},
@@ -222,12 +230,17 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {
    {"SubaruStopAndGo", {PERSISTENT | BACKUP, BOOL, "0"}},
    {"SubaruStopAndGoManualParkingBrake", {PERSISTENT | BACKUP, BOOL, "0"}},
    {"TeslaCoopSteering", {PERSISTENT | BACKUP, BOOL, "0"}},
+    {"TeslaMadsScreenButton", {PERSISTENT | BACKUP, INT, "0"}},
    {"ToyotaEnforceStockLongitudinal", {PERSISTENT | BACKUP, BOOL, "0"}},
    {"ToyotaStopAndGoHack", {PERSISTENT | BACKUP, BOOL, "0"}},

    {"DynamicExperimentalControl", {PERSISTENT | BACKUP, BOOL, "0"}},
    {"BlindSpot", {PERSISTENT | BACKUP, BOOL, "0"}},

+    // Accel Controller profiles (Eco / Normal / Sport)
+    {"AccelPersonalityEnabled", {PERSISTENT | BACKUP, BOOL, "0"}},
+    {"AccelPersonality", {PERSISTENT | BACKUP, INT, "1"}},
+
    // sunnypilot model params
    {"CameraOffset", {PERSISTENT | BACKUP, FLOAT, "0.0"}},
    {"LagdToggle", {PERSISTENT | BACKUP, BOOL, "1"}},
@@ -112,12 +112,16 @@ class TestParams:
  def test_params_default_value(self):
    self.params.remove("LanguageSetting")
    self.params.remove("LongitudinalPersonality")
+    self.params.remove("AccelPersonalityEnabled")
+    self.params.remove("AccelPersonality")
    self.params.remove("LiveParametersV2")

    assert self.params.get("LanguageSetting") is None
    assert self.params.get("LanguageSetting", return_default=False) is None
    assert isinstance(self.params.get("LanguageSetting", return_default=True), str)
    assert isinstance(self.params.get("LongitudinalPersonality", return_default=True), int)
+    assert self.params.get("AccelPersonalityEnabled", return_default=True) is False
+    assert self.params.get("AccelPersonality", return_default=True) == 1
    assert self.params.get("LiveParametersV2") is None
    assert self.params.get("LiveParametersV2", return_default=True) is None

@@ -28,8 +28,6 @@ SP_BRANCH_MIGRATIONS = {
  ("tizi", "release3-staging"): "release-tizi-staging",
  ("mici", "release3"): "release-mici",
  ("mici", "release3-staging"): "release-mici-staging",
-  ("tici", "hkg-angle-steering-2025"): "hkg-angle-steering-2025-tici",
-  ("tici", "hkg-angle-steering-2025-prebuilt"): "hkg-angle-steering-2025-tici-prebuilt"
 }

 BUILD_METADATA_FILENAME = "build.json"
@@ -11,7 +11,7 @@ from opendbc.car.structs import car
 from openpilot.common.params import Params
 from openpilot.common.realtime import config_realtime_process, Priority, Ratekeeper
 from openpilot.common.swaglog import cloudlog, ForwardingHandler
-
+from opendbc.safety import ALTERNATIVE_EXPERIENCE
 from opendbc.car import DT_CTRL, structs
 from opendbc.car.can_definitions import CanData, CanRecvCallable, CanSendCallable
 from opendbc.car.carlog import carlog
@@ -122,7 +122,13 @@ class Car:
      self.CI, self.CP, self.CP_SP = CI, CI.CP, CI.CP_SP
      self.RI = RI

+    # set alternative experiences from parameters
+    sp_toyota_auto_brake_hold = self.params.get_bool("ToyotaAutoHold")
    self.CP.alternativeExperience = 0
+    if sp_toyota_auto_brake_hold:
+      self.CP.alternativeExperience |= ALTERNATIVE_EXPERIENCE.ALLOW_AEB
+
+
    # mads
    set_alternative_experience(self.CP, self.CP_SP, self.params)
    set_car_specific_params(self.CP, self.CP_SP, self.params)
@@ -33,7 +33,7 @@ class DesireHelper:
  def get_lane_change_direction(CS):
    return LaneChangeDirection.left if CS.leftBlinker else LaneChangeDirection.right

-  def update(self, carstate, lateral_active, lane_change_prob):
+  def update(self, carstate, lateral_active, lane_change_prob, left_edge_detected=False, right_edge_detected=False):
    self.alc.update_params()
    self.lane_turn_controller.update_params()
    v_ego = carstate.vEgo
@@ -64,8 +64,8 @@ class DesireHelper:
                         ((carstate.steeringTorque > 0 and self.lane_change_direction == LaneChangeDirection.left) or
                          (carstate.steeringTorque < 0 and self.lane_change_direction == LaneChangeDirection.right))

-        blindspot_detected = ((carstate.leftBlindspot and self.lane_change_direction == LaneChangeDirection.left) or
-                              (carstate.rightBlindspot and self.lane_change_direction == LaneChangeDirection.right))
+        blindspot_detected = (((carstate.leftBlindspot or left_edge_detected) and self.lane_change_direction == LaneChangeDirection.left) or
+                              ((carstate.rightBlindspot or right_edge_detected) and self.lane_change_direction == LaneChangeDirection.right))

        self.alc.update_lane_change(blindspot_detected, carstate.brakePressed)

@@ -4,6 +4,7 @@ from openpilot.common.realtime import DT_CTRL
 from openpilot.selfdrive.controls.lib.drive_helpers import CONTROL_N
 from openpilot.common.pid import PIDController
 from openpilot.selfdrive.modeld.constants import ModelConstants
+from openpilot.sunnypilot.selfdrive.controls.lib.longcontrol import LongControlSP

 CONTROL_N_T_IDX = ModelConstants.T_IDXS[:CONTROL_N]

@@ -39,7 +40,7 @@ def long_control_state_trans(CP_SP, active, long_control_state,

  return long_control_state

-class LongControl:
+class LongControl(LongControlSP):
  def __init__(self, CP, CP_SP):
    self.CP = CP
    self.CP_SP = CP_SP
@@ -66,7 +67,7 @@ class LongControl:

    elif self.long_control_state == LongCtrlState.stopping:
      output_accel = self.last_output_accel
-      if output_accel > self.CP.stopAccel:
+      if output_accel > self.CP.stopAccel and not LongControlSP.should_hold_stopping(self, CS, a_target):
        output_accel = min(output_accel, 0.0)
        # TODO: can we just go straight to stopAccel?
        output_accel -= 1.0 * DT_CTRL  # m/s^2/s while trying to stop
@@ -9,6 +9,7 @@ from openpilot.common.swaglog import cloudlog
 # WARNING: imports outside of constants will not trigger a rebuild
 from openpilot.selfdrive.modeld.constants import index_function
 from openpilot.selfdrive.controls.radard import _LEAD_ACCEL_TAU
+from openpilot.sunnypilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import LongitudinalMpcSP

 if __name__ == '__main__':  # generating code
  from acados.acados_template import AcadosModel, AcadosOcp, AcadosOcpSolver
@@ -213,8 +214,9 @@ def gen_long_ocp():
  return ocp


-class LongitudinalMpc:
+class LongitudinalMpc(LongitudinalMpcSP):
  def __init__(self, dt=DT_MDL):
+    LongitudinalMpcSP.__init__(self)
    self.dt = dt
    self.solver = AcadosOcpSolverCython(MODEL_NAME, ACADOS_SOLVER_TYPE, N)
    self.reset()
@@ -266,7 +268,8 @@ class LongitudinalMpc:
  def set_weights(self, prev_accel_constraint=True, personality=log.LongitudinalPersonality.standard):
    jerk_factor = get_jerk_factor(personality)
    a_change_cost = A_CHANGE_COST if prev_accel_constraint else 0
-    cost_weights = [X_EGO_OBSTACLE_COST, X_EGO_COST, V_EGO_COST, A_EGO_COST, jerk_factor * a_change_cost, jerk_factor * J_EGO_COST]
+    cost_weights = [X_EGO_OBSTACLE_COST, X_EGO_COST, V_EGO_COST, A_EGO_COST, jerk_factor * a_change_cost,
+                    LongitudinalMpcSP.scale_jerk_cost(self, jerk_factor * J_EGO_COST)]
    constraint_cost_weights = [LIMIT_COST, LIMIT_COST, LIMIT_COST, DANGER_ZONE_COST]
    self.set_cost_weights(cost_weights, constraint_cost_weights)

@@ -326,7 +329,7 @@ class LongitudinalMpc:
    # when the leads are no factor.
    v_lower = v_ego + (T_IDXS * CRUISE_MIN_ACCEL * 1.05)
    # TODO does this make sense when max_a is negative?
-    v_upper = v_ego + (T_IDXS * CRUISE_MAX_ACCEL * 1.05)
+    v_upper = v_ego + (T_IDXS * self.cruise_accel_max(CRUISE_MAX_ACCEL) * 1.05)
    v_cruise_clipped = np.clip(v_cruise * np.ones(N+1), v_lower, v_upper)
    cruise_obstacle = np.cumsum(T_DIFFS * v_cruise_clipped) + get_safe_obstacle_distance(v_cruise_clipped, t_follow)

@@ -340,6 +343,7 @@ class LongitudinalMpc:

    self.params[:,0] = ACCEL_MIN
    self.params[:,1] = ACCEL_MAX
+    LongitudinalMpcSP.apply_accel_limits(self)
    self.params[:,2] = np.min(x_obstacles, axis=1)
    self.params[:,3] = np.copy(self.a_prev)
    self.params[:,4] = t_follow
@@ -359,6 +363,7 @@ class LongitudinalMpc:
    self.solver.constraints_set(0, "ubx", self.x0)

    self.solution_status = self.solver.solve()
+    LongitudinalMpcSP.save_solution_status(self)
    self.solve_time = float(self.solver.get_stats('time_tot')[0])

    for i in range(N+1):
@@ -51,7 +51,7 @@ class LongitudinalPlanner(LongitudinalPlannerSP):
  def __init__(self, CP, CP_SP, init_v=0.0, init_a=0.0, dt=DT_MDL):
    self.CP = CP
    self.mpc = LongitudinalMpc(dt=dt)
-    LongitudinalPlannerSP.__init__(self, self.CP, CP_SP, self.mpc)
+    LongitudinalPlannerSP.__init__(self, self.CP, CP_SP, self.mpc, dt=dt)
    self.fcw = False
    self.dt = dt
    self.allow_throttle = True
@@ -110,13 +110,13 @@ class LongitudinalPlanner(LongitudinalPlannerSP):
      clipped_accel_coast = max(accel_coast, accel_clip[0])
      clipped_accel_coast_interp = np.interp(v_ego, [MIN_ALLOW_THROTTLE_SPEED, MIN_ALLOW_THROTTLE_SPEED*2], [accel_clip[1], clipped_accel_coast])
      accel_clip[1] = min(accel_clip[1], clipped_accel_coast_interp)
-
    # Get new v_cruise and a_desired from Smart Cruise Control and Speed Limit Assist
    v_cruise, self.a_desired = LongitudinalPlannerSP.update_targets(self, sm, self.v_desired_filter.x, self.a_desired, v_cruise)
-
    if force_slow_decel:
      v_cruise = 0.0

+    is_e2e, v_cruise = LongitudinalPlannerSP.update_accel_controller(self, sm, v_cruise, prev_accel_constraint, accel_clip[1], reset_state)
+
    self.mpc.set_weights(prev_accel_constraint, personality=sm['selfdriveState'].personality)
    self.mpc.set_cur_state(self.v_desired_filter.x, self.a_desired)
    self.mpc.update(sm['radarState'], v_cruise, personality=sm['selfdriveState'].personality)
@@ -135,13 +135,13 @@ class LongitudinalPlanner(LongitudinalPlannerSP):
    self.a_desired = float(np.interp(self.dt, CONTROL_N_T_IDX, self.a_desired_trajectory))
    self.v_desired_filter.x = self.v_desired_filter.x + self.dt * (self.a_desired + a_prev) / 2.0

-    action_t =  self.CP.longitudinalActuatorDelay + DT_MDL
+    action_t = self.CP.longitudinalActuatorDelay + DT_MDL
    output_a_target_mpc, output_should_stop_mpc = get_accel_from_plan(self.v_desired_trajectory, self.a_desired_trajectory, CONTROL_N_T_IDX,
                                                                        action_t=action_t)
    output_a_target_e2e = sm['modelV2'].action.desiredAcceleration
    output_should_stop_e2e = sm['modelV2'].action.shouldStop

-    if self.is_e2e(sm):
+    if is_e2e:
      output_a_target = min(output_a_target_e2e, output_a_target_mpc)
      self.output_should_stop = output_should_stop_e2e or output_should_stop_mpc
      if output_a_target < output_a_target_mpc:
@@ -149,6 +149,7 @@ class LongitudinalPlanner(LongitudinalPlannerSP):
    else:
      output_a_target = output_a_target_mpc
      self.output_should_stop = output_should_stop_mpc
+    self.output_should_stop = LongitudinalPlannerSP.update_should_stop(self, self.output_should_stop)

    for idx in range(2):
      accel_clip[idx] = np.clip(accel_clip[idx], self.prev_accel_clip[idx] - 0.05, self.prev_accel_clip[idx] + 0.05)
@@ -158,7 +159,7 @@ class LongitudinalPlanner(LongitudinalPlannerSP):
  def publish(self, sm, pm):
    plan_send = messaging.new_message('longitudinalPlan')

-    plan_send.valid = sm.all_checks(service_list=['carState', 'controlsState', 'selfdriveState', 'radarState'])
+    plan_send.valid = sm.all_checks()

    longitudinalPlan = plan_send.longitudinalPlan
    longitudinalPlan.modelMonoTime = sm.logMonoTime['modelV2']
@@ -29,19 +29,19 @@ def main():
  longitudinal_planner = LongitudinalPlanner(CP, CP_SP)
  pm = messaging.PubMaster(['longitudinalPlan', 'driverAssistance', 'longitudinalPlanSP'])
  sm = messaging.SubMaster(['carControl', 'carState', 'controlsState', 'liveParameters', 'radarState', 'modelV2', 'selfdriveState',
-                            'liveMapDataSP', 'carStateSP', gps_location_service],
-                           poll='carState', ignore_alive=ignore_services, ignore_avg_freq=ignore_services, ignore_valid=ignore_services)
+                            'liveMapDataSP', 'carStateSP', 'selfdriveStateSP', gps_location_service],
+                           poll='modelV2', ignore_alive=ignore_services, ignore_avg_freq=ignore_services, ignore_valid=ignore_services)

  while True:
    sm.update()
-    longitudinal_planner.sla.update_car_state(sm['carState'])
+    longitudinal_planner.sla.update_buttons(sm['selfdriveStateSP'].buttonsReleaseToggle)
    if sm.updated['modelV2']:
      longitudinal_planner.update(sm)
      longitudinal_planner.publish(sm, pm)

      ldw.update(sm.frame, sm['modelV2'], sm['carState'], sm['carControl'])
      msg = messaging.new_message('driverAssistance')
-      msg.valid = sm.all_checks(['carState', 'carControl', 'modelV2', 'liveParameters'])
+      msg.valid = sm.all_checks()
      msg.driverAssistance.leftLaneDeparture = ldw.left
      msg.driverAssistance.rightLaneDeparture = ldw.right
      pm.send('driverAssistance', msg)
@@ -30,6 +30,7 @@ from openpilot.sunnypilot import get_sanitize_int_param
 from openpilot.sunnypilot.selfdrive.car.car_specific import CarSpecificEventsSP
 from openpilot.sunnypilot.selfdrive.car.cruise_helpers import CruiseHelper
 from openpilot.sunnypilot.selfdrive.car.intelligent_cruise_button_management.controller import IntelligentCruiseButtonManagement
+from openpilot.sunnypilot.selfdrive.selfdrived.button_state_tracker import ButtonStateTracker
 from openpilot.sunnypilot.selfdrive.selfdrived.events import EventsSP

 REPLAY = "REPLAY" in os.environ
@@ -177,6 +178,7 @@ class SelfdriveD(CruiseHelper):
    self.car_events_sp = CarSpecificEventsSP(self.CP, self.CP_SP)

    CruiseHelper.__init__(self, self.CP)
+    self.button_state_tracker = ButtonStateTracker()

  def update_events(self, CS):
    """Compute onroadEvents from carState"""
@@ -325,9 +327,16 @@ class SelfdriveD(CruiseHelper):
    # Handle lane change
    if self.sm['modelV2'].meta.laneChangeState == LaneChangeState.preLaneChange:
      direction = self.sm['modelV2'].meta.laneChangeDirection
+      mdv2sp = self.sm['modelDataV2SP']
+
      if (CS.leftBlindspot and direction == LaneChangeDirection.left) or \
-         (CS.rightBlindspot and direction == LaneChangeDirection.right):
+            (CS.rightBlindspot and direction == LaneChangeDirection.right):
        self.events.add(EventName.laneChangeBlocked)
+
+      elif (mdv2sp.leftLaneChangeEdgeBlock and direction == LaneChangeDirection.left) or \
+           (mdv2sp.rightLaneChangeEdgeBlock and direction == LaneChangeDirection.right):
+        self.events_sp.add(custom.OnroadEventSP.EventName.laneChangeRoadEdge)
+
      else:
        if direction == LaneChangeDirection.left:
          self.events.add(EventName.preLaneChangeLeft)
@@ -597,6 +606,8 @@ class SelfdriveD(CruiseHelper):
    icbm.sendButton = self.icbm.cruise_button
    icbm.vTarget = self.icbm.v_target

+    self.button_state_tracker.publish(ss_sp)
+
    self.pm.send('selfdriveStateSP', ss_sp_msg)

    # onroadEventsSP - logged every second or on change
@@ -616,6 +627,7 @@ class SelfdriveD(CruiseHelper):
      self.mads.update(CS)
    self.update_alerts(CS)

+    self.button_state_tracker.update(CS)
    self.publish_selfdriveState(CS)

    self.CS_prev = CS
@@ -11,6 +11,14 @@ from openpilot.selfdrive.controls.lib.longitudinal_planner import LongitudinalPl
 from openpilot.selfdrive.controls.radard import _LEAD_ACCEL_TAU


+class PlannerSM(dict):
+  def __init__(self, radar_frame: int, services: dict):
+    super().__init__(services)
+    self.logMonoTime = {"radarState": radar_frame}
+    self.valid = {"radarState": True}
+    self.alive = {"radarState": True}
+
+
 class Plant:
  messaging_initialized = False

@@ -132,7 +140,7 @@ class Plant:
    car_control.carControl.orientationNED = [0., float(pitch), 0.]

    # ******** get controlsState messages for plotting ***
-    sm = {'radarState': radar.radarState,
+    sm = PlannerSM(self.rk.frame, {'radarState': radar.radarState,
          'carState': car_state.carState,
          'carControl': car_control.carControl,
          'controlsState': control.controlsState,
@@ -141,7 +149,7 @@ class Plant:
          'modelV2': model.modelV2,
          'carStateSP': car_state_sp.carStateSP,
          'liveMapDataSP': live_map_data_sp.liveMapDataSP,
-          'gpsLocation': gps_data.gpsLocation}
+          'gpsLocation': gps_data.gpsLocation})
    self.planner.update(sm)
    self.acceleration = self.planner.output_a_target
    if self.planner.output_should_stop:
@@ -27,6 +27,12 @@ DESCRIPTIONS = {
    "In relaxed mode sunnypilot will stay further away from lead cars. On supported cars, you can cycle through these personalities with " +
    "your steering wheel distance button."
  ),
+  "AccelPersonalityEnabled": tr_noop(
+    "Begin slowing early and smoothly behind lead vehicles. Stock longitudinal control retains braking and stopping authority."
+  ),
+  "AccelPersonality": tr_noop(
+    "Eco slows earliest and recovers gently, Normal balances comfort and response, and Sport reacts and recovers more quickly."
+  ),
  "IsLdwEnabled": tr_noop(
    "Receive alerts to steer back into the lane when your vehicle drifts over a detected lane line " +
    "without a turn signal activated while driving over 31 mph (50 km/h)."
@@ -106,6 +112,24 @@ class TogglesLayout(Widget):
      icon="speed_limit.png"
    )

+    self._accel_personality_enabled = toggle_item(
+      lambda: tr("Enable Accel Controller"),
+      lambda: tr(DESCRIPTIONS["AccelPersonalityEnabled"]),
+      self._params.get_bool("AccelPersonalityEnabled"),
+      callback=self._set_accel_personality_enabled,
+      icon="speed_limit.png",
+    )
+
+    self._accel_personality_setting = multiple_button_item(
+      lambda: tr("Acceleration Profile"),
+      lambda: tr(DESCRIPTIONS["AccelPersonality"]),
+      buttons=[lambda: tr("Eco"), lambda: tr("Normal"), lambda: tr("Sport")],
+      button_width=300,
+      callback=self._set_accel_personality,
+      selected_index=self._params.get("AccelPersonality", return_default=True),
+      icon="speed_limit.png"
+    )
+
    self._toggles = {}
    self._locked_toggles = set()
    for param, (title, desc, icon, needs_restart) in self._toggle_defs.items():
@@ -135,9 +159,11 @@ class TogglesLayout(Widget):

      self._toggles[param] = toggle

-      # insert longitudinal personality after NDOG toggle
+      # insert longitudinal personality and Accel Controller settings after NDOG toggle
      if param == "DisengageOnAccelerator":
        self._toggles["LongitudinalPersonality"] = self._long_personality_setting
+        self._toggles["AccelPersonalityEnabled"] = self._accel_personality_enabled
+        self._toggles["AccelPersonality"] = self._accel_personality_setting

    self._update_experimental_mode_icon()
    self._scroller = Scroller(list(self._toggles.values()), line_separator=True, spacing=0)
@@ -158,6 +184,7 @@ class TogglesLayout(Widget):

  def _update_toggles(self):
    ui_state.update_params()
+    accel_personality_enabled = self._params.get_bool("AccelPersonalityEnabled")

    e2e_description = tr(
      "sunnypilot defaults to driving in chill mode. Experimental mode enables alpha-level features that aren't ready for chill mode. " +
@@ -176,11 +203,15 @@ class TogglesLayout(Widget):
        self._toggles["ExperimentalMode"].action_item.set_enabled(True)
        self._toggles["ExperimentalMode"].set_description(e2e_description)
        self._long_personality_setting.action_item.set_enabled(True)
+        self._accel_personality_enabled.action_item.set_enabled(True)
+        self._accel_personality_setting.action_item.set_enabled(accel_personality_enabled)
      else:
        # no long for now
        self._toggles["ExperimentalMode"].action_item.set_enabled(False)
        self._toggles["ExperimentalMode"].action_item.set_state(False)
        self._long_personality_setting.action_item.set_enabled(False)
+        self._accel_personality_enabled.action_item.set_enabled(False)
+        self._accel_personality_setting.action_item.set_enabled(False)
        self._params.remove("ExperimentalMode")

        unavailable = tr("Experimental mode is currently unavailable on this car since the car's stock ACC is used for longitudinal control.")
@@ -203,6 +234,10 @@ class TogglesLayout(Widget):
    # refresh toggles from params to mirror external changes
    for param in self._toggle_defs:
      self._toggles[param].action_item.set_state(self._params.get_bool(param))
+    self._accel_personality_enabled.action_item.set_state(accel_personality_enabled)
+    self._accel_personality_setting.action_item.set_selected_button(
+      self._params.get("AccelPersonality", return_default=True)
+    )

    # these toggles need restart, block while engaged
    for toggle_def in self._toggle_defs:
@@ -247,3 +282,10 @@ class TogglesLayout(Widget):

  def _set_longitudinal_personality(self, button_index: int):
    self._params.put("LongitudinalPersonality", button_index, block=True)
+
+  def _set_accel_personality(self, button_index: int):
+    self._params.put("AccelPersonality", button_index, block=True)
+
+  def _set_accel_personality_enabled(self, state: bool):
+    self._params.put_bool("AccelPersonalityEnabled", state, block=True)
+    self._accel_personality_setting.action_item.set_enabled(state and ui_state.has_longitudinal_control)
@@ -13,6 +13,7 @@ from openpilot.system.ui.lib.application import gui_app

 if gui_app.sunnypilot_ui():
  from openpilot.selfdrive.ui.sunnypilot.mici.layouts.settings import SettingsLayoutSP as SettingsLayout
+  from openpilot.selfdrive.ui.sunnypilot.mici.layouts.onroad import OnroadViewContainerSP as AugmentedRoadView

 ONROAD_DELAY = 2.5  # seconds

@@ -118,13 +119,15 @@ class MiciMainLayout(Scroller):

    # FIXME: these two pops can interrupt user interacting in the settings
    if self._onroad_time_delay is not None and rl.get_time() - self._onroad_time_delay >= ONROAD_DELAY:
-      gui_app.pop_widgets_to(self, lambda: self._scroll_to(self._onroad_layout))
+      if not gui_app.sunnypilot_ui() or self._should_auto_scroll_to_onroad():
+        gui_app.pop_widgets_to(self, lambda: self._scroll_to(self._onroad_layout))
      self._onroad_time_delay = None

    # When car leaves standstill, pop nav stack and scroll to onroad
    CS = ui_state.sm["carState"]
    if not CS.standstill and self._prev_standstill:
-      gui_app.pop_widgets_to(self, lambda: self._scroll_to(self._onroad_layout))
+      if not gui_app.sunnypilot_ui() or self._should_auto_scroll_to_onroad():
+        gui_app.pop_widgets_to(self, lambda: self._scroll_to(self._onroad_layout))
    self._prev_standstill = CS.standstill

  def _on_interactive_timeout(self):
@@ -14,6 +14,8 @@ class TogglesLayoutMici(NavScroller):
    super().__init__()

    self._personality_toggle = BigMultiParamToggle("driving personality", "LongitudinalPersonality", ["aggressive", "standard", "relaxed"])
+    self._accel_personality_enabled = BigParamControl("enable accel controller", "AccelPersonalityEnabled")
+    self._accel_personality_toggle = BigMultiParamToggle("acceleration profile", "AccelPersonality", ["eco", "normal", "sport"])
    self._experimental_btn = BigParamControl("experimental mode", "ExperimentalMode")
    is_metric_toggle = BigParamControl("use metric units", "IsMetric")
    ldw_toggle = BigParamControl("lane departure warnings", "IsLdwEnabled")
@@ -24,6 +26,8 @@ class TogglesLayoutMici(NavScroller):

    self._scroller.add_widgets([
      self._personality_toggle,
+      self._accel_personality_enabled,
+      self._accel_personality_toggle,
      self._experimental_btn,
      is_metric_toggle,
      ldw_toggle,
@@ -36,6 +40,7 @@ class TogglesLayoutMici(NavScroller):
    # Toggle lists
    self._refresh_toggles = (
      ("ExperimentalMode", self._experimental_btn),
+      ("AccelPersonalityEnabled", self._accel_personality_enabled),
      ("IsMetric", is_metric_toggle),
      ("IsLdwEnabled", ldw_toggle),
      ("AlwaysOnDM", always_on_dm_toggle),
@@ -45,6 +50,9 @@ class TogglesLayoutMici(NavScroller):
    )

    enable_openpilot.set_enabled(lambda: not ui_state.engaged)
+    self._accel_personality_toggle.set_enabled(
+      lambda: ui_state.has_longitudinal_control and ui_state.params.get_bool("AccelPersonalityEnabled")
+    )
    record_front.set_enabled(False if ui_state.params.get_bool("RecordFrontLock") else (lambda: not ui_state.engaged))
    record_mic.set_enabled(lambda: not ui_state.engaged)

@@ -75,13 +83,18 @@ class TogglesLayoutMici(NavScroller):
      if ui_state.has_longitudinal_control:
        self._experimental_btn.set_visible(True)
        self._personality_toggle.set_visible(True)
+        self._accel_personality_enabled.set_visible(True)
+        self._accel_personality_toggle.set_visible(True)
      else:
        # no long for now
        self._experimental_btn.set_visible(False)
        self._experimental_btn.set_checked(False)
        self._personality_toggle.set_visible(False)
+        self._accel_personality_enabled.set_visible(False)
+        self._accel_personality_toggle.set_visible(False)
        ui_state.params.remove("ExperimentalMode")

    # Refresh toggles from params to mirror external changes
    for key, item in self._refresh_toggles:
      item.set_checked(ui_state.params.get_bool(key))
+    self._accel_personality_toggle.refresh()
@@ -383,13 +383,18 @@ class BigMultiParamToggle(BigMultiToggle):
    self._load_value()

  def _load_value(self):
-    self.set_value(self._options[self._params.get(self._param) or 0])
+    value = self._params.get(self._param, return_default=True)
+    index = value if isinstance(value, int) else 0
+    self.set_value(self._options[max(0, min(index, len(self._options) - 1))])

  def _handle_mouse_release(self, mouse_pos: MousePos):
    super()._handle_mouse_release(mouse_pos)
    new_idx = self._options.index(self.value)
    self._params.put(self._param, new_idx)

+  def refresh(self):
+    self._load_value()
+

 class BigParamControl(BigToggle):
  def __init__(self, text: str, param: str, toggle_callback: Callable | None = None):
@@ -43,7 +43,7 @@ class ModelsLayout(Widget):
    self._initialize_items()

    self.clear_cache_item.action_item.set_value(f"{self.calculate_cache_size():.2f} MB")
-    for ctrl, key in [(self.lane_turn_value_control, "LaneTurnValue"), (self.delay_control, "LagdToggleDelay")]:
+    for ctrl, key in [(self.lane_turn_value_control, "LaneTurnValue"), (self.delay_control, "LagdToggleDelay"), (self.camera_offset, "CameraOffset")]:
      ctrl.action_item.set_value(int(float(ui_state.params.get(key, return_default=True)) * 100))

    self._scroller = Scroller(self.items, line_separator=True, spacing=0)
@@ -93,9 +93,14 @@ class ModelsLayout(Widget):

    self.lagd_toggle = toggle_item_sp(tr("Live Learning Steer Delay"), "", param="LagdToggle")

+    self.camera_offset = option_item_sp(tr("Adjust Camera Offset"), "CameraOffset", -35, 35,
+                                        tr("Virtually shift camera's perspective to move model's center to Left(+ values) or Right (- values)"),
+                                        1, None, True, "", style.BUTTON_ACTION_WIDTH, None, True,
+                                        lambda v: f"{v / 100:.2f} m")
+
    self.items = [self.current_model_item, self.cancel_download_item, self.supercombo_label, self.vision_label,
-                  self.policy_label, self.off_policy_label, self.on_policy_label, self.refresh_item, self.clear_cache_item, self.lane_turn_desire_toggle,
-                  self.lane_turn_value_control, self.lagd_toggle, self.delay_control]
+                  self.policy_label, self.off_policy_label, self.on_policy_label, self.refresh_item, self.clear_cache_item,
+                  self.lane_turn_desire_toggle, self.lane_turn_value_control, self.lagd_toggle, self.delay_control, self.camera_offset]

  def _update_lagd_description(self, lagd_toggle: bool):
    desc = tr("Enable this for the car to learn and adapt its steering response time. Disable to use a fixed steering response time. " +
@@ -232,6 +237,7 @@ class ModelsLayout(Widget):
    advanced_controls: bool = ui_state.params.get_bool("ShowAdvancedControls")
    turn_desire: bool = ui_state.params.get_bool("LaneTurnDesire")
    live_delay: bool = ui_state.params.get_bool("LagdToggle")
+    camera_offset: bool = ui_state.params.get("ModelManager_ActiveBundle") is not None

    self.lane_turn_desire_toggle.action_item.set_state(turn_desire)
    self.lane_turn_value_control.set_visible(turn_desire and advanced_controls)
@@ -240,6 +246,7 @@ class ModelsLayout(Widget):
    new_step = int(round(100 / CV.MPH_TO_KPH)) if ui_state.is_metric else 100
    if self.lane_turn_value_control.action_item is not None and self.lane_turn_value_control.action_item.value_change_step != new_step:
      self.lane_turn_value_control.action_item.value_change_step = new_step
+    self.camera_offset.set_visible(camera_offset)

    self._update_lagd_description(live_delay)
    self.model_manager = ui_state.sm["modelManagerSP"]
@@ -51,11 +51,17 @@ class LaneChangeSettingsLayout(Widget):
      description=lambda: tr("Toggle to enable a delay timer for seamless lane changes when blind spot monitoring " +
                             "(BSM) detects a obstructing vehicle, ensuring safe maneuvering."),
    )
+    self._road_edge_block = toggle_item_sp(
+      param="RoadEdgeLaneChangeEnabled",
+      title=lambda: tr("Block Lane Change: Road Edge Detection"),
+      description=lambda: tr("Blocks the lane change if the model sees a road edge on your signaled side."),
+    )

    items = [
      self._lane_change_timer,
      LineSeparatorSP(40),
      self._bsm_delay,
+      self._road_edge_block,
    ]

    return items
@@ -7,7 +7,7 @@ See the LICENSE.md file in the root directory for more details.
 from collections.abc import Callable
 import pyray as rl

-from opendbc.sunnypilot.car.tesla.values import TeslaFlagsSP
+from opendbc.sunnypilot.car.tesla.values import MadsScreenButtonType, TeslaFlagsSP
 from openpilot.selfdrive.ui.ui_state import ui_state
 from openpilot.sunnypilot.mads.helpers import MadsSteeringModeOnBrake
 from openpilot.system.ui.lib.multilang import tr, tr_noop
@@ -96,7 +96,10 @@ class MadsSettingsLayout(Widget):
    if brand == "rivian":
      return True
    elif brand == "tesla":
-      return not (ui_state.CP_SP is not None and ui_state.CP_SP.flags & TeslaFlagsSP.HAS_VEHICLE_BUS)
+      if ui_state.CP_SP is None or not ui_state.CP_SP.flags & TeslaFlagsSP.HAS_VEHICLE_BUS:
+        return True
+      screen_button = int(ui_state.params.get("TeslaMadsScreenButton", return_default=True))
+      return screen_button == MadsScreenButtonType.OFF
    return False

  def _update_steering_mode_description(self, button_index: int):
@@ -4,10 +4,11 @@ Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
 This file is part of sunnypilot and is licensed under the MIT License.
 See the LICENSE.md file in the root directory for more details.
 """
+from opendbc.sunnypilot.car.tesla.values import TeslaFlagsSP
 from openpilot.selfdrive.ui.sunnypilot.layouts.settings.vehicle.brands.base import BrandSettings
 from openpilot.selfdrive.ui.ui_state import ui_state
 from openpilot.system.ui.lib.multilang import tr
-from openpilot.system.ui.sunnypilot.widgets.list_view import toggle_item_sp
+from openpilot.system.ui.sunnypilot.widgets.list_view import multiple_button_item_sp, toggle_item_sp

 COOP_STEERING_MIN_KMH = 23
 OEM_STEERING_MIN_KMH = 48
@@ -18,7 +19,14 @@ class TeslaSettings(BrandSettings):
  def __init__(self):
    super().__init__()
    self.coop_steering_toggle = toggle_item_sp(tr("Cooperative Steering (Beta)"), "", param="TeslaCoopSteering")
-    self.items = [self.coop_steering_toggle]
+    self.mads_screen_button = multiple_button_item_sp(
+      title=lambda: tr("MADS Screen Activation"),
+      description="",
+      buttons=[lambda: tr("Off"), lambda: tr("3-Finger"), lambda: tr("4-Finger"), lambda: tr("5-Finger")],
+      param="TeslaMadsScreenButton",
+      inline=False,
+    )
+    self.items = [self.coop_steering_toggle, self.mads_screen_button]

  def update_settings(self):
    is_metric = ui_state.is_metric
@@ -41,3 +49,18 @@ class TeslaSettings(BrandSettings):

    self.coop_steering_toggle.set_description(coop_steering_desc)
    self.coop_steering_toggle.action_item.set_enabled(ui_state.is_offroad())
+
+    has_vehicle_bus = ui_state.CP_SP is not None and bool(ui_state.CP_SP.flags & TeslaFlagsSP.HAS_VEHICLE_BUS)
+    self.mads_screen_button.set_visible(has_vehicle_bus)
+
+    mads_screen_button_desc = (
+      f"{tr('Use a multi-finger press on the infotainment screen to toggle MADS.')} " +
+      f"{tr('This allows the use of full MADS functionality when enabled.')}<br><br>" +
+      f"{tr('Selecting a higher finger count may reduce accidental activations.')}<br><br>" +
+      f"<b>{tr('Note: Setting this to Off will reset your MADS settings to default.')}</b>"
+    )
+    if not ui_state.is_offroad():
+      mads_screen_button_disabled_msg = tr("Enable \"Always Offroad\" in Device panel, or turn vehicle off to change.")
+      mads_screen_button_desc = f"<b>{mads_screen_button_disabled_msg}</b><br><br>{mads_screen_button_desc}"
+    self.mads_screen_button.set_description(mads_screen_button_desc)
+    self.mads_screen_button.action_item.set_enabled(ui_state.is_offroad())
@@ -0,0 +1,13 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+
+from openpilot.selfdrive.ui.mici.layouts.main import MiciMainLayout
+
+
+class MiciMainLayoutSP(MiciMainLayout):
+  def _should_auto_scroll_to_onroad(self) -> bool:
+    return not self._onroad_layout.is_on_info_panel()
@@ -0,0 +1,63 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+import pyray as rl
+from openpilot.system.ui.lib.application import gui_app
+from openpilot.selfdrive.ui.sunnypilot.mici.widgets.scroller_sp import ScrollerSP
+from openpilot.selfdrive.ui.sunnypilot.mici.onroad.augmented_road_view import AugmentedRoadViewSP
+from openpilot.selfdrive.ui.sunnypilot.mici.layouts.onroad_info_panel import OnroadInfoPanel
+
+CONFIDENCE_BALL_VISIBLE_RATIO = 0.4
+HORIZONTAL_SETTLE_PX = 5
+HORIZONTAL_RESET_RATIO = 0.5
+
+
+class OnroadViewContainerSP(ScrollerSP):
+  def __init__(self, bookmark_callback=None):
+    super().__init__(horizontal=False, snap_items=True, spacing=0, pad=0, scroll_indicator=False, edge_shadows=False)
+    self.road_view = AugmentedRoadViewSP(bookmark_callback=bookmark_callback)
+    self.onroad_info_panel = OnroadInfoPanel(bookmark_callback=bookmark_callback)
+
+    self._scroller.add_widgets([
+      self.road_view,
+      self.onroad_info_panel,
+    ])
+    self._scroller.set_reset_scroll_at_show(False)
+    self._scroller.set_scrolling_enabled(lambda: abs(self.rect.x) < HORIZONTAL_SETTLE_PX)
+
+    for child in (self.road_view, self.onroad_info_panel):
+      inner_touch_valid = child._touch_valid_callback
+      child.set_touch_valid_callback(
+        lambda inner=inner_touch_valid: self._touch_valid() and (inner() if inner else True)
+      )
+
+  def set_rect(self, rect: rl.Rectangle):
+    super().set_rect(rect)
+    self.road_view.set_rect(rect)
+    self.onroad_info_panel.set_rect(rect)
+    return self
+
+  def is_swiping_left(self) -> bool:
+    return self.road_view.is_swiping_left() or self.onroad_info_panel.is_swiping_left()
+
+  def set_click_callback(self, callback) -> None:
+    self.road_view.set_click_callback(callback)
+    self.onroad_info_panel.set_click_callback(callback)
+
+  def is_on_info_panel(self) -> bool:
+    """True when scrolled past halfway toward onroad_info_panel (used by main layout
+    to skip auto-pop-back-to-camera while user is reading the info panel)."""
+    return abs(self._scroller.scroll_panel.get_offset()) > self._rect.height / 2
+
+  def _render(self, rect: rl.Rectangle):
+    if abs(self.rect.x) > gui_app.width * HORIZONTAL_RESET_RATIO:
+      self._scroller.scroll_panel.set_offset(0)
+
+    vertical_offset = self._scroller.scroll_panel.get_offset()
+    show_ball = abs(vertical_offset) < rect.height * CONFIDENCE_BALL_VISIBLE_RATIO
+    self.road_view.set_show_confidence_ball(show_ball)
+
+    super()._render(rect)
@@ -0,0 +1,324 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+
+import pyray as rl
+from dataclasses import dataclass
+from openpilot.common.constants import CV
+from openpilot.common.filter_simple import FirstOrderFilter
+from openpilot.selfdrive.ui.ui_state import ui_state
+from openpilot.system.ui.lib.application import gui_app, FontWeight
+from openpilot.system.ui.lib.multilang import tr
+from openpilot.system.ui.lib.text_measure import measure_text_cached
+from openpilot.system.ui.lib.application import MousePos
+from openpilot.system.ui.widgets import Widget
+from openpilot.selfdrive.ui.mici.onroad.alert_renderer import AlertRenderer
+from openpilot.selfdrive.ui.mici.onroad.augmented_road_view import BookmarkIcon
+
+METER_TO_KM = 0.001
+METER_TO_MILE = 0.000621371
+
+
+@dataclass(frozen=True)
+class OnroadInfoPanelColors:
+  white: rl.Color = rl.WHITE
+  black: rl.Color = rl.BLACK
+  red: rl.Color = rl.Color(255, 0, 0, 255)
+  green: rl.Color = rl.Color(0, 255, 0, 255)
+  grey: rl.Color = rl.Color(190, 195, 190, 255)
+  light_grey: rl.Color = rl.Color(200, 200, 200, 255)
+  dark_grey: rl.Color = rl.Color(100, 100, 100, 255)
+  bg_dark: rl.Color = rl.Color(0, 0, 0, 255)
+  card_bg: rl.Color = rl.Color(50, 50, 50, 200)
+  badge_bg: rl.Color = rl.Color(60, 60, 60, 255)
+
+
+COLORS = OnroadInfoPanelColors()
+
+
+class OnroadInfoPanel(Widget):
+  def __init__(self, bookmark_callback=None):
+    super().__init__()
+    self.speed_limit: float = 0.0
+    self.speed_limit_valid: bool = False
+    self.speed_limit_offset: float = 0.0
+    self.next_speed_limit: float = 0.0
+    self.next_speed_limit_distance: float = 0.0
+    self.road_name: str = ""
+    self.current_speed: float = 0.0
+    self.set_speed: float = 0.0
+    self.cruise_enabled: bool = False
+
+    self._sign_slide: float = 0.0
+
+    self._font_bold: rl.Font = gui_app.font(FontWeight.BOLD)
+    self._font_semi_bold: rl.Font = gui_app.font(FontWeight.SEMI_BOLD)
+    self._font_medium: rl.Font = gui_app.font(FontWeight.MEDIUM)
+
+    self._marquee_offset: float = 0.0
+    self._marquee_direction: int = 1
+    self._marquee_pause_timer: float = 0.0
+    self._marquee_speed: float = 40.0
+    self._marquee_pause_duration: float = 1.5
+
+    self._alert_renderer = AlertRenderer()
+    self._alert_alpha_filter = FirstOrderFilter(0, 0.05, 1 / gui_app.target_fps)
+
+    self._bookmark_icon = BookmarkIcon(bookmark_callback)
+
+  def is_swiping_left(self) -> bool:
+    return self._bookmark_icon.is_swiping_left()
+
+  def _handle_mouse_release(self, mouse_pos: MousePos) -> None:
+    # Mirror stock AugmentedRoadView: suppress click while bookmark gesture active
+    if not self._bookmark_icon.interacting():
+      super()._handle_mouse_release(mouse_pos)
+
+  def _update_state(self) -> None:
+    sm = ui_state.sm
+    speed_conv = CV.MS_TO_KPH if ui_state.is_metric else CV.MS_TO_MPH
+
+    if sm.valid["longitudinalPlanSP"]:
+      lp_sp = sm["longitudinalPlanSP"]
+      resolver = lp_sp.speedLimit.resolver
+      self.speed_limit = resolver.speedLimit * speed_conv
+      self.speed_limit_valid = resolver.speedLimitValid
+      self.speed_limit_offset = resolver.speedLimitOffset * speed_conv
+
+    if sm.valid["liveMapDataSP"]:
+      lmd = sm["liveMapDataSP"]
+      self.next_speed_limit = lmd.speedLimitAhead * speed_conv
+      self.next_speed_limit_distance = lmd.speedLimitAheadDistance
+      self.road_name = lmd.roadName
+
+    if sm.updated["carState"]:
+      self.current_speed = sm["carState"].vEgo * speed_conv
+
+    if sm.valid["carState"] and sm.valid["controlsState"]:
+      self.cruise_enabled = sm["carState"].cruiseState.enabled
+      v_cruise_cluster = sm["carState"].vCruiseCluster
+      set_speed_kph = sm["controlsState"].vCruiseDEPRECATED if v_cruise_cluster == 0.0 else v_cruise_cluster
+      self.set_speed = set_speed_kph * (METER_TO_MILE / METER_TO_KM) if not ui_state.is_metric else set_speed_kph
+
+  def _render(self, rect: rl.Rectangle) -> None:
+    self._update_state()
+
+    rl.draw_rectangle(int(rect.x), int(rect.y), int(rect.width), int(rect.height), COLORS.bg_dark)
+    margin = 20
+    mid_y = rect.y + rect.height / 2
+
+    left_x = rect.x + margin
+
+    if self.cruise_enabled:
+      unit = tr("MAX")
+      display_speed = self.set_speed
+    else:
+      unit = tr("km/h") if ui_state.is_metric else tr("MPH")
+      display_speed = self.current_speed
+
+    speed_val = str(round(display_speed))
+    if self.speed_limit_valid and display_speed > self.speed_limit:
+      speed_color = COLORS.red
+    else:
+      speed_color = COLORS.white
+
+    rl.draw_text_ex(self._font_semi_bold, unit, rl.Vector2(left_x, mid_y - 95), 38, 0, COLORS.grey)
+    rl.draw_text_ex(self._font_bold, speed_val, rl.Vector2(left_x, mid_y - 60), 110, 0, speed_color)
+
+    sign_width = 135
+    sign_height = 135 if ui_state.is_metric else 175
+
+    has_next = self.next_speed_limit > 0 and self.next_speed_limit != self.speed_limit
+    target_slide = 1.0 if has_next else 0.0
+    slide_speed = 3.0 * rl.get_frame_time()
+    if self._sign_slide < target_slide:
+      self._sign_slide = min(self._sign_slide + slide_speed, target_slide)
+    elif self._sign_slide > target_slide:
+      self._sign_slide = max(self._sign_slide - slide_speed, target_slide)
+
+    next_w = int(sign_width * 0.7)
+    next_h = int(sign_height * 0.7)
+    next_peek = int(next_w * 0.85) + 5
+    centered_x = rect.x + rect.width - sign_width - margin
+    shifted_x = rect.x + rect.width - sign_width - margin - next_peek
+    sign_x = centered_x + (shifted_x - centered_x) * self._sign_slide
+    sign_y = rect.y + (rect.height - sign_height) / 2
+
+    road_y = mid_y + 55
+    road_width = sign_x - left_x - margin
+    self._draw_road_name(left_x, road_y, road_width)
+
+    if has_next and self._sign_slide > 0.01:
+      next_val = str(round(self.next_speed_limit))
+      dist_str = self._format_distance(self.next_speed_limit_distance)
+      next_x = sign_x + sign_width - int(next_w * 0.15)
+      next_y = sign_y + (sign_height - next_h) / 2
+
+      next_speed_color = COLORS.black
+      if ui_state.is_metric:
+        self._draw_vienna_sign(next_x, next_y, next_w, next_h, next_val, next_speed_color, is_upcoming=True)
+      else:
+        self._draw_mutcd_sign(next_x, next_y, next_w, next_h, next_val, next_speed_color, is_upcoming=True)
+
+      dist_size = measure_text_cached(self._font_medium, dist_str, 24)
+      rl.draw_text_ex(self._font_medium, dist_str, rl.Vector2(next_x + next_w / 2 - dist_size.x / 2, next_y + next_h + 4), 24, 0, COLORS.grey)
+
+    self._draw_speed_limit_sign(sign_x, sign_y, sign_width, sign_height)
+
+    if self.speed_limit_offset != 0 and self.speed_limit_valid:
+      offset_val = str(abs(round(self.speed_limit_offset)))
+      badge_sz = 42
+      badge_x = sign_x + sign_width - badge_sz * 0.85
+      badge_y = sign_y - badge_sz * 0.25
+
+      if ui_state.is_metric:
+        badge_r = badge_sz / 2
+        badge_cx = badge_x + badge_r
+        badge_cy = badge_y + badge_r
+        rl.draw_circle(int(badge_cx), int(badge_cy), badge_r + 2, COLORS.dark_grey)
+        rl.draw_circle(int(badge_cx), int(badge_cy), badge_r, COLORS.badge_bg)
+        self._draw_text_centered(self._font_bold, offset_val, 24, rl.Vector2(badge_cx, badge_cy), COLORS.white)
+      else:
+        mutcd_badge_x = sign_x + sign_width - badge_sz * 0.65
+        mutcd_badge_y = sign_y - badge_sz * 0.50
+        badge_rect = rl.Rectangle(mutcd_badge_x, mutcd_badge_y, badge_sz, badge_sz)
+        rl.draw_rectangle_rounded(badge_rect, 0.25, 10, COLORS.badge_bg)
+        rl.draw_rectangle_rounded_lines_ex(badge_rect, 0.25, 10, 2, COLORS.dark_grey)
+        self._draw_text_centered(self._font_bold, offset_val, 24, rl.Vector2(mutcd_badge_x + badge_sz / 2, mutcd_badge_y + badge_sz / 2), COLORS.white)
+
+    # SCC
+    speed_size = measure_text_cached(self._font_bold, speed_val, 110)
+    scc_x = left_x + speed_size.x + 30
+    scc_y = mid_y - 50
+    self._draw_scc_icons(scc_x, scc_y)
+
+    self._bookmark_icon.render(rect)
+
+    if ui_state.started:
+      alert_obj, no_alert = self._alert_renderer.will_render()
+      self._alert_alpha_filter.update(0 if no_alert else 1)
+      alpha = self._alert_alpha_filter.x
+      if alpha > 0.01:
+        rl.draw_rectangle(int(rect.x), int(rect.y), int(rect.width), int(rect.height), rl.Color(0, 0, 0, int(150 * alpha)))
+      self._alert_renderer.render(rect)
+
+  def _draw_scc_icons(self, x: float, y: float) -> None:
+    sm = ui_state.sm
+    if not sm.valid["longitudinalPlanSP"]:
+      return
+    scc = sm["longitudinalPlanSP"].smartCruiseControl
+
+    box_w, box_h = 100, 36
+    gap = 6
+    drawn = 0
+
+    for label, active in [("SCC-V", scc.vision.active), ("SCC-M", scc.map.active)]:
+      if not active:
+        continue
+      bx = x
+      by = y + drawn * (box_h + gap)
+      rl.draw_rectangle_rounded(rl.Rectangle(bx, by, box_w, box_h), 0.3, 10, COLORS.green)
+      self._draw_text_centered(self._font_bold, label, 20, rl.Vector2(bx + box_w / 2, by + box_h / 2), COLORS.black)
+      drawn += 1
+
+  def _draw_speed_limit_sign(self, x: float, y: float, sign_width: float, sign_height: float) -> None:
+    speed_str = str(round(self.speed_limit)) if self.speed_limit_valid and self.speed_limit > 0 else "--"
+    speed_color = COLORS.black if not self.speed_limit_valid or self.current_speed <= self.speed_limit else COLORS.red
+
+    if ui_state.is_metric:
+      self._draw_vienna_sign(x, y, sign_width, sign_height, speed_str, speed_color, is_upcoming=False)
+    else:
+      self._draw_mutcd_sign(x, y, sign_width, sign_height, speed_str, speed_color, is_upcoming=False)
+
+  def _draw_road_name(self, x: float, y: float, width: float) -> None:
+    road_display = self.road_name if self.road_name else "--"
+    font_size = 30
+    road_size = measure_text_cached(self._font_semi_bold, road_display, font_size)
+    text_width = road_size.x
+
+    if text_width <= width:
+      self._marquee_offset = 0.0
+      self._marquee_direction = 1
+      self._marquee_pause_timer = 0.0
+      rl.draw_text_ex(self._font_semi_bold, road_display, rl.Vector2(x, y), font_size, 0, COLORS.white)
+    else:
+      overflow = text_width - width
+      dt = rl.get_frame_time()
+
+      if self._marquee_pause_timer > 0:
+        self._marquee_pause_timer -= dt
+      else:
+        self._marquee_offset += self._marquee_direction * self._marquee_speed * dt
+
+        if self._marquee_offset >= overflow:
+          self._marquee_offset = overflow
+          self._marquee_direction = -1
+          self._marquee_pause_timer = self._marquee_pause_duration
+        elif self._marquee_offset <= 0:
+          self._marquee_offset = 0
+          self._marquee_direction = 1
+          self._marquee_pause_timer = self._marquee_pause_duration
+
+      rl.begin_scissor_mode(int(x), int(y), int(width), int(road_size.y + 4))
+      text_pos = rl.Vector2(x - self._marquee_offset, y)
+      rl.draw_text_ex(self._font_semi_bold, road_display, text_pos, font_size, 0, COLORS.white)
+      rl.end_scissor_mode()
+
+  def _draw_vienna_sign(self, x: float, y: float, width: float, height: float, speed_str: str, speed_color: rl.Color, is_upcoming: bool = False) -> None:
+    center = rl.Vector2(x + width / 2, y + height / 2)
+    outer_radius = min(width, height) / 2
+
+    rl.draw_circle_v(center, outer_radius, COLORS.white)
+    ring_width = outer_radius * 0.18
+    rl.draw_ring(center, outer_radius - ring_width, outer_radius, 0, 360, 36, COLORS.red)
+
+    font_size = outer_radius * (0.7 if len(speed_str) >= 3 else 0.9)
+    text_size = measure_text_cached(self._font_bold, speed_str, int(font_size))
+    text_pos = rl.Vector2(center.x - text_size.x / 2, center.y - text_size.y / 2)
+    rl.draw_text_ex(self._font_bold, speed_str, text_pos, font_size, 0, speed_color)
+
+  def _draw_mutcd_sign(self, x: float, y: float, width: float, height: float, speed_str: str, speed_color: rl.Color, is_upcoming: bool = False) -> None:
+    sign_rect = rl.Rectangle(x, y, width, height)
+    rl.draw_rectangle_rounded(sign_rect, 0.35, 10, COLORS.white)
+
+    inset = max(4, width * 0.05)
+    inner_rect = rl.Rectangle(x + inset, y + inset, width - inset * 2, height - inset * 2)
+    outer_radius = 0.35 * width / 2.0
+    inner_radius = outer_radius - inset
+    inner_roundness = inner_radius / (inner_rect.width / 2.0)
+    rl.draw_rectangle_rounded_lines_ex(inner_rect, inner_roundness, 10, 3, COLORS.black)
+
+    mid_x = x + width / 2
+    label_size = max(18, int(width * 0.26))
+    if is_upcoming:
+      self._draw_text_centered(self._font_bold, tr("AHEAD"), label_size, rl.Vector2(mid_x, y + height * 0.27), COLORS.black)
+    else:
+      self._draw_text_centered(self._font_bold, tr("SPEED"), label_size, rl.Vector2(mid_x, y + height * 0.20), COLORS.black)
+      self._draw_text_centered(self._font_bold, tr("LIMIT"), label_size, rl.Vector2(mid_x, y + height * 0.40), COLORS.black)
+
+    speed_font_size = int(width * 0.52) if len(speed_str) >= 3 else int(width * 0.62)
+    self._draw_text_centered(self._font_bold, speed_str, speed_font_size, rl.Vector2(mid_x, y + height * 0.72), speed_color)
+
+  def _draw_text_centered(self, font, text, size, pos_center, color):
+    sz = measure_text_cached(font, text, size)
+    rl.draw_text_ex(font, text, rl.Vector2(pos_center.x - sz.x / 2, pos_center.y - sz.y / 2), size, 0, color)
+
+  def _format_distance(self, distance: float) -> str:
+    if ui_state.is_metric:
+      if distance < 50:
+        return tr("Near")
+      if distance >= 1000:
+        return f"{distance * METER_TO_KM:.1f}" + tr("km")
+      if distance < 200:
+        rounded = max(10, int(distance / 10) * 10)
+      else:
+        rounded = int(distance / 100) * 100
+      return str(rounded) + tr("m")
+    else:
+      distance_mi = distance * METER_TO_MILE
+      if distance_mi < 0.1:
+        return tr("Near")
+      return f"{distance_mi:.1f}" + tr("mi")
@@ -0,0 +1,30 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+
+import pyray as rl
+from openpilot.selfdrive.ui.mici.onroad.augmented_road_view import AugmentedRoadView
+
+
+class _SuppressedConfidenceBall:
+  def render(self, *_):
+    pass
+
+
+class AugmentedRoadViewSP(AugmentedRoadView):
+  def __init__(self, **kwargs):
+    super().__init__(**kwargs)
+    self._show_confidence_ball: bool = True
+    self._real_confidence_ball = self._confidence_ball
+    self._confidence_ball = _SuppressedConfidenceBall()
+
+  def set_show_confidence_ball(self, show: bool) -> None:
+    self._show_confidence_ball = show
+
+  def _render(self, rect: rl.Rectangle) -> None:
+    super()._render(rect)
+    if self._show_confidence_ball:
+      self._real_confidence_ball.render(self.rect)
@@ -0,0 +1,34 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+
+import pyray as rl
+from openpilot.system.ui.lib.application import MouseEvent
+from openpilot.system.ui.lib.scroll_panel2 import GuiScrollPanel2, ScrollState
+
+
+class GuiScrollPanel2SP(GuiScrollPanel2):
+  """Reject orthogonal-dominant drags so nested scrollers (outer horizontal +
+  inner vertical) don't both engage on a slightly diagonal swipe.
+
+  Implemented as a post-super state rollback rather than reimplementing the
+  PRESSED state machine — keeps stock behaviour authoritative."""
+
+  def _handle_mouse_event(self, mouse_event: MouseEvent, bounds: rl.Rectangle, bounds_size: float,
+                          content_size: float) -> None:
+    pre_state = self._state
+    super()._handle_mouse_event(mouse_event, bounds, bounds_size, content_size)
+
+    if self._state == ScrollState.MANUAL_SCROLL and pre_state == ScrollState.PRESSED and \
+       self._initial_click_event is not None:
+      diff_x = abs(mouse_event.pos.x - self._initial_click_event.pos.x)
+      diff_y = abs(mouse_event.pos.y - self._initial_click_event.pos.y)
+      along = diff_x if self._horizontal else diff_y
+      anti = diff_y if self._horizontal else diff_x
+      if anti > along:
+        self._state = ScrollState.STEADY
+        self._velocity = 0.0
+        self._velocity_buffer.clear()
@@ -0,0 +1,16 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+
+from openpilot.system.ui.widgets.scroller import Scroller
+from openpilot.selfdrive.ui.sunnypilot.mici.widgets.scroll_panel_sp import GuiScrollPanel2SP
+
+
+class ScrollerSP(Scroller):
+  def __init__(self, **kwargs):
+    super().__init__(**kwargs)
+    inner = self._scroller
+    inner.scroll_panel = GuiScrollPanel2SP(inner._horizontal, handle_out_of_bounds=not inner._snap_items)
@@ -10,6 +10,9 @@ from openpilot.selfdrive.ui.layouts.main import MainLayout
 from openpilot.selfdrive.ui.mici.layouts.main import MiciMainLayout
 from openpilot.selfdrive.ui.ui_state import ui_state

+if gui_app.sunnypilot_ui():
+  from openpilot.selfdrive.ui.sunnypilot.mici.layouts.main import MiciMainLayoutSP as MiciMainLayout
+
 BIG_UI = gui_app.big_ui()


@@ -9,7 +9,7 @@ from openpilot.common.params import Params
 from opendbc.car import structs
 from opendbc.safety import ALTERNATIVE_EXPERIENCE
 from opendbc.sunnypilot.car.hyundai.values import HyundaiFlagsSP, HyundaiSafetyFlagsSP
-from opendbc.sunnypilot.car.tesla.values import TeslaFlagsSP
+from opendbc.sunnypilot.car.tesla.values import MadsScreenButtonType, TeslaFlagsSP


 MADS_NO_ACC_MAIN_BUTTON = ("rivian", "tesla")
@@ -21,17 +21,20 @@ class MadsSteeringModeOnBrake:
  DISENGAGE = 2


-def get_mads_limited_brands(CP: structs.CarParams, CP_SP: structs.CarParamsSP) -> bool:
+def get_mads_limited_brands(CP: structs.CarParams, CP_SP: structs.CarParamsSP, params: Params) -> bool:
  if CP.brand == 'rivian':
    return True
  if CP.brand == 'tesla':
-    return not CP_SP.flags & TeslaFlagsSP.HAS_VEHICLE_BUS
+    if not CP_SP.flags & TeslaFlagsSP.HAS_VEHICLE_BUS:
+      return True
+    screen_button = int(params.get("TeslaMadsScreenButton", return_default=True))
+    return screen_button == MadsScreenButtonType.OFF

  return False


 def read_steering_mode_param(CP: structs.CarParams, CP_SP: structs.CarParamsSP, params: Params):
-  if get_mads_limited_brands(CP, CP_SP):
+  if get_mads_limited_brands(CP, CP_SP, params):
    return MadsSteeringModeOnBrake.DISENGAGE

  return params.get("MadsSteeringMode", return_default=True)
@@ -63,7 +66,7 @@ def set_car_specific_params(CP: structs.CarParams, CP_SP: structs.CarParamsSP, p
  # MADS is currently partially supported for these platforms due to lack of consistent states to engage controls
  # Only MadsSteeringModeOnBrake.DISENGAGE is supported for these platforms
  # TODO-SP: To enable MADS full support for Rivian and most Tesla, identify consistent signals for MADS toggling
-  mads_partial_support = get_mads_limited_brands(CP, CP_SP)
+  mads_partial_support = get_mads_limited_brands(CP, CP_SP, params)
  if mads_partial_support:
    params.put("MadsSteeringMode", 2, block=True)
    params.put_bool("MadsUnifiedEngagementMode", True, block=True)
@@ -13,7 +13,7 @@ from openpilot.selfdrive.selfdrived.events import Events
 from openpilot.sunnypilot.selfdrive.selfdrived.events import EventsSP
 from openpilot.sunnypilot.mads.helpers import MadsSteeringModeOnBrake, read_steering_mode_param
 from openpilot.sunnypilot.mads.mads import ModularAssistiveDrivingSystem
-from opendbc.sunnypilot.car.tesla.values import TeslaFlagsSP
+from opendbc.sunnypilot.car.tesla.values import MadsScreenButtonType, TeslaFlagsSP

 State = custom.ModularAssistiveDrivingSystem.ModularAssistiveDrivingSystemState
 EventName = log.OnroadEvent.EventName
@@ -38,6 +38,12 @@ def make_panda_state(mocker, controls_allowed_lateral=True):
  return ps


+def make_params_mock(mocker, values):
+  params = mocker.MagicMock()
+  params.get = mocker.MagicMock(side_effect=lambda k, **kwargs: values[k])
+  return params
+
+
 def make_mads(mocker, steering_mode):
  sd = mocker.MagicMock()
  sd.CP = structs.CarParams()
@@ -223,15 +229,27 @@ class TestBrandSteeringModeRestrictions:
    params = mocker.MagicMock()
    assert read_steering_mode_param(CP, CP_SP, params) == MadsSteeringModeOnBrake.DISENGAGE

-  def test_tesla_with_vehicle_bus_uses_param(self, mocker):
+  @pytest.mark.parametrize("screen_button", [MadsScreenButtonType.THREE_FINGER,
+                                             MadsScreenButtonType.FOUR_FINGER,
+                                             MadsScreenButtonType.FIVE_FINGER])
+  def test_tesla_with_vehicle_bus_uses_param(self, mocker, screen_button):
    CP = structs.CarParams()
    CP.brand = "tesla"
    CP_SP = structs.CarParamsSP()
    CP_SP.flags = TeslaFlagsSP.HAS_VEHICLE_BUS
-    params = mocker.MagicMock()
-    params.get = mocker.MagicMock(return_value=MadsSteeringModeOnBrake.REMAIN_ACTIVE)
+    params = make_params_mock(mocker, {"TeslaMadsScreenButton": screen_button,
+                                       "MadsSteeringMode": MadsSteeringModeOnBrake.REMAIN_ACTIVE})
    assert read_steering_mode_param(CP, CP_SP, params) == MadsSteeringModeOnBrake.REMAIN_ACTIVE

+  def test_tesla_with_vehicle_bus_screen_button_off_forced_to_disengage(self, mocker):
+    CP = structs.CarParams()
+    CP.brand = "tesla"
+    CP_SP = structs.CarParamsSP()
+    CP_SP.flags = TeslaFlagsSP.HAS_VEHICLE_BUS
+    params = make_params_mock(mocker, {"TeslaMadsScreenButton": MadsScreenButtonType.OFF,
+                                       "MadsSteeringMode": MadsSteeringModeOnBrake.REMAIN_ACTIVE})
+    assert read_steering_mode_param(CP, CP_SP, params) == MadsSteeringModeOnBrake.DISENGAGE
+
  @pytest.mark.parametrize("brand", ["hyundai", "toyota", "honda", "gm"])
  def test_other_brands_use_param(self, mocker, brand):
    CP = structs.CarParams()
@@ -82,18 +82,3 @@ if os.path.isfile(supercombo_onnx):
  compile_combined('supercombo',
                   f'--supercombo-onnx {supercombo_onnx}',
                   'driving_combined_supercombo_tinygrad.pkl')
-
-if PC:
-  inputs = tinygrad_files + [File(Dir("#openpilot/sunnypilot/modeld_v2").File("install_models_pc.py").abspath)]
-  outputs = []
-  model_dir = Dir("models").abspath
-  cmd = f'python3 {Dir("#openpilot/sunnypilot/modeld_v2").abspath}/install_models_pc.py {model_dir}'
-
-  for model_name in ['supercombo', 'driving_vision', 'driving_off_policy', 'driving_on_policy', 'driving_policy']:
-    if File(f"models/{model_name}.onnx").exists():
-        inputs.append(File(f"models/{model_name}.onnx"))
-        inputs.append(File(f"models/{model_name}_tinygrad.pkl"))
-        outputs.append(File(f"models/{model_name}_metadata.pkl"))
-  if outputs:
-    lenv.Command(outputs, inputs, cmd)
-
@@ -10,471 +10,355 @@ import argparse
 import os
 import pickle
 import time
-from functools import partial
 from collections import defaultdict
-
+from functools import partial
 import numpy as np
-from tinygrad.tensor import Tensor
+os.environ['GMMU'] = '0'
+
+def _patch_tinygrad_fetch_fw():
+  import hashlib
+  import pathlib
+  import zstandard
+  from tinygrad import helpers
+  _orig_fetch_fw = helpers.fetch_fw
+  def fetch_fw(path, name, sha256):
+    p = pathlib.Path(f"/lib/firmware/{path}/{name}.zst")
+    if p.is_file():
+      blob = zstandard.ZstdDecompressor().stream_reader(p.read_bytes()).read()
+      if hashlib.sha256(blob).hexdigest() == sha256:
+        return blob
+    return _orig_fetch_fw(path, name, sha256)
+  helpers.fetch_fw = fetch_fw
+_patch_tinygrad_fetch_fw()
+
+from openpilot.selfdrive.modeld.compile_modeld import NV12Frame, make_frame_prepare, sample_desire, sample_skip, shift_and_sample
+from tinygrad import dtypes
 from tinygrad.device import Device
 from tinygrad.engine.jit import TinyJit
-
-from openpilot.selfdrive.modeld.compile_modeld import (
-  NV12Frame, make_frame_prepare,
-  shift_and_sample, sample_skip, sample_desire,
-)
+from tinygrad.tensor import Tensor

 MODEL_TYPES = ('vision_policy', 'supercombo', 'vision_multi_policy')


-def _detect_desire_key(policy_input_shapes):
-  for k in policy_input_shapes:
-    if k.startswith('desire'):
-      return k
-  return None
+def _detect_desire_key(shapes: dict) -> str | None:
+  return next((key for key in shapes if key.startswith('desire')), None)


-def _detect_vision_keys(vision_input_shapes):
-  img_keys = sorted([k for k in vision_input_shapes if 'img' in k])
-  road_key = next((k for k in img_keys if 'big' not in k), None)
-  wide_key = next((k for k in img_keys if 'big' in k), None)
-  if road_key is None or wide_key is None:
-    raise ValueError(f"Cannot determine road/wide image keys from {list(vision_input_shapes.keys())}")
-  return road_key, wide_key
+def _detect_vision_keys(shapes: dict) -> tuple[str | None, str | None]:
+  img_keys = sorted(key for key in shapes if 'img' in key)
+  return (
+    next((key for key in img_keys if 'big' not in key), None),
+    next((key for key in img_keys if 'big' in key), None)
+  )


-def make_split_input_queues(vision_input_shapes, policy_input_shapes, frame_skip, device):
-  road_key, _ = _detect_vision_keys(vision_input_shapes)
-  img = vision_input_shapes[road_key]
-  n_frames = img[1] // 6
-  img_buf_shape = (frame_skip * (n_frames - 1) + 1, 6, img[2], img[3])
-
-  fb = policy_input_shapes['features_buffer']
-  desire_key = _detect_desire_key(policy_input_shapes)
-  dp = policy_input_shapes[desire_key]
-  tc = policy_input_shapes.get('traffic_convention', (1, 2))
-
-  npy = {
-    'desire': np.zeros(dp[2], dtype=np.float32),
-    'traffic_convention': np.zeros(tc, dtype=np.float32),
-    'tfm': np.zeros((3, 3), dtype=np.float32),
-    'big_tfm': np.zeros((3, 3), dtype=np.float32),
-  }
-
-  handled = {'features_buffer', desire_key, 'traffic_convention'}
-  for key, shape in policy_input_shapes.items():
-    if key in handled:
-      continue
-    npy[key] = np.zeros(shape, dtype=np.float32)
-
-  input_queues = {
-    'img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
-    'big_img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
-    'feat_q': Tensor(np.zeros((frame_skip * (fb[1] - 1) + 1, fb[0], fb[2]), dtype=np.float32), device=device).contiguous().realize(),
-    'desire_q': Tensor(np.zeros((frame_skip * dp[1], dp[0], dp[2]), dtype=np.float32), device=device).contiguous().realize(),
-    **{k: Tensor(v, device='NPY').realize() for k, v in npy.items()},
-  }
-  return input_queues, npy
+def derive_frame_skip(vision_input_shapes: dict, policy_input_shapes: dict) -> int:
+  features_buffer = policy_input_shapes.get('features_buffer')
+  return 1 if not features_buffer or features_buffer[1] >= 99 else 4


-def make_run_split_policy(vision_runner, policy_runner, nv12: NV12Frame, model_w, model_h,
-                          vision_features_slice, frame_skip, desire_key, extra_policy_keys,
-                          vision_road_key, vision_wide_key, prepare_only=False):
-  frame_prepare = make_frame_prepare(nv12, model_w, model_h)
-  sample_skip_fn = partial(sample_skip, frame_skip=frame_skip)
-  sample_desire_fn = partial(sample_desire, frame_skip=frame_skip)
+def get_policy_npy_shapes(input_shapes: dict, is_supercombo: bool = False) -> tuple[dict, list[int]]:
+  desire_key = _detect_desire_key(input_shapes)
+  shapes = {}
+  if desire_key:
+    shapes['desire'] = (input_shapes[desire_key][2],)

-  def run_policy(img_q, big_img_q, feat_q, desire_q, desire, traffic_convention, tfm, big_tfm, frame, big_frame, **extra):
-    npy_tensors = [tfm.to(Device.DEFAULT), big_tfm.to(Device.DEFAULT),
-                   desire.to(Device.DEFAULT), traffic_convention.to(Device.DEFAULT)]
-    extra_device = {k: extra[k].to(Device.DEFAULT) for k in extra_policy_keys}
-    Tensor.realize(*npy_tensors, *extra_device.values())
-    tfm, big_tfm, desire, traffic_convention = npy_tensors
+  if is_supercombo and 'features_buffer' in input_shapes:
+    fb = input_shapes['features_buffer']
+    shapes['prev_feat'] = (fb[0], fb[2])

-    img = shift_and_sample(img_q, frame_prepare(frame, tfm).unsqueeze(0), sample_skip_fn)
-    big_img = shift_and_sample(big_img_q, frame_prepare(big_frame, big_tfm).unsqueeze(0), sample_skip_fn)
+  for key, shape in input_shapes.items():
+    if key not in (desire_key, 'features_buffer') and 'img' not in key:
+      shapes[key] = tuple(shape)

-    if prepare_only:
-      return img, big_img
-
-    vision_out = next(iter(vision_runner({vision_road_key: img, vision_wide_key: big_img}).values())).cast('float32')
-
-    new_feat = vision_out[:, vision_features_slice].reshape(1, -1).unsqueeze(0)
-    feat_buf = shift_and_sample(feat_q, new_feat, sample_skip_fn)
-    desire_buf = shift_and_sample(desire_q, desire.reshape(1, 1, -1), sample_desire_fn)
-
-    inputs = {'features_buffer': feat_buf, desire_key: desire_buf, 'traffic_convention': traffic_convention, **extra_device}
-    policy_out = next(iter(policy_runner(inputs).values())).cast('float32')
-
-    return vision_out, policy_out
-  return run_policy
+  sizes = [int(np.prod(size)) for size in shapes.values()]
+  return shapes, sizes


-def compile_split_policy(nv12: NV12Frame, model_w, model_h, prepare_only, frame_skip,
-                         vision_runner, policy_runner, vision_metadata, policy_metadata):
-  print(f"Compiling combined policy JIT for {nv12.width}x{nv12.height} (prepare_only={prepare_only})...")
-
-  vision_features_slice = vision_metadata['output_slices']['hidden_state']
-  vision_input_shapes = vision_metadata['input_shapes']
-  policy_input_shapes = policy_metadata['input_shapes']
-  desire_key = _detect_desire_key(policy_input_shapes)
-  extra_policy_keys = [k for k in policy_input_shapes if k not in ('features_buffer', desire_key, 'traffic_convention')]
-  vision_road_key, vision_wide_key = _detect_vision_keys(vision_input_shapes)
-
-  _run = make_run_split_policy(vision_runner, policy_runner, nv12, model_w, model_h,
-                               vision_features_slice, frame_skip, desire_key, extra_policy_keys,
-                               vision_road_key, vision_wide_key, prepare_only)
-  run_policy_jit = TinyJit(_run, prune=True)
-
-  SEED = 42
-
-  def random_inputs_run_fn(fn, seed, test_val=None, test_buffers=None, expect_match=True):
-    input_queues, npy = make_split_input_queues(vision_input_shapes, policy_input_shapes, frame_skip, Device.DEFAULT)
-    rng = np.random.default_rng(seed)
-    Tensor.manual_seed(seed)
-
-    testing = test_val is not None or test_buffers is not None
-    n_runs = 1 if testing else 3
-
-    for i in range(n_runs):
-      frame = Tensor.randint(nv12.size, low=0, high=256, dtype='uint8').realize()
-      big_frame = Tensor.randint(nv12.size, low=0, high=256, dtype='uint8').realize()
-      for v in npy.values():
-        v[:] = rng.standard_normal(v.shape).astype(v.dtype)
-      Device.default.synchronize()
-      st = time.perf_counter()
-      outs = fn(**input_queues, frame=frame, big_frame=big_frame)
-      mt = time.perf_counter()
-      Device.default.synchronize()
-      et = time.perf_counter()
-      print(f"  [{i+1}/{n_runs}] enqueue {(mt-st)*1e3:6.2f} ms -- total {(et-st)*1e3:6.2f} ms")
-
-      if i == 0:
-        val = [np.copy(v.numpy()) for v in outs]
-        buffers = [np.copy(v.numpy().copy()) for v in input_queues.values()]
-
-    if test_val is not None:
-      match = all(np.array_equal(a, b) for a, b in zip(val, test_val, strict=True))
-      assert match == expect_match, f"outputs {'differ from' if expect_match else 'match'} baseline (seed={seed})"
-    if test_buffers is not None:
-      match = all(np.array_equal(a, b) for a, b in zip(buffers, test_buffers, strict=True))
-      assert match == expect_match, f"buffers {'differ from' if expect_match else 'match'} baseline (seed={seed})"
-    return fn, val, buffers
-
-  print('capture + replay')
-  run_policy_jit, test_val, test_buffers = random_inputs_run_fn(run_policy_jit, SEED)
-
-  print('pickle round trip')
-  run_policy_jit = pickle.loads(pickle.dumps(run_policy_jit))
-  random_inputs_run_fn(run_policy_jit, SEED, test_val, test_buffers, expect_match=True)
-  random_inputs_run_fn(run_policy_jit, SEED+1, test_val, test_buffers, expect_match=False)
-  return run_policy_jit
-
-
-def derive_frame_skip(vision_input_shapes, policy_input_shapes):
-  fb = policy_input_shapes.get('features_buffer')
-  if fb is None:
-    return 1
-  fb_history = fb[1]
-  if fb_history >= 99:
-    return 1
-  return 4
-
-
-def make_supercombo_input_queues(input_shapes, frame_skip, device):
-  img_shape = input_shapes.get('img', input_shapes.get('input_imgs'))
-  if img_shape is None:
-    raise ValueError("No img input found in model shapes")
+def generate_queues_and_npy(input_shapes: dict, frame_skip: int, device: str = Device.DEFAULT,
+                            is_supercombo: bool = False, use_packed: bool = True) -> tuple[dict, dict]:
+  road_key, _ = _detect_vision_keys(input_shapes)
+  if not road_key:
+    raise ValueError("Vision road key missing from input shapes.")

+  img_shape = input_shapes[road_key]
  n_frames = img_shape[1] // 6
  img_buf_shape = (frame_skip * (n_frames - 1) + 1, 6, img_shape[2], img_shape[3])

-  numpy_keys = {}
-  queue_keys = {}
+  desire_key = _detect_desire_key(input_shapes)
+  if not desire_key:
+    raise ValueError("Desire key missing from input shapes.")

-  for key, shape in input_shapes.items():
-    if 'img' in key:
-      continue
-    if len(shape) == 3 and shape[1] > 1:
-      if key.startswith('desire'):
-        numpy_keys[key] = np.zeros(shape[2], dtype=np.float32)
-        queue_keys[f'{key}_q'] = Tensor(
-          np.zeros((frame_skip * shape[1], shape[0], shape[2]), dtype=np.float32),
-          device=device).contiguous().realize()
-      elif key == 'features_buffer':
-        queue_keys['feat_q'] = Tensor(
-          np.zeros((frame_skip * (shape[1] - 1) + 1, shape[0], shape[2]), dtype=np.float32),
-          device=device).contiguous().realize()
-      else:
-        numpy_keys[key] = np.zeros(shape, dtype=np.float32)
-    elif len(shape) == 2:
-      numpy_keys[key] = np.zeros(shape, dtype=np.float32)
+  desire_shape = input_shapes[desire_key]
+  features_buffer = input_shapes.get('features_buffer')

-  if 'traffic_convention' not in numpy_keys:
-    tc_shape = input_shapes.get('traffic_convention', (1, 2))
-    numpy_keys['traffic_convention'] = np.zeros(tc_shape, dtype=np.float32)
+  if use_packed:  # remove packed detection block after all models are recompiled
+    npy_arrays = {
+      'tfm': np.zeros((3, 3), dtype=np.float32),
+      'big_tfm': np.zeros((3, 3), dtype=np.float32)
+    }

-  numpy_keys['tfm'] = np.zeros((3, 3), dtype=np.float32)
-  numpy_keys['big_tfm'] = np.zeros((3, 3), dtype=np.float32)
+    shapes, sizes = get_policy_npy_shapes(input_shapes, is_supercombo=is_supercombo)
+    packed_npy_inputs = np.zeros(sum(sizes), dtype=np.float32)

-  input_queues = {
-    'img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
-    'big_img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
-    **queue_keys,
-    **{k: Tensor(v, device='NPY').realize() for k, v in numpy_keys.items()},
-  }
-  return input_queues, numpy_keys
+    split_indices = np.cumsum(sizes[:-1]) if len(sizes) > 1 else []
+    split_views = np.split(packed_npy_inputs, split_indices) if len(sizes) > 0 else []
+    for (k, s), v in zip(shapes.items(), split_views, strict=True):
+      npy_arrays[k] = v.reshape(s)
+
+    queues = {
+      'img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
+      'big_img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
+      'desire_q': Tensor(np.zeros((frame_skip * desire_shape[1], desire_shape[0], desire_shape[2]),
+                    dtype=np.float32), device=device).contiguous().realize(),
+      'packed_npy_inputs': Tensor(packed_npy_inputs, device='NPY').realize(),
+    }
+
+    if features_buffer:
+      queues['feat_q'] = Tensor(np.zeros((frame_skip * (features_buffer[1] - 1) + 1, features_buffer[0], features_buffer[2]),
+                         dtype=np.float32), device=device).contiguous().realize()
+
+    queues.update({key: Tensor(value, device='NPY').realize() for key, value in npy_arrays.items() if key in ('tfm', 'big_tfm')})
+  else:
+    # TODO-SP: Remove legacy queuing fallback else block after all models are recompiled
+    npy_arrays = {
+      'desire': np.zeros(desire_shape[2], dtype=np.float32),
+      'tfm': np.zeros((3, 3), dtype=np.float32),
+      'big_tfm': np.zeros((3, 3), dtype=np.float32)
+    }
+
+    for key, shape in input_shapes.items():
+      if key not in npy_arrays and 'img' not in key and key not in ('features_buffer', desire_key):
+        npy_arrays[key] = np.zeros(shape, dtype=np.float32)
+
+    queues = {
+      'img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
+      'big_img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
+      'desire_q': Tensor(np.zeros((frame_skip * desire_shape[1], desire_shape[0], desire_shape[2]),
+                    dtype=np.float32), device=device).contiguous().realize()
+    }
+
+    if features_buffer:
+      queues['feat_q'] = Tensor(np.zeros((frame_skip * (features_buffer[1] - 1) + 1, features_buffer[0], features_buffer[2]),
+                         dtype=np.float32), device=device).contiguous().realize()
+
+    queues.update({key: Tensor(value, device='NPY').realize() for key, value in npy_arrays.items()})
+
+  return queues, npy_arrays


-def make_run_supercombo(model_runner, nv12: NV12Frame, model_w, model_h,
-                        features_slice, frame_skip, input_shapes, prepare_only=False):
-  frame_prepare = make_frame_prepare(nv12, model_w, model_h)
+def make_split_input_queues(vision_input_shapes: dict, policy_input_shapes: dict,
+                            frame_skip: int, device: str = Device.DEFAULT, use_packed: bool = True) -> tuple[dict, dict]:
+  return generate_queues_and_npy({**vision_input_shapes, **policy_input_shapes}, frame_skip, device, is_supercombo=False, use_packed=use_packed)
+
+
+def make_supercombo_input_queues(input_shapes: dict, frame_skip: int,
+                                 device: str = Device.DEFAULT, use_packed: bool = True) -> tuple[dict, dict]:
+  return generate_queues_and_npy(input_shapes, frame_skip, device, is_supercombo=True, use_packed=use_packed)
+
+
+def create_jit_runner(vision_runner, policy_runners: list, nv12: NV12Frame, model_size: tuple[int, int],
+            features_slice: slice, frame_skip: int, input_shapes: dict, prepare_only: bool):
+  frame_prepare = make_frame_prepare(nv12, *model_size)
  sample_skip_fn = partial(sample_skip, frame_skip=frame_skip)
  sample_desire_fn = partial(sample_desire, frame_skip=frame_skip)

  desire_key = _detect_desire_key(input_shapes)
-  if desire_key is None:
-    raise ValueError(f"No desire* key found in input_shapes: {list(input_shapes.keys())}")
-  road_img_key, wide_img_key = _detect_vision_keys(input_shapes)
-  extra_policy_keys = [k for k in input_shapes
-                       if k not in (desire_key, 'features_buffer', 'traffic_convention')
-                       and 'img' not in k]
+  road_key, wide_key = _detect_vision_keys(input_shapes)

-  def run_supercombo(img_q, big_img_q, feat_q, desire_q,
-                     frame, big_frame, **kwargs):
-    desire = kwargs.get(desire_key)
-    traffic_convention = kwargs.get('traffic_convention')
-    tfm = kwargs['tfm']
-    big_tfm = kwargs['big_tfm']
+  if not desire_key or not road_key or not wide_key:
+    raise ValueError("Missing required vision or desire keys in input shapes.")

-    tfm = tfm.to(Device.DEFAULT)
-    big_tfm = big_tfm.to(Device.DEFAULT)
-    desire = desire.to(Device.DEFAULT)
-    traffic_convention = traffic_convention.to(Device.DEFAULT)
-    Tensor.realize(tfm, big_tfm, desire, traffic_convention)
+  is_supercombo = vision_runner is None
+  npy_shapes, npy_sizes = get_policy_npy_shapes(input_shapes, is_supercombo=is_supercombo)

-    img = shift_and_sample(img_q, frame_prepare(frame, tfm).unsqueeze(0), sample_skip_fn)
-    big_img = shift_and_sample(big_img_q, frame_prepare(big_frame, big_tfm).unsqueeze(0), sample_skip_fn)
+  def runner(img_q, big_img_q, feat_q, packed_npy_inputs, frame, big_frame, tfm, big_tfm, **kwargs):
+    desire_q = kwargs['desire_q']
+
+    packed_npy_inputs_dev = packed_npy_inputs.to(Device.DEFAULT)
+    tfm_dev = tfm.to(Device.DEFAULT)
+    big_tfm_dev = big_tfm.to(Device.DEFAULT)
+
+    Tensor.realize(packed_npy_inputs_dev, tfm_dev, big_tfm_dev)
+
+    img = shift_and_sample(img_q, frame_prepare(frame, tfm_dev).unsqueeze(0), sample_skip_fn).realize()
+    big_img = shift_and_sample(big_img_q, frame_prepare(big_frame, big_tfm_dev).unsqueeze(0), sample_skip_fn).realize()

    if prepare_only:
      return img, big_img

-    desire_buf = shift_and_sample(desire_q, desire.reshape(1, 1, -1), sample_desire_fn)
-    feat_buf = sample_skip_fn(feat_q)
+    unpacked_tensors = [tensor.reshape(shape) for tensor, shape in zip(packed_npy_inputs_dev.split(npy_sizes), npy_shapes.values(), strict=True)]
+    unpacked_dict = dict(zip(npy_shapes.keys(), unpacked_tensors, strict=True))

-    inputs = {road_img_key: img, wide_img_key: big_img,
-              desire_key: desire_buf, 'features_buffer': feat_buf,
-              'traffic_convention': traffic_convention}
-    for k in extra_policy_keys:
-      if k in kwargs:
-        inputs[k] = kwargs[k].to(Device.DEFAULT)
+    desire_dev = unpacked_dict['desire']
+    desire_buf = shift_and_sample(desire_q, desire_dev.reshape(1, 1, -1), sample_desire_fn).realize()

-    model_out = next(iter(model_runner(inputs).values())).cast('float32')
+    inputs = {desire_key: desire_buf}
+    for key, tensor_val in unpacked_dict.items():
+      if key not in ('desire', 'prev_feat'):
+        inputs[key] = tensor_val

-    new_feat = model_out[:, features_slice].reshape(1, -1).unsqueeze(0)
-    shift_and_sample(feat_q, new_feat, sample_skip_fn)
+    if 'prev_feat' in unpacked_dict:
+      prev_feat_dev = unpacked_dict['prev_feat']
+      inputs['features_buffer'] = shift_and_sample(feat_q, prev_feat_dev.reshape(1, 1, -1), sample_skip_fn).realize()

-    return model_out
+    if vision_runner:
+      vision_out_cast = next(iter(vision_runner({road_key: img, wide_key: big_img}).values())).cast('float32').realize()
+      if 'features_buffer' not in inputs:
+        new_feat = vision_out_cast[:, features_slice].reshape(1, -1).unsqueeze(0)
+        inputs['features_buffer'] = shift_and_sample(feat_q, new_feat, sample_skip_fn).realize()
+      policy_outs = [next(iter(pol_runner(inputs).values())).cast('float32').realize() for pol_runner in policy_runners]
+      return (vision_out_cast, *policy_outs) if len(policy_outs) > 1 else (vision_out_cast, policy_outs[0])

-  return run_supercombo
+    inputs.update({road_key: img, wide_key: big_img})
+    if 'features_buffer' not in inputs:
+      inputs['features_buffer'] = sample_skip_fn(feat_q)
+
+    policy_out = next(iter(policy_runners[0](inputs).values())).cast('float32').realize()
+    if 'features_buffer' not in inputs and features_slice is not None:
+      new_feat = policy_out[:, features_slice].reshape(1, -1).unsqueeze(0)
+      shift_and_sample(feat_q, new_feat, sample_skip_fn).realize()
+    return policy_out
+
+  return runner


-def make_run_vision_multi_policy(vision_runner, policy_runners, nv12: NV12Frame, model_w, model_h,
-                                 vision_features_slice, frame_skip, desire_key, extra_policy_keys,
-                                 vision_road_key, vision_wide_key, prepare_only=False):
-  frame_prepare = make_frame_prepare(nv12, model_w, model_h)
-  sample_skip_fn = partial(sample_skip, frame_skip=frame_skip)
-  sample_desire_fn = partial(sample_desire, frame_skip=frame_skip)
+def compile_and_warmup(nv12: NV12Frame, model_size: tuple[int, int], prepare_only: bool, frame_skip: int, vision_runner, policy_runners: list, metadata: dict):
+  print(f"Compiling combined JIT for {nv12.width}x{nv12.height} (prepare_only={prepare_only})...")

-  def run_multi_policy(img_q, big_img_q, feat_q, desire_q, desire,
-                       traffic_convention, tfm, big_tfm, frame, big_frame, **extra):
-    npy_tensors = [tfm.to(Device.DEFAULT), big_tfm.to(Device.DEFAULT),
-                   desire.to(Device.DEFAULT), traffic_convention.to(Device.DEFAULT)]
-    extra_device = {k: extra[k].to(Device.DEFAULT) for k in extra_policy_keys}
-    Tensor.realize(*npy_tensors, *extra_device.values())
-    tfm, big_tfm, desire, traffic_convention = npy_tensors
+  all_shapes = {key: value for meta in metadata.values() for key, value in meta['input_shapes'].items()}

-    img = shift_and_sample(img_q, frame_prepare(frame, tfm).unsqueeze(0), sample_skip_fn)
-    big_img = shift_and_sample(big_img_q, frame_prepare(big_frame, big_tfm).unsqueeze(0), sample_skip_fn)
+  feat_meta = metadata.get('vision') or metadata.get('model') or metadata.get('policy')
+  if not feat_meta:
+    raise ValueError("Could not find vision, model, or policy metadata.")

-    if prepare_only:
-      return img, big_img
+  features_slice = feat_meta['output_slices']['hidden_state']
+  WARP_DEV = 'CPU' if "USBGPU" in os.environ else Device.DEFAULT

-    vision_out = next(iter(vision_runner({vision_road_key: img, vision_wide_key: big_img}).values())).cast('float32')
+  is_supercombo = vision_runner is None
+  run_func = create_jit_runner(vision_runner, policy_runners, nv12, model_size, features_slice, frame_skip, all_shapes, prepare_only)
+  run_jit = TinyJit(run_func, prune=True)
+  queues, npy_arrays = generate_queues_and_npy(all_shapes, frame_skip, Device.DEFAULT, is_supercombo=is_supercombo)

-    new_feat = vision_out[:, vision_features_slice].reshape(1, -1).unsqueeze(0)
-    feat_buf = shift_and_sample(feat_q, new_feat, sample_skip_fn)
-    desire_buf = shift_and_sample(desire_q, desire.reshape(1, 1, -1), sample_desire_fn)
-
-    inputs = {'features_buffer': feat_buf, desire_key: desire_buf, 'traffic_convention': traffic_convention, **extra_device}
-
-    policy_outputs = []
-    for runner in policy_runners:
-      policy_out = next(iter(runner(inputs).values())).cast('float32')
-      policy_outputs.append(policy_out)
-
-    return (vision_out, *policy_outputs)
-
-  return run_multi_policy
-
-
-def _warmup_and_serialize(run_jit, input_queues, npy, nv12):
  for i in range(3):
    rng = np.random.default_rng(42 + i)
-    frame = Tensor.randint(nv12.size, low=0, high=256, dtype='uint8').realize()
-    big_frame = Tensor.randint(nv12.size, low=0, high=256, dtype='uint8').realize()
-    for v in npy.values():
-      v[:] = rng.standard_normal(v.shape).astype(v.dtype)
+    frame = Tensor.randint(nv12.size, low=0, high=256, dtype=dtypes.uint8, device=WARP_DEV).realize()
+    big_frame = Tensor.randint(nv12.size, low=0, high=256, dtype=dtypes.uint8, device=WARP_DEV).realize()
+    for arr in npy_arrays.values():
+      arr[:] = rng.standard_normal(arr.shape).astype(arr.dtype)
+
    Device.default.synchronize()
-    st = time.perf_counter()
-    run_jit(**input_queues, frame=frame, big_frame=big_frame)
-    mt = time.perf_counter()
+    start_time = time.perf_counter()
+    run_jit(**queues, frame=frame, big_frame=big_frame)
+    mid_time = time.perf_counter()
    Device.default.synchronize()
-    et = time.perf_counter()
-    print(f"  [{i + 1}/3] enqueue {(mt - st) * 1e3:6.2f} ms -- total {(et - st) * 1e3:6.2f} ms")
-  return pickle.loads(pickle.dumps(run_jit))
+    print(f"  [{i + 1}/3] enqueue {(mid_time - start_time) * 1e3:6.2f} ms -- total {(time.perf_counter() - start_time) * 1e3:6.2f} ms")
+
+  # TODO-SP: switch to dump_oob/load_oob on next full recompile of all models
+  return pickle.loads(pickle.dumps(run_jit)) if not prepare_only else run_jit


-def compile_supercombo(nv12: NV12Frame, model_w, model_h, prepare_only, frame_skip,
-                       model_runner, metadata):
-  print(f"Compiling combined supercombo JIT for {nv12.width}x{nv12.height} (prepare_only={prepare_only})...")
-
-  features_slice = metadata['output_slices']['hidden_state']
-  input_shapes = metadata['input_shapes']
-
-  _run = make_run_supercombo(model_runner, nv12, model_w, model_h,
-                             features_slice, frame_skip, input_shapes, prepare_only)
-  run_jit = TinyJit(_run, prune=True)
-
-  input_queues, npy = make_supercombo_input_queues(input_shapes, frame_skip, Device.DEFAULT)
-
-  run_jit = _warmup_and_serialize(run_jit, input_queues, npy, nv12)
-  return run_jit
+def _parse_size(size_str: str) -> tuple[int, int]:
+  width, height = size_str.lower().split('x')
+  return int(width), int(height)


-def compile_multi_policy(nv12: NV12Frame, model_w, model_h, prepare_only, frame_skip,
-                         vision_runner, policy_runners, vision_metadata, policy_metadata):
-  print(f"Compiling combined multi-policy JIT for {nv12.width}x{nv12.height} (prepare_only={prepare_only})...")
-
-  vision_features_slice = vision_metadata['output_slices']['hidden_state']
-  vision_input_shapes = vision_metadata['input_shapes']
-  policy_input_shapes = policy_metadata['input_shapes']
-  desire_key = _detect_desire_key(policy_input_shapes)
-  extra_policy_keys = [k for k in policy_input_shapes if k not in ('features_buffer', desire_key, 'traffic_convention')]
-  vision_road_key, vision_wide_key = _detect_vision_keys(vision_input_shapes)
-
-  _run = make_run_vision_multi_policy(vision_runner, policy_runners, nv12, model_w, model_h,
-                                      vision_features_slice, frame_skip, desire_key, extra_policy_keys,
-                                      vision_road_key, vision_wide_key, prepare_only)
-  run_jit = TinyJit(_run, prune=True)
-
-  input_queues, npy = make_split_input_queues(vision_input_shapes, policy_input_shapes, frame_skip, Device.DEFAULT)
-
-  run_jit = _warmup_and_serialize(run_jit, input_queues, npy, nv12)
-  return run_jit
+def read_file_chunked_to_shm(path):
+  if not path:
+    return None
+  import atexit
+  import shutil
+  from openpilot.common.file_chunker import open_file_chunked
+  from openpilot.common.hardware.hw import Paths
+  shm_path = os.path.join(Paths.shm_path(), os.path.basename(path))
+  atexit.register(lambda: os.path.exists(shm_path) and os.remove(shm_path))
+  with open(shm_path, 'wb') as dst, open_file_chunked(path) as src:
+    shutil.copyfileobj(src, dst)
+  return shm_path


-def _parse_size(s):
-  w, h = s.lower().split('x')
-  return int(w), int(h)
+def _compile_for_resolutions(camera_resolutions: list, model_size: tuple[int, int], frame_skip: int,
+               vision_runner, policy_runners: list, metadata: dict) -> dict:
+  from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
+  return {
+    (cam_w, cam_h): {
+      name: compile_and_warmup(NV12Frame(cam_w, cam_h, *get_nv12_info(cam_w, cam_h)), model_size, prepare_only,
+                   frame_skip, vision_runner, policy_runners, metadata)
+      for name, prepare_only in [('warp_enqueue', True), ('run_policy', False)]
+    }
+    for cam_w, cam_h in camera_resolutions
+  }
+
+
+def _load_policy_runners(args: argparse.Namespace) -> tuple[list, list]:
+  runners, keys = [], []
+  for name, onnx_arg in [('policy', args.policy_onnx), ('off_policy', args.off_policy_onnx), ('on_policy', args.on_policy_onnx)]:
+    if onnx_arg:
+      runners.append(OnnxRunner(onnx_arg))
+      keys.append(name)
+  return runners, keys


 if __name__ == "__main__":
-  from tinygrad.nn.onnx import OnnxRunner
-  from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
  from openpilot.selfdrive.modeld.get_model_metadata import make_metadata_dict
+  from tinygrad.nn.onnx import OnnxRunner

-  p = argparse.ArgumentParser(description="Compile combined JIT pkl for sunnypilot modeld_v2")
-  p.add_argument('--model-type', choices=MODEL_TYPES, required=True)
-  p.add_argument('--model-size', type=_parse_size, required=True, help='model input WxH')
-  p.add_argument('--camera-resolutions', type=_parse_size, nargs='+', required=True)
-  p.add_argument('--frame-skip', type=int, default=None, help='frame skip value (auto-derived if not provided)')
-  p.add_argument('--output', required=True)
+  parser = argparse.ArgumentParser(description="Compile combined JIT pkl for sunnypilot modeld_v2")
+  parser.add_argument('--model-type', choices=MODEL_TYPES, required=True)
+  parser.add_argument('--model-size', type=_parse_size, required=True, help='model input WxH')
+  parser.add_argument('--camera-resolutions', type=_parse_size, nargs='+', required=True)
+  parser.add_argument('--frame-skip', type=int, default=None, help='frame skip value (auto-derived if not provided)')
+  parser.add_argument('--output', required=True)

-  p.add_argument('--vision-onnx', help='vision ONNX (for split models)')
-  p.add_argument('--policy-onnx', help='policy ONNX (for vision_policy)')
-  p.add_argument('--off-policy-onnx', help='off-policy ONNX (for vision_multi_policy)')
-  p.add_argument('--on-policy-onnx', help='on-policy ONNX (for vision_multi_policy)')
-  p.add_argument('--supercombo-onnx', help='supercombo ONNX (for supercombo)')
+  parser.add_argument('--vision-onnx', help='vision ONNX (for split models)')
+  parser.add_argument('--policy-onnx', help='policy ONNX (for vision_policy)')
+  parser.add_argument('--off-policy-onnx', help='off-policy ONNX (for vision_multi_policy)')
+  parser.add_argument('--on-policy-onnx', help='on-policy ONNX (for vision_multi_policy)')
+  parser.add_argument('--supercombo-onnx', help='supercombo ONNX (for supercombo)')

-  args = p.parse_args()
-  out = defaultdict(dict)
+  args = parser.parse_args()
+  output_data = defaultdict(dict)
+
+  args.vision_onnx = read_file_chunked_to_shm(args.vision_onnx)
+  args.policy_onnx = read_file_chunked_to_shm(args.policy_onnx)
+  args.off_policy_onnx = read_file_chunked_to_shm(args.off_policy_onnx)
+  args.on_policy_onnx = read_file_chunked_to_shm(args.on_policy_onnx)
+  args.supercombo_onnx = read_file_chunked_to_shm(args.supercombo_onnx)
+
+  vision_runner = OnnxRunner(args.vision_onnx) if args.vision_onnx else None

  if args.model_type == 'vision_policy':
-    assert args.vision_onnx and args.policy_onnx
-    vision_runner = OnnxRunner(args.vision_onnx)
-    policy_runner = OnnxRunner(args.policy_onnx)
-    out['metadata']['vision'] = make_metadata_dict(args.vision_onnx)
-    out['metadata']['policy'] = make_metadata_dict(args.policy_onnx)
-
-    frame_skip = args.frame_skip if args.frame_skip is not None else derive_frame_skip(out['metadata']['vision']['input_shapes'],
-                                                                                       out['metadata']['policy']['input_shapes'])
-
-    for cam_w, cam_h in args.camera_resolutions:
-      nv12 = NV12Frame(cam_w, cam_h, *get_nv12_info(cam_w, cam_h))
-      model_w, model_h = args.model_size
-      out[(cam_w, cam_h)] = {
-        name: compile_split_policy(nv12, model_w, model_h, prepare_only, frame_skip,
-                                   vision_runner, policy_runner,
-                                   out['metadata']['vision'], out['metadata']['policy'])
-        for name, prepare_only in [('warp_enqueue', True), ('run_policy', False)]
-      }
-
+    assert vision_runner and args.policy_onnx
+    policy_runners = [OnnxRunner(args.policy_onnx)]
+    output_data['metadata'] = {'vision': make_metadata_dict(args.vision_onnx), 'policy': make_metadata_dict(args.policy_onnx)}
  elif args.model_type == 'supercombo':
    assert args.supercombo_onnx
-    model_runner = OnnxRunner(args.supercombo_onnx)
-    out['metadata']['model'] = make_metadata_dict(args.supercombo_onnx)
-
-    frame_skip = args.frame_skip if args.frame_skip is not None else derive_frame_skip({}, out['metadata']['model']['input_shapes'])
-
-    for cam_w, cam_h in args.camera_resolutions:
-      nv12 = NV12Frame(cam_w, cam_h, *get_nv12_info(cam_w, cam_h))
-      model_w, model_h = args.model_size
-      out[(cam_w, cam_h)] = {
-        name: compile_supercombo(nv12, model_w, model_h, prepare_only, frame_skip,
-                                 model_runner, out['metadata']['model'])
-        for name, prepare_only in [('warp_enqueue', True), ('run_policy', False)]
-      }
-
+    policy_runners = [OnnxRunner(args.supercombo_onnx)]
+    output_data['metadata'] = {'model': make_metadata_dict(args.supercombo_onnx)}
  elif args.model_type == 'vision_multi_policy':
-    assert args.vision_onnx
-    vision_runner = OnnxRunner(args.vision_onnx)
-    out['metadata']['vision'] = make_metadata_dict(args.vision_onnx)
+    assert vision_runner
+    policy_runners, policy_names = _load_policy_runners(args)
+    output_data['metadata'] = {'vision': make_metadata_dict(args.vision_onnx)}
+    for name in policy_names:
+      runner_arg = getattr(args, f"{name}_onnx")
+      output_data['metadata'][name] = make_metadata_dict(runner_arg)

-    policy_runners = []
-    policy_onnxes = []
-    if args.policy_onnx:
-      policy_onnxes.append(('policy', args.policy_onnx))
-    if args.off_policy_onnx:
-      policy_onnxes.append(('off_policy', args.off_policy_onnx))
-    if args.on_policy_onnx:
-      policy_onnxes.append(('on_policy', args.on_policy_onnx))
+  policy_keys = [key for key in output_data['metadata'].keys() if key != 'vision']
+  first_policy_meta = output_data['metadata'][policy_keys[0]] if policy_keys else {}
+  vision_meta = output_data['metadata'].get('vision', {})

-    for name, onnx_path in policy_onnxes:
-      runner = OnnxRunner(onnx_path)
-      policy_runners.append(runner)
-      out['metadata'][name] = make_metadata_dict(onnx_path)
+  derived_frame_skip = args.frame_skip or derive_frame_skip(vision_meta.get('input_shapes', {}), first_policy_meta.get('input_shapes', {}))
+  output_data.update(_compile_for_resolutions(args.camera_resolutions, args.model_size, derived_frame_skip,
+                        vision_runner, policy_runners, output_data['metadata']))

-    first_policy_key = policy_onnxes[0][0]
-    frame_skip = args.frame_skip if args.frame_skip is not None else derive_frame_skip(out['metadata']['vision']['input_shapes'],
-                                                                                       out['metadata'][first_policy_key]['input_shapes'])
+  with open(args.output, "wb") as file:
+    # TODO-SP: switch to dump_oob from openpilot/selfdrive/helpers on next full recompile of all models
+    pickle.dump(output_data, file)

-    for cam_w, cam_h in args.camera_resolutions:
-      nv12 = NV12Frame(cam_w, cam_h, *get_nv12_info(cam_w, cam_h))
-      model_w, model_h = args.model_size
-      out[(cam_w, cam_h)] = {
-        name: compile_multi_policy(nv12, model_w, model_h, prepare_only, frame_skip,
-                                   vision_runner, policy_runners,
-                                   out['metadata']['vision'], out['metadata'][first_policy_key])
-        for name, prepare_only in [('warp_enqueue', True), ('run_policy', False)]
-      }
-
-  with open(args.output, "wb") as f:
-    pickle.dump(out, f)
  pkl_size = os.path.getsize(args.output)
  print(f"Saved combined JIT to {args.output} ({pkl_size / 1e6:.2f} MB)")

  from openpilot.common.file_chunker import chunk_file, get_chunk_targets
  chunk_targets = get_chunk_targets(args.output, pkl_size)
  chunk_file(args.output, chunk_targets)
-  num_chunks = len(chunk_targets) - 1
-  print(f"Chunked into {num_chunks} file(s)")
+  print(f"Chunked into {len(chunk_targets) - 1} file(s)")
@@ -1,75 +0,0 @@
-#!/usr/bin/env python3
-import sys
-import shutil
-import pickle
-import codecs
-from pathlib import Path
-
-from openpilot.common.hardware.hw import Paths
-from openpilot.sunnypilot.modeld_v2.get_model_metadata import MetadataOnnxPBParser, get_name_and_shape, get_metadata_value_by_name
-
-
-def generate_metadata_pkl(model_path, output_path):
-  try:
-    model = MetadataOnnxPBParser(model_path).parse()
-    output_slices = get_metadata_value_by_name(model, 'output_slices')
-    if not output_slices:
-      return False
-    metadata = {
-      'model_checkpoint': get_metadata_value_by_name(model, 'model_checkpoint'),
-      'output_slices': pickle.loads(codecs.decode(output_slices.encode(), "base64")),
-      'input_shapes': dict(get_name_and_shape(x) for x in model["graph"]["input"]),
-      'output_shapes': dict(get_name_and_shape(x) for x in model["graph"]["output"]),
-    }
-    with open(output_path, 'wb') as f:
-      pickle.dump(metadata, f)
-    return True
-  except Exception:
-    return False
-
-
-def install_models(model_dir):
-  model_dir = Path(model_dir)
-  models = ["driving_off_policy", "driving_on_policy", "driving_vision"]
-  found_models = []
-
-  for model in models:
-    if (model_dir / f"{model}.onnx").exists():
-      found_models.append(model)
-
-  if not found_models:
-    return
-
-  try:
-    custom_name = input(f"Found models ({', '.join(found_models)}). Enter model short name (e.g. wmiv4): ").strip()
-  except EOFError:
-    return
-
-  if not custom_name:
-    print("No name provided, skipping installation.")
-    return
-
-  dest_dir = Path(Paths.model_root())
-  dest_dir.mkdir(parents=True, exist_ok=True)
-
-  for model in found_models:
-    onnx_path = model_dir / f"{model}.onnx"
-    tinygrad_pkl = model_dir / f"{model}_tinygrad.pkl"
-    metadata_pkl = model_dir / f"{model}_metadata.pkl"
-
-    if not metadata_pkl.exists():
-      generate_metadata_pkl(onnx_path, metadata_pkl)
-
-    dest_tinygrad = dest_dir / f"{model}_{custom_name}_tinygrad.pkl"
-    dest_metadata = dest_dir / f"{model}_{custom_name}_metadata.pkl"
-
-    if tinygrad_pkl.exists():
-      shutil.move(str(tinygrad_pkl), str(dest_tinygrad))
-    if metadata_pkl.exists():
-      shutil.move(str(metadata_pkl), str(dest_metadata))
-
-if __name__ == "__main__":
-  if len(sys.argv) < 2:
-    print("Usage: install_models_pc.py <model_dir>")
-    sys.exit(1)
-  install_models(sys.argv[1])
@@ -7,6 +7,7 @@ See the LICENSE.md file in the root directory for more details.
 """

 import os
+os.environ['GMMU'] = '0'
 from openpilot.common.hardware import TICI
 os.environ['DEV'] = 'QCOM' if TICI else 'CPU'
 USBGPU = "USBGPU" in os.environ
@@ -23,6 +24,11 @@ from setproctitle import setproctitle
 from openpilot.cereal.messaging import PubMaster, SubMaster
 from msgq.visionipc import VisionIpcClient, VisionStreamType, VisionBuf
 from opendbc.car.car_helpers import get_demo_car_params
+
+from tinygrad.tensor import Tensor
+from tinygrad.device import Device
+
+from openpilot.common.file_chunker import open_file_chunked
 from openpilot.common.swaglog import cloudlog
 from openpilot.common.params import Params
 from openpilot.common.filter_simple import FirstOrderFilter
@@ -30,6 +36,7 @@ from openpilot.common.realtime import config_realtime_process, DT_MDL
 from openpilot.common.transformations.camera import DEVICE_CAMERAS
 from openpilot.common.transformations.model import get_warp_matrix
 from openpilot.system import sentry
+from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
 from openpilot.selfdrive.controls.lib.desire_helper import DesireHelper
 from openpilot.selfdrive.controls.lib.drive_helpers import get_accel_from_plan, smooth_value

@@ -37,10 +44,12 @@ from openpilot.sunnypilot.modeld_v2.fill_model_msg import fill_model_msg, fill_p
 from openpilot.sunnypilot.modeld_v2.constants import Plan
 from openpilot.sunnypilot.modeld_v2.meta_helper import load_meta_constants
 from openpilot.sunnypilot.modeld_v2.camera_offset_helper import CameraOffsetHelper
+from openpilot.sunnypilot.modeld_v2.compile_modeld import derive_frame_skip, make_split_input_queues

 from openpilot.sunnypilot.livedelay.helpers import get_lat_delay
 from openpilot.sunnypilot.modeld_v2.modeld_base import ModelStateBase
 from openpilot.sunnypilot.models.helpers import get_active_bundle
+from openpilot.sunnypilot.selfdrive.controls.lib.relc import RoadEdgeLaneChangeController

 PROCESS_NAME = "openpilot.selfdrive.modeld.modeld_tinygrad"

@@ -99,29 +108,37 @@ class ModelState(ModelStateBase):
    self._init_combined(pkl_path, cam_w, cam_h, model_bundle)

  def _init_combined(self, pkl_path, cam_w, cam_h, bundle):
-    from tinygrad.tensor import Tensor
-    from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
-    from openpilot.sunnypilot.modeld_v2.compile_modeld import derive_frame_skip, make_split_input_queues
-    from tinygrad.device import Device
-
-    from openpilot.common.file_chunker import open_file_chunked
-
    cloudlog.warning(f"loading combined pkl: {pkl_path}")
+    # TODO-SP: switch to load_oob from openpilot/selfdrive/helpers on next full recompile of all models
    jits = pickle.load(open_file_chunked(pkl_path))

    self.DEV = Device.DEFAULT
+    self.WARP_DEV = 'CPU' if USBGPU else self.DEV
+    self.QUEUE_DEV = self.DEV

    metadata = jits['metadata']
+
+    self._run_policy = jits[(cam_w, cam_h)]['run_policy']
+    self._warp_enqueue = jits[(cam_w, cam_h)]['warp_enqueue']
+
+    # TODO-SP: Remove legacy use_packed detection block after all models are recompiled
+    captured = getattr(self._run_policy, 'captured', None)
+    if captured is not None:
+      use_packed = 'packed_npy_inputs' in getattr(captured, 'expected_names', [])
+    else:
+      use_packed = True
+
    if 'model' in metadata:
      model_metadata = metadata['model']
      self.vision_output_slices = model_metadata['output_slices']
      self.policy_output_slices = {}
      self._policy_slices_list = []
      self._combined_model_type = 'supercombo'
-      self._vision_input_names = [k for k in model_metadata['input_shapes'] if 'img' in k]
+      self._vision_input_names = [key for key in model_metadata['input_shapes'] if 'img' in key]
      from openpilot.sunnypilot.modeld_v2.compile_modeld import make_supercombo_input_queues
      frame_skip = derive_frame_skip({}, model_metadata['input_shapes'])
-      self.input_queues, self.numpy_inputs = make_supercombo_input_queues(model_metadata['input_shapes'], frame_skip, device=self.DEV)
+      self.input_queues, self.numpy_inputs = make_supercombo_input_queues(model_metadata['input_shapes'],
+                                                                          frame_skip, device=self.QUEUE_DEV, use_packed=use_packed)
    else:
      vision_metadata = metadata['vision']
      policy_keys = [k for k in metadata if k != 'vision']
@@ -139,11 +156,12 @@ class ModelState(ModelStateBase):
      policy_input_shapes = first_policy_metadata['input_shapes']
      self._vision_input_names = [k for k in vision_input_shapes if 'img' in k]
      frame_skip = derive_frame_skip(vision_input_shapes, policy_input_shapes)
-      self.input_queues, self.numpy_inputs = make_split_input_queues(vision_input_shapes, policy_input_shapes, frame_skip, device=self.DEV)
+      self.input_queues, self.numpy_inputs = make_split_input_queues(vision_input_shapes, policy_input_shapes,
+                                                                     frame_skip, device=self.QUEUE_DEV, use_packed=use_packed)

-    from openpilot.sunnypilot.modeld_v2.parse_model_outputs_split import Parser as SplitParser
-    from openpilot.sunnypilot.modeld_v2.parse_model_outputs import Parser as CombinedParser
-    self.parser = SplitParser() if self._combined_model_type != 'supercombo' else CombinedParser()
+    self._desire_key = next(key for key in self.numpy_inputs if key.startswith('desire'))
+    self._road_key = next(key for key in self._vision_input_names if 'big' not in key)
+    self._wide_key = next(key for key in self._vision_input_names if 'big' in key)

    is_20hz = bundle.is20hz if bundle else self._combined_model_type in ('split', 'multi_policy')
    if is_20hz:
@@ -153,20 +171,24 @@ class ModelState(ModelStateBase):
      from openpilot.sunnypilot.modeld_v2.constants import ModelConstants
      self.constants = ModelConstants()

+    if self._combined_model_type != 'supercombo':
+      from openpilot.sunnypilot.modeld_v2.parse_model_outputs_split import Parser as SplitParser
+      self.parser = SplitParser()
+    else:
+      from openpilot.sunnypilot.modeld_v2.parse_model_outputs import Parser as CombinedParser
+      self.parser = CombinedParser()
+
    self.prev_desire = np.zeros(self.constants.DESIRE_LEN, dtype=np.float32)
    self.full_frames: dict = {}
    self._blob_cache: dict = {}
    nv12_info = get_nv12_info(cam_w, cam_h)
    self.frame_buf_params = dict.fromkeys(self._vision_input_names, nv12_info)

-    self._run_policy = jits[(cam_w, cam_h)]['run_policy']
-    self._warp_enqueue = jits[(cam_w, cam_h)]['warp_enqueue']
-    road_name = next(k for k in self._vision_input_names if 'big' not in k)
-    yuv_size = self.frame_buf_params[road_name][3]
+    yuv_size = self.frame_buf_params[self._road_key][3]
    self._warp_enqueue(
      **self.input_queues,
-      frame=Tensor(np.zeros(yuv_size, dtype=np.uint8), device=self.DEV).contiguous().realize(),
-      big_frame=Tensor(np.zeros(yuv_size, dtype=np.uint8), device=self.DEV).contiguous().realize())
+      frame=Tensor(np.zeros(yuv_size, dtype=np.uint8), device=self.WARP_DEV).contiguous().realize(),
+      big_frame=Tensor(np.zeros(yuv_size, dtype=np.uint8), device=self.WARP_DEV).contiguous().realize())


  @property
@@ -179,30 +201,28 @@ class ModelState(ModelStateBase):

  @property
  def desire_key(self) -> str:
-    return next(k for k in self.numpy_inputs if k.startswith('desire'))
+    return self._desire_key

  def run(self, bufs: dict[str, VisionBuf], transforms: dict[str, np.ndarray],
                inputs: dict[str, np.ndarray], prepare_only: bool) -> dict[str, np.ndarray] | None:
-    from tinygrad.tensor import Tensor
-
    for key in bufs.keys():
      ptr = np.frombuffer(bufs[key].data, dtype=np.uint8).ctypes.data
      yuv_size = self.frame_buf_params[key][3]
      cache_key = (key, ptr)
      if cache_key not in self._blob_cache:
-        self._blob_cache[cache_key] = Tensor.from_blob(ptr, (yuv_size,), dtype='uint8', device=self.DEV)
+        self._blob_cache[cache_key] = Tensor.from_blob(ptr, (yuv_size,), dtype='uint8', device=self.WARP_DEV)
      self.full_frames[key] = self._blob_cache[cache_key]

    desire_key = self.desire_key
    inputs[desire_key][0] = 0
    self.numpy_inputs[desire_key][:] = np.where(inputs[desire_key] - self.prev_desire > .99, inputs[desire_key], 0)
    self.prev_desire[:] = inputs[desire_key]
-    for key in ('traffic_convention', 'lateral_control_params'):
+    for key in ('traffic_convention', 'lateral_control_params', 'action_t'):
      if key in self.numpy_inputs and key in inputs:
        self.numpy_inputs[key][:] = inputs[key]

-    road_key = next(n for n in bufs if 'big' not in n)
-    wide_key = next(n for n in bufs if 'big' in n)
+    road_key = self._road_key
+    wide_key = self._wide_key
    self.numpy_inputs['tfm'][:, :] = transforms[road_key].reshape(3, 3)
    self.numpy_inputs['big_tfm'][:, :] = transforms[wide_key].reshape(3, 3)

@@ -216,17 +236,26 @@ class ModelState(ModelStateBase):
      model_output = raw_outputs.numpy().flatten()
      sliced = {k: model_output[np.newaxis, v] for k, v in self.vision_output_slices.items()}
      outputs = self.parser.parse_outputs(sliced)
+      if 'prev_feat' in self.numpy_inputs:
+        self.numpy_inputs['prev_feat'][:] = model_output[self.vision_output_slices['hidden_state']]
    else:
      vision_output = raw_outputs[0].numpy().flatten()
      vision_sliced = {k: vision_output[np.newaxis, v] for k, v in self.vision_output_slices.items()}
      outputs = self.parser.parse_vision_outputs(vision_sliced)

+      if 'prev_feat' in self.numpy_inputs and 'hidden_state' in self.vision_output_slices:
+        self.numpy_inputs['prev_feat'][:] = vision_output[self.vision_output_slices['hidden_state']]
+
      for i, policy_slices in enumerate(self._policy_slices_list):
        policy_output = raw_outputs[i + 1].numpy().flatten()
        policy_sliced = {k: policy_output[np.newaxis, v] for k, v in policy_slices.items()}
        parsed = self.parser.parse_policy_outputs(policy_sliced)
-        if 'off' in self._policy_keys[i] and self._has_on_policy:
+        if ('off' in self._policy_keys[i]
+          and self._has_on_policy
+          and any('plan' in self._policy_slices_list[j] for j, k in enumerate(self._policy_keys) if 'on' in k.lower())):
+
          parsed.pop('plan', None)
+
        outputs.update(parsed)

      if 'planplus' in outputs and 'plan' in outputs:
@@ -237,17 +266,30 @@ class ModelState(ModelStateBase):
      buf[0, :-1] = buf[0, 1:]
      buf[0, -1, :] = outputs['desired_curvature'][0, :] if not self.mlsim else 0

+    # TODO-SP: This is a hack to prevent GPU corruption by calculating in CPU space, it can be removed on next recompile
+    if 'prev_feat' not in self.numpy_inputs and 'feat_q' in self.input_queues:
+      feat_val = self.input_queues['feat_q'].numpy()
+      self.input_queues['feat_q'].assign(feat_val).realize()
+
    return outputs

  def get_action_from_model(self, model_output: dict[str, np.ndarray], prev_action: log.ModelDataV2.Action,
                            lat_action_t: float, long_action_t: float, v_ego: float) -> log.ModelDataV2.Action:
-    plan = model_output['plan'][0]
-    desired_accel, should_stop = get_accel_from_plan(plan[:, Plan.VELOCITY][:, 0], plan[:, Plan.ACCELERATION][:, 0], self.constants.T_IDXS,
-                                                     action_t=long_action_t)
+    if 'action' not in model_output:
+      plan = model_output['plan'][0]
+      desired_accel, should_stop = get_accel_from_plan(plan[:, Plan.VELOCITY][:, 0], plan[:, Plan.ACCELERATION][:, 0], self.constants.T_IDXS,
+                                                       action_t=long_action_t)
+
+      curvature_plan = (plan + (self.PLANPLUS_CONTROL - 1.0) * model_output['planplus'][0]
+                        if 'planplus' in model_output and self.PLANPLUS_CONTROL != 1.0 else plan)
+      desired_curvature = get_curvature_from_output(model_output, curvature_plan, v_ego, lat_action_t, self.mlsim)
+    else:
+      desired_accel = model_output['action'][0, 1]
+      desired_curvature = model_output['action'][0, 0] / (max(1.0, v_ego))**2
+      should_stop = (v_ego < 0.3 and desired_accel < 0.1)
+
    desired_accel = smooth_value(desired_accel, prev_action.desiredAcceleration, self.LONG_SMOOTH_SECONDS)

-    curvature_plan = plan + (self.PLANPLUS_CONTROL - 1.0) * model_output['planplus'][0] if 'planplus' in model_output and self.PLANPLUS_CONTROL != 1.0 else plan
-    desired_curvature = get_curvature_from_output(model_output, curvature_plan, v_ego, lat_action_t, self.mlsim)
    if self.generation is not None and self.generation >= 10: # smooth curvature for post FOF models
      if v_ego > self.MIN_LAT_CONTROL_SPEED:
        desired_curvature = smooth_value(desired_curvature, prev_action.desiredCurvature, self.LAT_SMOOTH_SECONDS)
@@ -325,6 +367,7 @@ def main(demo=False):
  prev_action = log.ModelDataV2.Action()

  DH = DesireHelper()
+  RELC = RoadEdgeLaneChangeController(DH)
  meta_constants = load_meta_constants()

  while True:
@@ -400,6 +443,12 @@ def main(demo=False):

    bufs = {name: buf_extra if 'big' in name else buf_main for name in model.vision_input_names}
    transforms = {name: model_transform_extra if 'big' in name else model_transform_main for name in model.vision_input_names}
+
+    frame_delay = DT_MDL # compensate for time passed since the frame was captured: current_time - timestamp_eof is 50ms on average
+    action_delay = DT_MDL / 2 # middle of the interval between model output (current state) and next frame (expected state)
+    lat_action_t = lat_delay + frame_delay + action_delay
+    long_action_t = long_delay + frame_delay + action_delay
+
    inputs:dict[str, np.ndarray] = {
      model.desire_key: vec_desire,
      'traffic_convention': traffic_convention,
@@ -408,6 +457,9 @@ def main(demo=False):
    if 'lateral_control_params' in model.numpy_inputs:
      inputs['lateral_control_params'] = np.array([v_ego, lat_delay], dtype=np.float32)

+    if 'action_t' in model.numpy_inputs:
+      inputs['action_t'] = np.array([lat_action_t, long_action_t], dtype=np.float32)
+
    mt1 = time.perf_counter()
    model_output = model.run(bufs, transforms, inputs, prepare_only)
    mt2 = time.perf_counter()
@@ -419,7 +471,7 @@ def main(demo=False):
      posenet_send = messaging.new_message('cameraOdometry')
      mdv2sp_send = messaging.new_message('modelDataV2SP')

-      action = model.get_action_from_model(model_output, prev_action, lat_delay + DT_MDL, long_delay + DT_MDL, v_ego)
+      action = model.get_action_from_model(model_output, prev_action, lat_action_t, long_action_t, v_ego)
      prev_action = action
      fill_model_msg(drivingdata_send, modelv2_send, model_output, action,
                     publish_state, meta_main.frame_id, meta_extra.frame_id, frame_id,
@@ -429,7 +481,10 @@ def main(demo=False):
      l_lane_change_prob = desire_state[log.Desire.laneChangeLeft]
      r_lane_change_prob = desire_state[log.Desire.laneChangeRight]
      lane_change_prob = l_lane_change_prob + r_lane_change_prob
-      DH.update(sm['carState'], sm['carControl'].latActive, lane_change_prob)
+      RELC.update(modelv2_send.modelV2.roadEdgeStds, modelv2_send.modelV2.laneLineProbs, v_ego)
+      mdv2sp_send.modelDataV2SP.leftLaneChangeEdgeBlock = RELC.left_edge_detected
+      mdv2sp_send.modelDataV2SP.rightLaneChangeEdgeBlock = RELC.right_edge_detected
+      DH.update(sm['carState'], sm['carControl'].latActive, lane_change_prob, RELC.left_edge_detected, RELC.right_edge_detected)
      modelv2_send.modelV2.meta.laneChangeState = DH.lane_change_state
      modelv2_send.modelV2.meta.laneChangeDirection = DH.lane_change_direction
      mdv2sp_send.modelDataV2SP.laneTurnDirection = DH.lane_turn_direction
@@ -1,13 +1,16 @@
 import numpy as np
 from openpilot.sunnypilot.modeld_v2.constants import ModelConstants

+
 def safe_exp(x, out=None):
  # -11 is around 10**14, more causes float16 overflow
  return np.exp(np.clip(x, -np.inf, 11), out=out)

+
 def sigmoid(x):
  return 1. / (1. + safe_exp(-x))

+
 def softmax(x, axis=-1):
  x -= np.max(x, axis=axis, keepdims=True)
  if x.dtype == np.float32 or x.dtype == np.float64:
@@ -17,6 +20,19 @@ def softmax(x, axis=-1):
  x /= np.sum(x, axis=axis, keepdims=True)
  return x

+
+def _infer_mhp(slice_size: int, prod_out_shape: int, max_in_n: int = 16, max_out_n: int = 6) -> tuple[int, int]:
+  for out_n in range(max_out_n + 1):
+    per = 2 * prod_out_shape + out_n
+    if per <= 0:
+      continue
+    if slice_size % per == 0:
+      in_n = slice_size // per
+      if 1 <= in_n <= max_in_n:
+        return in_n, out_n
+  return 1, 0  # single hypothesis, no weights — matches a non-MDN output
+
+
 class Parser:
  def __init__(self, ignore_missing=False):
    self.ignore_missing = ignore_missing
@@ -40,17 +56,22 @@ class Parser:
    raw = outs[name]
    outs[name] = sigmoid(raw)

-  def parse_mdn(self, name, outs, in_N=0, out_N=1, out_shape=None):
+  def parse_mdn(self, name, outs, out_shape, in_N=0, out_N=0):
    if self.check_missing(outs, name):
      return
    raw = outs[name]
-    raw = raw.reshape((raw.shape[0], max(in_N, 1), -1))
+
+    if in_N == 0 and out_N == 0:
+      prod = int(np.prod(out_shape))
+      in_N, out_N = _infer_mhp(raw.shape[1], prod)
+
+    raw = raw.reshape((raw.shape[0], in_N, -1))

    n_values = (raw.shape[2] - out_N)//2
    pred_mu = raw[:,:,:n_values]
    pred_std = safe_exp(raw[:,:,n_values: 2*n_values])

-    if in_N > 1:
+    if in_N > 1 and out_N > 0:
      weights = np.zeros((raw.shape[0], in_N, out_N), dtype=raw.dtype)
      for i in range(out_N):
        weights[:,:,i - out_N] = softmax(raw[:,:,i - out_N], axis=-1)
@@ -61,7 +82,6 @@ class Parser:
          weights[fidx] = weights[fidx][idxs]
          pred_mu[fidx] = pred_mu[fidx][idxs]
          pred_std[fidx] = pred_std[fidx][idxs]
-      assert out_shape is not None
      full_shape = tuple([raw.shape[0], in_N] + list(out_shape))
      outs[name + '_weights'] = weights
      outs[name + '_hypotheses'] = pred_mu.reshape(full_shape)
@@ -74,37 +94,43 @@ class Parser:
          idxs = np.argsort(weights[fidx,:,hidx])[::-1]
          pred_mu_final[fidx, hidx] = pred_mu[fidx, idxs[0]]
          pred_std_final[fidx, hidx] = pred_std[fidx, idxs[0]]
+    elif in_N > 1 and out_N == 0:
+      # MHP without weights: keep every hypothesis intact, surface them as
+      # ``*_hypotheses`` and propagate the full multi-hypothesis tensor.
+      full_shape = tuple([raw.shape[0], in_N] + list(out_shape))
+      outs[name + '_hypotheses'] = pred_mu.reshape(full_shape)
+      outs[name + '_stds_hypotheses'] = pred_std.reshape(full_shape)
+      pred_mu_final = pred_mu
+      pred_std_final = pred_std
    else:
      pred_mu_final = pred_mu
      pred_std_final = pred_std

-    if out_N > 1:
-      assert out_shape is not None
-      final_shape = tuple([raw.shape[0], out_N] + list(out_shape))
+    if out_N > 1 or (in_N > 1 and out_N == 0):
+      n_selections = out_N if out_N > 1 else in_N
+      final_shape = tuple([raw.shape[0], n_selections] + list(out_shape))
    else:
-      assert out_shape is not None
      final_shape = tuple([raw.shape[0],] + list(out_shape))
    outs[name] = pred_mu_final.reshape(final_shape)
    outs[name + '_stds'] = pred_std_final.reshape(final_shape)

  def parse_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
-    self.parse_mdn('plan', outs, in_N=ModelConstants.PLAN_MHP_N, out_N=ModelConstants.PLAN_MHP_SELECTION,
-                   out_shape=(ModelConstants.IDX_N,ModelConstants.PLAN_WIDTH))
-    self.parse_mdn('lane_lines', outs, in_N=0, out_N=0, out_shape=(ModelConstants.NUM_LANE_LINES,ModelConstants.IDX_N,ModelConstants.LANE_LINES_WIDTH))
-    self.parse_mdn('road_edges', outs, in_N=0, out_N=0, out_shape=(ModelConstants.NUM_ROAD_EDGES,ModelConstants.IDX_N,ModelConstants.LANE_LINES_WIDTH))
-    self.parse_mdn('pose', outs, in_N=0, out_N=0, out_shape=(ModelConstants.POSE_WIDTH,))
-    self.parse_mdn('road_transform', outs, in_N=0, out_N=0, out_shape=(ModelConstants.POSE_WIDTH,))
+    # supercombo (4955 / 102) and newer variants (e.g. 990 / 144).
+    self.parse_mdn('plan',   outs, out_shape=(ModelConstants.IDX_N, ModelConstants.PLAN_WIDTH))
+    self.parse_mdn('lane_lines', outs, out_shape=(ModelConstants.NUM_LANE_LINES, ModelConstants.IDX_N, ModelConstants.LANE_LINES_WIDTH))
+    self.parse_mdn('road_edges', outs, out_shape=(ModelConstants.NUM_ROAD_EDGES, ModelConstants.IDX_N, ModelConstants.LANE_LINES_WIDTH))
+    self.parse_mdn('pose',         outs, out_shape=(ModelConstants.POSE_WIDTH,))
+    self.parse_mdn('road_transform', outs, out_shape=(ModelConstants.POSE_WIDTH,))
    if 'sim_pose' in outs:
-      self.parse_mdn('sim_pose', outs, in_N=0, out_N=0, out_shape=(ModelConstants.POSE_WIDTH,))
-    self.parse_mdn('wide_from_device_euler', outs, in_N=0, out_N=0, out_shape=(ModelConstants.WIDE_FROM_DEVICE_WIDTH,))
-    self.parse_mdn('lead', outs, in_N=ModelConstants.LEAD_MHP_N, out_N=ModelConstants.LEAD_MHP_SELECTION,
-                   out_shape=(ModelConstants.LEAD_TRAJ_LEN,ModelConstants.LEAD_WIDTH))
+      self.parse_mdn('sim_pose', outs, out_shape=(ModelConstants.POSE_WIDTH,))
+    self.parse_mdn('wide_from_device_euler', outs, out_shape=(ModelConstants.WIDE_FROM_DEVICE_WIDTH,))
+    self.parse_mdn('lead', outs, out_shape=(ModelConstants.LEAD_TRAJ_LEN, ModelConstants.LEAD_WIDTH))
    if 'lat_planner_solution' in outs:
-      self.parse_mdn('lat_planner_solution', outs, in_N=0, out_N=0, out_shape=(ModelConstants.IDX_N,ModelConstants.LAT_PLANNER_SOLUTION_WIDTH))
+      self.parse_mdn('lat_planner_solution', outs, out_shape=(ModelConstants.IDX_N, ModelConstants.LAT_PLANNER_SOLUTION_WIDTH))
    if 'desired_curvature' in outs:
-      self.parse_mdn('desired_curvature', outs, in_N=0, out_N=0, out_shape=(ModelConstants.DESIRED_CURV_WIDTH,))
+      self.parse_mdn('desired_curvature', outs, out_shape=(ModelConstants.DESIRED_CURV_WIDTH,))
    for k in ['lead_prob', 'lane_lines_prob', 'meta']:
      self.parse_binary_crossentropy(k, outs)
    self.parse_categorical_crossentropy('desire_state', outs, out_shape=(ModelConstants.DESIRE_PRED_WIDTH,))
-    self.parse_categorical_crossentropy('desire_pred', outs, out_shape=(ModelConstants.DESIRE_PRED_LEN,ModelConstants.DESIRE_PRED_WIDTH))
+    self.parse_categorical_crossentropy('desire_pred',  outs, out_shape=(ModelConstants.DESIRE_PRED_LEN, ModelConstants.DESIRE_PRED_WIDTH))
    return outs
@@ -123,7 +123,7 @@ class Parser:
      self.parse_categorical_crossentropy('desire_state', outs, out_shape=(SplitModelConstants.DESIRE_PRED_WIDTH,))
    if 'lane_lines' in outs:
      self.parse_mdn('lane_lines', outs, in_N=0, out_N=0,
-                    out_shape=(SplitModelConstants.NUM_LANE_LINES,SplitModelConstants.IDX_N,SplitModelConstants.LANE_LINES_WIDTH))
+                     out_shape=(SplitModelConstants.NUM_LANE_LINES,SplitModelConstants.IDX_N,SplitModelConstants.LANE_LINES_WIDTH))
    if 'lane_lines_prob' in outs:
      self.parse_binary_crossentropy('lane_lines_prob', outs)
    if 'lead_prob' in outs:
@@ -134,9 +134,11 @@ class Parser:
      self.parse_binary_crossentropy('meta', outs)
    if 'road_edges' in outs:
      self.parse_mdn('road_edges', outs, in_N=0, out_N=0,
-                    out_shape=(SplitModelConstants.NUM_ROAD_EDGES,SplitModelConstants.IDX_N,SplitModelConstants.LANE_LINES_WIDTH))
+                     out_shape=(SplitModelConstants.NUM_ROAD_EDGES,SplitModelConstants.IDX_N,SplitModelConstants.LANE_LINES_WIDTH))
    if 'sim_pose' in outs:
      self.parse_mdn('sim_pose', outs, in_N=0, out_N=0, out_shape=(SplitModelConstants.POSE_WIDTH,))
+    if 'action' in outs:
+      self.parse_mdn('action', outs, in_N=0, out_N=0, out_shape=(SplitModelConstants.ACTION_WIDTH,))

  def parse_vision_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
    self.parse_mdn('pose', outs, in_N=0, out_N=0, out_shape=(SplitModelConstants.POSE_WIDTH,))
@@ -67,22 +67,12 @@ class TestStockEquivalence:
    state = model_state_factory(ARCHETYPES['vision_policy_split'])

    frame_skip = derive_frame_skip(SPLIT_VISION_INPUT_SHAPES, SPLIT_POLICY_INPUT_SHAPES)
-    # action_t is a deep-model prerequisite the SP loader doesn't provide yet; see skip_keys below
    stock_shapes = {**SPLIT_VISION_INPUT_SHAPES, **SPLIT_POLICY_INPUT_SHAPES, 'action_t': (1, 2)}
    stock_queues, stock_npy = make_input_queues(stock_shapes, frame_skip, device='NPY')

-    # TODO-SP: remove action_t skip once SP adds prerequisite for deep models (action_t input queue)
-    # prev_feat is a stock QCOM corruption workaround handled inside the SP loader's JIT path
-    skip_keys = {'action_t', 'prev_feat'}
-    # stock packs the per-key policy inputs into packed_npy_inputs; the npy views carry the individual keys
-    stock_queue_keys = set(stock_queues.keys())
-    if 'packed_npy_inputs' in stock_queue_keys:
-      stock_queue_keys.remove('packed_npy_inputs')
-      stock_queue_keys |= set(stock_npy.keys())
-    assert set(state.input_queues.keys()) == stock_queue_keys - skip_keys, \
-      f"Queue keys differ: v2={set(state.input_queues.keys())}, stock={stock_queue_keys}"
-    assert set(state.numpy_inputs.keys()) == set(stock_npy.keys()) - skip_keys, \
-      f"Npy keys differ: v2={set(state.numpy_inputs.keys())}, stock={set(stock_npy.keys())}"
+    assert set(state.input_queues.keys()) == set(stock_queues.keys())
+    assert {'desire', 'traffic_convention'} <= set(state.numpy_inputs.keys())
+    assert set(state.numpy_inputs.keys()) == set(stock_npy.keys()) - {'action_t', 'prev_feat'}

  def test_split_queue_keys_work_with_desire_key(self, model_state_factory):
    from openpilot.sunnypilot.modeld_v2.compile_modeld import derive_frame_skip, make_split_input_queues
@@ -1,103 +0,0 @@
-import os
-os.environ['DEV'] = 'CPU'
-import pytest
-import numpy as np
-from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
-from openpilot.sunnypilot.modeld_v2.warp import CAMERA_CONFIGS
-from openpilot.sunnypilot.modeld_v2.warp import Warp, MODEL_W, MODEL_H
-
-VISION_NAME_PAIRS = [  # needed to account for supercombos input_imgs
-  ('img', 'big_img'),
-  ('input_imgs', 'big_input_imgs'),
-]
-
-
-class MockVisionBuf:
-  def __init__(self, w, h):
-    self.width = w
-    self.height = h
-    _, _, _, yuv_size = get_nv12_info(w, h)
-    self.data = np.zeros(yuv_size, dtype=np.uint8)
-
-
-@pytest.mark.parametrize("buffer_length", [2, 5])
-def test_warp_initialization(buffer_length):
-  warp = Warp(buffer_length)
-  assert warp.buffer_length == buffer_length
-  assert warp.img_buffer_shape == (buffer_length * 6, MODEL_H // 2, MODEL_W // 2)
-
-
-@pytest.mark.parametrize("buffer_length", [2, 5])
-@pytest.mark.parametrize("cam_w, cam_h", CAMERA_CONFIGS)
-@pytest.mark.parametrize("road, wide", VISION_NAME_PAIRS)
-def test_warp_process(buffer_length, cam_w, cam_h, road, wide):
-  warp = Warp(buffer_length)
-  mock_buf = MockVisionBuf(cam_w, cam_h)
-  transform = np.eye(3, dtype=np.float32).flatten()
-  bufs = {road: mock_buf, wide: mock_buf}
-  transforms = {road: transform, wide: transform}
-
-  out = warp.process(bufs, transforms)
-  assert isinstance(out, dict)
-  assert road in out and wide in out
-  assert out[road].shape == (1, 12, MODEL_H // 2, MODEL_W // 2)
-  assert out[wide].shape == (1, 12, MODEL_H // 2, MODEL_W // 2)
-
-  key = (cam_w, cam_h)
-  assert key in warp.jit_cache
-
-  out2 = warp.process(bufs, transforms)
-  assert out2[road].shape == out[road].shape
-
-
-@pytest.mark.parametrize("road, wide", VISION_NAME_PAIRS)
-def test_warp_buffer_shift(road, wide):
-  warp = Warp(2)
-  cam_w, cam_h = CAMERA_CONFIGS[1]
-  transform = np.eye(3, dtype=np.float32).flatten()
-
-  buf1 = MockVisionBuf(cam_w, cam_h)
-  buf1.data[0] = 255
-  bufs1 = {road: buf1, wide: buf1}
-  transforms = {road: transform, wide: transform}
-  out1 = warp.process(bufs1, transforms)
-  road1 = out1[road].numpy().copy()
-
-  buf2 = MockVisionBuf(cam_w, cam_h)
-  buf2.data[0] = 128
-  bufs2 = {road: buf2, wide: buf2}
-  out2 = warp.process(bufs2, transforms)
-  assert not np.array_equal(road1, out2[road].numpy())
-
-
-@pytest.mark.parametrize("buffer_length", [2, 5])
-@pytest.mark.parametrize("road, wide", VISION_NAME_PAIRS)
-def test_warp_buffer_accumulation(buffer_length, road, wide):
-  warp = Warp(buffer_length)
-  cam_w, cam_h = CAMERA_CONFIGS[0]
-  transform = np.eye(3, dtype=np.float32).flatten()
-  transforms = {road: transform, wide: transform}
-  outputs = []
-
-  for i in range(buffer_length + 1):
-    buf = MockVisionBuf(cam_w, cam_h)
-    buf.data[:] = i * 10
-    out = warp.process({road: buf, wide: buf}, transforms)
-    outputs.append(out[road].numpy().copy())
-
-  assert warp.full_buffers['img'].shape == (buffer_length * 6, MODEL_H // 2, MODEL_W // 2)
-  for i in range(1, len(outputs)):
-    assert not np.array_equal(outputs[i - 1], outputs[i])
-
-
-def test_warp_different_cameras_same_instance():
-  warp = Warp(2)
-  transform = np.eye(3, dtype=np.float32).flatten()
-
-  buf1 = MockVisionBuf(*CAMERA_CONFIGS[0])
-  warp.process({'img': buf1, 'big_img': buf1}, {'img': transform, 'big_img': transform})
-  assert len(warp.jit_cache) == 1
-
-  buf2 = MockVisionBuf(*CAMERA_CONFIGS[1])
-  warp.process({'img': buf2, 'big_img': buf2}, {'img': transform, 'big_img': transform})
-  assert len(warp.jit_cache) == 2
@@ -1,171 +0,0 @@
-import pickle
-import time
-import numpy as np
-from pathlib import Path
-from tinygrad.tensor import Tensor
-from tinygrad.engine.jit import TinyJit
-from tinygrad.device import Device
-
-from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
-from openpilot.common.transformations.model import MEDMODEL_INPUT_SIZE
-from openpilot.common.transformations.camera import _ar_ox_fisheye, _os_fisheye
-from openpilot.selfdrive.modeld.compile_modeld import NV12Frame, make_frame_prepare as _make_frame_prepare
-
-CAMERA_CONFIGS = [
-  (_ar_ox_fisheye.width, _ar_ox_fisheye.height),
-  (_os_fisheye.width, _os_fisheye.height),
-]
-
-
-def make_frame_prepare(cam_w, cam_h, model_w, model_h):
-  nv12 = NV12Frame(cam_w, cam_h, *get_nv12_info(cam_w, cam_h))
-  return _make_frame_prepare(nv12, model_w, model_h)
-
-
-def warp_pkl_path(w, h):
-  from openpilot.selfdrive.modeld.helpers import MODELS_DIR
-  return MODELS_DIR / f'warp_{w}x{h}_tinygrad.pkl'
-
-
-def make_update_img_input(frame_prepare, model_w, model_h):
-  def update_img_input_tinygrad(tensor, frame, M_inv):
-    M_inv = M_inv.to(Device.DEFAULT)
-    new_img = frame_prepare(frame, M_inv)
-    tensor.assign(tensor[6:].cat(new_img, dim=0).contiguous())
-    return Tensor.cat(tensor[:6], tensor[-6:], dim=0).contiguous().reshape(1, 12, model_h//2, model_w//2)
-  return update_img_input_tinygrad
-
-
-def make_update_both_imgs(frame_prepare, model_w, model_h):
-  update_img = make_update_img_input(frame_prepare, model_w, model_h)
-  def update_both_imgs_tinygrad(calib_img_buffer, new_img, M_inv,
-                                calib_big_img_buffer, new_big_img, M_inv_big):
-    calib_img_pair = update_img(calib_img_buffer, new_img, M_inv)
-    calib_big_img_pair = update_img(calib_big_img_buffer, new_big_img, M_inv_big)
-    return calib_img_pair, calib_big_img_pair
-  return update_both_imgs_tinygrad
-
-MODELS_DIR = Path(__file__).parent / 'models'
-MODEL_W, MODEL_H = MEDMODEL_INPUT_SIZE
-UPSTREAM_BUFFER_LENGTH = 5
-
-
-def v2_warp_pkl_path(cam_w, cam_h, buffer_length):
-  return MODELS_DIR / f'warp_{cam_w}x{cam_h}_b{buffer_length}_tinygrad.pkl'
-
-
-def compile_v2_warp(cam_w, cam_h, buffer_length):
-  _, _, _, yuv_size = get_nv12_info(cam_w, cam_h)
-  img_buffer_shape = (buffer_length * 6, MODEL_H // 2, MODEL_W // 2)
-
-  print(f"Compiling v2 warp for {cam_w}x{cam_h} buffer_length={buffer_length}...")
-
-  frame_prepare = make_frame_prepare(cam_w, cam_h, MODEL_W, MODEL_H)
-  update_both_imgs = make_update_both_imgs(frame_prepare, MODEL_W, MODEL_H)
-  update_img_jit = TinyJit(update_both_imgs, prune=True)
-
-  full_buffer = Tensor.zeros(img_buffer_shape, dtype='uint8').contiguous().realize()
-  big_full_buffer = Tensor.zeros(img_buffer_shape, dtype='uint8').contiguous().realize()
-  new_frame_np = np.random.default_rng(0).integers(0, 256, yuv_size, dtype=np.uint8)
-  new_big_frame_np = np.random.default_rng(1).integers(0, 256, yuv_size, dtype=np.uint8)
-  for i in range(10):
-    img_inputs = [full_buffer,
-                  Tensor.from_blob(new_frame_np.ctypes.data, (yuv_size,), dtype='uint8').realize(),
-                  Tensor(Tensor.randn(3, 3).mul(8).realize().numpy(), device='NPY')]
-    big_img_inputs = [big_full_buffer,
-                      Tensor.from_blob(new_big_frame_np.ctypes.data, (yuv_size,), dtype='uint8').realize(),
-                      Tensor(Tensor.randn(3, 3).mul(8).realize().numpy(), device='NPY')]
-    inputs = img_inputs + big_img_inputs
-    Device.default.synchronize()
-
-    st = time.perf_counter()
-    _ = update_img_jit(*inputs)
-    mt = time.perf_counter()
-    Device.default.synchronize()
-    et = time.perf_counter()
-    print(f"  [{i+1}/10] enqueue {(mt-st)*1e3:6.2f} ms -- total {(et-st)*1e3:6.2f} ms")
-
-  pkl_path = v2_warp_pkl_path(cam_w, cam_h, buffer_length)
-  with open(pkl_path, "wb") as f:
-    pickle.dump(update_img_jit, f)
-  print(f"  Saved to {pkl_path}")
-
-  jit = pickle.load(open(pkl_path, "rb"))
-  verify_frame = np.random.default_rng(0).integers(0, 256, yuv_size, dtype=np.uint8)
-  verify_big_frame = np.random.default_rng(1).integers(0, 256, yuv_size, dtype=np.uint8)
-  fresh_inputs = [
-    Tensor.zeros(img_buffer_shape, dtype='uint8').contiguous().realize(),
-    Tensor.from_blob(verify_frame.ctypes.data, (yuv_size,), dtype='uint8').realize(),
-    Tensor(Tensor.randn(3, 3).mul(8).realize().numpy(), device='NPY'),
-    Tensor.zeros(img_buffer_shape, dtype='uint8').contiguous().realize(),
-    Tensor.from_blob(verify_big_frame.ctypes.data, (yuv_size,), dtype='uint8').realize(),
-    Tensor(Tensor.randn(3, 3).mul(8).realize().numpy(), device='NPY'),
-  ]
-  jit(*fresh_inputs)
-
-
-class Warp:
-  def __init__(self, buffer_length=2):
-    self.buffer_length = buffer_length
-    self.img_buffer_shape = (buffer_length * 6, MODEL_H // 2, MODEL_W // 2)
-
-    self.jit_cache = {}
-    self.full_buffers = {k: Tensor.zeros(self.img_buffer_shape, dtype='uint8').contiguous().realize() for k in ['img', 'big_img']}
-    self._blob_cache: dict[int, Tensor] = {}
-    self._nv12_cache: dict[tuple[int, int], int] = {}
-    self.transforms_np = {k: np.zeros((3, 3), dtype=np.float32) for k in ['img', 'big_img']}
-    self.transforms = {k: Tensor(v, device='NPY').realize() for k, v in self.transforms_np.items()}
-
-  def process(self, bufs, transforms):
-    if not bufs:
-      return {}
-    road = next(n for n in bufs if 'big' not in n)
-    wide = next(n for n in bufs if 'big' in n)
-    cam_w, cam_h = bufs[road].width, bufs[road].height
-    key = (cam_w, cam_h)
-
-    if key not in self.jit_cache:
-      v2_pkl = v2_warp_pkl_path(cam_w, cam_h, self.buffer_length)
-      if v2_pkl.exists():
-        with open(v2_pkl, 'rb') as f:
-          self.jit_cache[key] = pickle.load(f)
-      elif self.buffer_length == UPSTREAM_BUFFER_LENGTH:
-        upstream_pkl = warp_pkl_path(cam_w, cam_h)
-        if upstream_pkl.exists():
-          with open(upstream_pkl, 'rb') as f:
-            self.jit_cache[key] = pickle.load(f)
-      if key not in self.jit_cache:
-        frame_prepare = make_frame_prepare(cam_w, cam_h, MODEL_W, MODEL_H)
-        update_both_imgs = make_update_both_imgs(frame_prepare, MODEL_W, MODEL_H)
-        self.jit_cache[key] = TinyJit(update_both_imgs, prune=True)
-
-    if key not in self._nv12_cache:
-      self._nv12_cache[key] = get_nv12_info(cam_w, cam_h)[3]
-    yuv_size = self._nv12_cache[key]
-
-    road_ptr = bufs[road].data.ctypes.data
-    wide_ptr = bufs[wide].data.ctypes.data
-    if road_ptr not in self._blob_cache:
-      self._blob_cache[road_ptr] = Tensor.from_blob(road_ptr, (yuv_size,), dtype='uint8')
-    if wide_ptr not in self._blob_cache:
-      self._blob_cache[wide_ptr] = Tensor.from_blob(wide_ptr, (yuv_size,), dtype='uint8')
-    road_blob = self._blob_cache[road_ptr]
-    wide_blob = self._blob_cache[wide_ptr] if wide_ptr != road_ptr else Tensor.from_blob(wide_ptr, (yuv_size,), dtype='uint8')
-    np.copyto(self.transforms_np['img'], transforms[road].reshape(3, 3))
-    np.copyto(self.transforms_np['big_img'], transforms[wide].reshape(3, 3))
-
-    Device.default.synchronize()
-    res = self.jit_cache[key](
-      self.full_buffers['img'], road_blob, self.transforms['img'],
-      self.full_buffers['big_img'], wide_blob, self.transforms['big_img'],
-    )
-    out_road = res[0].realize()
-    out_wide = res[1].realize()
-
-    return {road: out_road, wide: out_wide}
-
-
-if __name__ == "__main__":
-  for cam_w, cam_h in CAMERA_CONFIGS:
-    for bl in [2, 5]:
-      compile_v2_warp(cam_w, cam_h, bl)
@@ -6,11 +6,12 @@ See the LICENSE.md file in the root directory for more details.
 """

 import time
-
+import os
 import requests
 from requests.exceptions import (SSLError, RequestException, HTTPError)
 from openpilot.common.params import Params
 from openpilot.common.swaglog import cloudlog
+from openpilot.common.hardware.hw import Paths
 from openpilot.sunnypilot.models.helpers import is_bundle_version_compatible

 from openpilot.cereal import custom
@@ -26,11 +27,35 @@ class ModelParser:
    download_uri.sha256 = download_uri_data.get("sha256")
    return download_uri

+  @staticmethod
+  def _parse_chunk(chunk_data) -> custom.ModelManagerSP.Chunk:
+    chunk = custom.ModelManagerSP.Chunk()
+    chunk.fileName = chunk_data.get("file_name")
+    chunk.sha256 = chunk_data.get("sha256")
+    return chunk
+
  @staticmethod
  def _parse_artifact(artifact_data) -> custom.ModelManagerSP.Artifact:
    artifact = custom.ModelManagerSP.Artifact()
    artifact.fileName = artifact_data.get("file_name")
    artifact.downloadUri = ModelParser._parse_download_uri(artifact_data.get("download_uri", {}))
+
+    if "chunks" in artifact_data:
+      artifact.chunks = [ModelParser._parse_chunk(chunk_data) for chunk_data in artifact_data["chunks"]]
+
+      try:
+        model_dir = Paths.model_root()
+        os.makedirs(model_dir, exist_ok=True)
+        manifest_path = os.path.join(model_dir, f"{artifact.fileName}.chunkmanifest")
+        num_chunks = str(len(artifact.chunks))
+
+        if not os.path.exists(manifest_path) or open(manifest_path).read().strip() != num_chunks:
+          with open(manifest_path, "w") as f:
+            f.write(num_chunks)
+          cloudlog.info(f"Wrote chunk manifest for {artifact.fileName}: {num_chunks} chunks")
+      except Exception as e:
+        cloudlog.warning(f"Failed to write chunk manifest for {artifact.fileName}: {e}")
+
    return artifact

  @staticmethod
@@ -39,8 +64,6 @@ class ModelParser:

    model.type = model_data.get("type")
    model.artifact = ModelParser._parse_artifact(model_data.get("artifact", {}))
-    if metadata := model_data.get("metadata"):
-      model.metadata = ModelParser._parse_artifact(metadata)
    return model

  @staticmethod
@@ -116,7 +139,7 @@ class ModelCache:

 class ModelFetcher:
  """Handles fetching and caching of model data from remote source"""
-  MODEL_URL = "https://raw.githubusercontent.com/sunnypilot/sunnypilot-models/refs/heads/gh-pages/docs/driving_models_v17.json"
+  MODEL_URL = "https://raw.githubusercontent.com/sunnypilot/sunnypilot-models/refs/heads/gh-pages/docs/driving_models_v18.json"

  def __init__(self, params: Params):
    self.params = params
@@ -184,4 +207,5 @@ if __name__ == "__main__":
      # Print artifact details
      print(f"Artifact: {model.artifact.fileName}, Download URI: {model.artifact.downloadUri.uri}")
      # Print metadata details
-      print(f"Metadata: {model.metadata.fileName}, Download URI: {model.metadata.downloadUri.uri}")
+      if model.artifact.chunks:
+        print(f"Contains {len(model.artifact.chunks)} chunks.")
@@ -18,7 +18,7 @@ from openpilot.sunnypilot.models.constants import Meta, MetaSimPose, MetaTombRai
 from openpilot.common.hardware.hw import Paths

 # SET ME TO THE EXACT JSON VERSION WE SET IN SUNNYPILOT_MODELS REPO
-REQUIRED_JSON_VERSION = 15
+REQUIRED_JSON_VERSION = 16

 CUSTOM_MODEL_PATH = Paths.model_root()
 METADATA_PATH = Path(__file__).parent / '../models/supercombo_metadata.pkl'
@@ -56,12 +56,20 @@ def is_bundle_version_compatible(bundle: dict) -> bool:

 def _bundle_artifacts(bundle: custom.ModelManagerSP.ModelBundle) -> list[tuple[str, str]]:
  artifacts = []
+  from openpilot.common.file_chunker import get_chunk_name
  for model in getattr(bundle, 'models', []) or []:
-    for artifact in (getattr(model, 'artifact', None), getattr(model, 'metadata', None)):
-      if artifact and getattr(artifact, 'fileName', None) and getattr(artifact, 'downloadUri', None):
-        sha256 = getattr(artifact.downloadUri, 'sha256', None)
-        if sha256:
-          artifacts.append((artifact.fileName, sha256))
+    for artifact in (getattr(model, 'artifact', None),):
+      if artifact and getattr(artifact, 'fileName', None):
+        if len(artifact.chunks) > 0:
+          for i, chunk in enumerate(artifact.chunks):
+            chunk_name = get_chunk_name(artifact.fileName, i, len(artifact.chunks))
+            if getattr(chunk, 'sha256', None):
+              artifacts.append((chunk_name, chunk.sha256))
+        else:
+          if getattr(artifact, 'downloadUri', None):
+            sha256 = getattr(artifact.downloadUri, 'sha256', None)
+            if sha256:
+              artifacts.append((artifact.fileName, sha256))
  return artifacts


@@ -156,8 +164,7 @@ def _get_model():


 def load_metadata():
-  model = _get_model()
-  metadata_path = f"{CUSTOM_MODEL_PATH}/{model.metadata.fileName}" if model else METADATA_PATH
+  metadata_path = METADATA_PATH

  with open(metadata_path, 'rb') as f:
    return pickle.load(f)
@@ -38,11 +38,11 @@ class ModelManagerSP:
    if not self.selected_bundle:
      return
    for model in self.selected_bundle.models:
-      for artifact in (model.artifact, model.metadata):
-        if artifact is not source_artifact and artifact.fileName == source_artifact.fileName:
-          artifact.downloadProgress.status = source_artifact.downloadProgress.status
-          artifact.downloadProgress.progress = source_artifact.downloadProgress.progress
-          artifact.downloadProgress.eta = source_artifact.downloadProgress.eta
+      artifact = model.artifact
+      if artifact is not source_artifact and artifact.fileName == source_artifact.fileName:
+        artifact.downloadProgress.status = source_artifact.downloadProgress.status
+        artifact.downloadProgress.progress = source_artifact.downloadProgress.progress
+        artifact.downloadProgress.eta = source_artifact.downloadProgress.eta

  def _calculate_eta(self, filename: str, progress: float) -> int:
    """Calculate ETA based on elapsed time and current progress"""
@@ -89,20 +89,16 @@ class ModelManagerSP:
        del self._download_start_times[model.fileName]

  async def _download_chunked(self, base_url: str, base_path: str, artifact) -> None:
-    from openpilot.common.file_chunker import get_manifest_path, get_chunk_name
-    manifest_url = get_manifest_path(base_url)
+    from openpilot.common.file_chunker import get_chunk_name, get_manifest_path
+
+    num_chunks = len(artifact.chunks)
+    if num_chunks == 0:
+      raise ValueError("No chunks defined in artifact")
+
    manifest_path = get_manifest_path(base_path)
-
-    async with aiohttp.ClientSession() as session:
-      async with session.get(manifest_url) as resp:
-        if resp.status == 404:
-          raise FileNotFoundError
-        resp.raise_for_status()
-        num_chunks = int((await resp.read()).strip())
-
    self._download_start_times[artifact.fileName] = time.monotonic()

-    for i in range(num_chunks):
+    for i, _ in enumerate(artifact.chunks):
      chunk_url = get_chunk_name(base_url, i, num_chunks)
      chunk_path = get_chunk_name(base_path, i, num_chunks)
      chunk_downloaded = 0
@@ -117,7 +113,7 @@ class ModelManagerSP:
              if self.params.get("ModelManager_DownloadIndex") is None:
                raise Exception("Download cancelled")
              intra = chunk_downloaded / max(chunk_size, 1)
-              progress = min(99, (i + intra) / num_chunks * 100)
+              progress = min(99.0, ((i + intra) / num_chunks) * 100)
              artifact.downloadProgress.status = custom.ModelManagerSP.DownloadStatus.downloading
              artifact.downloadProgress.progress = progress
              artifact.downloadProgress.eta = self._calculate_eta(artifact.fileName, progress)
@@ -140,7 +136,22 @@ class ModelManagerSP:
    full_path = os.path.join(destination_path, filename)

    try:
-      if await verify_file(full_path, expected_hash):
+      is_cached = False
+      if len(artifact.chunks) > 0:
+        from openpilot.common.file_chunker import get_chunk_name
+        chunks_valid = True
+        for i, chunk in enumerate(artifact.chunks):
+          chunk_path = get_chunk_name(full_path, i, len(artifact.chunks))
+          if not await verify_file(chunk_path, chunk.sha256):
+            chunks_valid = False
+            break
+        if chunks_valid and len(artifact.chunks) > 0:
+          is_cached = True
+      else:
+        if await verify_file(full_path, expected_hash):
+          is_cached = True
+
+      if is_cached:
        artifact.downloadProgress.status = custom.ModelManagerSP.DownloadStatus.cached
        artifact.downloadProgress.progress = 100
        artifact.downloadProgress.eta = 0
@@ -148,13 +159,17 @@ class ModelManagerSP:
        self._report_status()
        return

-      try:
+      if len(artifact.chunks) > 0:
        await self._download_chunked(url, full_path, artifact)
-      except (FileNotFoundError, aiohttp.ClientResponseError):
+        from openpilot.common.file_chunker import get_chunk_name
+        for i, chunk in enumerate(artifact.chunks):
+          chunk_path = get_chunk_name(full_path, i, len(artifact.chunks))
+          if not await verify_file(chunk_path, chunk.sha256):
+            raise ValueError(f"Hash validation failed for chunk {i+1} of {filename}")
+      else:
        await self._download_file(url, full_path, artifact)
-
-      if not await verify_file(full_path, expected_hash):
-        raise ValueError(f"Hash validation failed for {filename}")
+        if not await verify_file(full_path, expected_hash):
+          raise ValueError(f"Hash validation failed for {filename}")

      artifact.downloadProgress.status = custom.ModelManagerSP.DownloadStatus.downloaded
      artifact.downloadProgress.progress = 100
@@ -170,18 +185,15 @@ class ModelManagerSP:
      artifact.downloadProgress.status = custom.ModelManagerSP.DownloadStatus.failed
      artifact.downloadProgress.eta = 0
      self._sync_artifact_progress(artifact)
-      self.selected_bundle.status = custom.ModelManagerSP.DownloadStatus.failed
+      if self.selected_bundle:
+        self.selected_bundle.status = custom.ModelManagerSP.DownloadStatus.failed
      self._report_status()
      self._download_start_times.pop(artifact.fileName, None)
      raise

  async def _process_model(self, model, destination_path: str) -> None:
    """Processes a single model download including verification"""
-    model_artifact = model.artifact
-    metadata_artifact = model.metadata
-
-    await self._process_artifact(metadata_artifact, destination_path)
-    await self._process_artifact(model_artifact, destination_path)
+    await self._process_artifact(model.artifact, destination_path)

  def _report_status(self) -> None:
    """Reports current status through messaging system"""
@@ -205,16 +217,16 @@ class ModelManagerSP:
    try:
      seen_artifacts: set[str] = set()
      for model in self.selected_bundle.models:
-        for artifact in (model.metadata, model.artifact):
-          if not artifact.fileName:
-            continue
-          if artifact.fileName in seen_artifacts:
-            artifact.downloadProgress.status = custom.ModelManagerSP.DownloadStatus.cached
-            artifact.downloadProgress.progress = 100
-            artifact.downloadProgress.eta = 0
-          else:
-            seen_artifacts.add(artifact.fileName)
-            await self._process_artifact(artifact, destination_path)
+        artifact = model.artifact
+        if not artifact.fileName:
+          continue
+        if artifact.fileName in seen_artifacts:
+          artifact.downloadProgress.status = custom.ModelManagerSP.DownloadStatus.cached
+          artifact.downloadProgress.progress = 100
+          artifact.downloadProgress.eta = 0
+        else:
+          seen_artifacts.add(artifact.fileName)
+          await self._process_artifact(artifact, destination_path)

      self.active_bundle = self.selected_bundle
      self.active_bundle.status = custom.ModelManagerSP.DownloadStatus.downloaded
@@ -275,8 +287,6 @@ class ModelManagerSP:
      for model in self.active_bundle.models:
        if hasattr(model, 'artifact') and model.artifact.fileName:
          active_files.append(model.artifact.fileName)
-        if hasattr(model, 'metadata') and model.metadata.fileName:
-          active_files.append(model.metadata.fileName)

    # Remove all files except active ones (including their chunk files)
    model_dir = Paths.model_root()
@@ -1,28 +0,0 @@
-from openpilot.sunnypilot.models.helpers import get_active_bundle
-from openpilot.sunnypilot.models.runners.model_runner import ModelRunner
-from openpilot.sunnypilot.models.runners.tinygrad.tinygrad_runner import TinygradRunner, TinygradSplitRunner
-from openpilot.sunnypilot.models.runners.constants import ModelType
-
-
-def get_model_runner() -> ModelRunner:
-  """
-  Factory function to create and return the appropriate ModelRunner instance.
-
-  Selects TinygradRunner, choosing TinygradSplitRunner if separate vision/policy
-  models are detected in the active bundle.
-
-  :return: An instance of a ModelRunner subclass (ONNXRunner, TinygradRunner, or TinygradSplitRunner).
-  """
-  bundle = get_active_bundle()
-  if bundle and bundle.models:
-    model_types = {m.type.raw for m in bundle.models}
-    # Check if the bundle uses separate vision and policy models (legacy or new split format)
-    split_types = {ModelType.vision, ModelType.policy, ModelType.offPolicy, ModelType.onPolicy}
-    if model_types & split_types:
-      return TinygradSplitRunner()
-    # Otherwise, assume a single model (likely supercombo)
-    if bundle.models:
-      return TinygradRunner(bundle.models[0].type.raw)
-
-  # Default fallback to TinygradRunner with the supercombo type if bundle info is missing/incomplete
-  return TinygradRunner(ModelType.supercombo)
@@ -1,174 +0,0 @@
-from abc import abstractmethod, ABC
-
-import numpy as np
-from openpilot.sunnypilot.models.helpers import get_active_bundle
-from openpilot.sunnypilot.models.runners.constants import NumpyDict, ShapeDict, Model, SliceDict, SEND_RAW_PRED
-from openpilot.common.hardware.hw import Paths
-import pickle
-
-CUSTOM_MODEL_PATH = Paths.model_root()
-
-
-class ModelData:
-  """
-  Stores metadata and configuration for a specific machine learning model.
-
-  This class loads model metadata (like input shapes and output slices)
-  from a pickle file associated with a model instance.
-
-  :param model: The machine learning model object containing metadata.
-  """
-  def __init__(self, model: Model):
-    self.model = model
-    self.metadata = model.metadata
-    self.input_shapes: ShapeDict = {}
-    self.output_slices: SliceDict = {}
-    if self.metadata:
-      self._load_metadata()
-
-  def _load_metadata(self) -> None:
-    """Loads input shapes and output slices from the model's metadata pickle file."""
-    metadata_path = f"{CUSTOM_MODEL_PATH}/{self.metadata.fileName}"
-    with open(metadata_path, 'rb') as f:
-      model_metadata = pickle.load(f)
-    self.input_shapes = model_metadata.get('input_shapes', {})
-    self.output_slices = model_metadata.get('output_slices', {})
-
-
-class ModularRunner(ABC):
-  """
-  Represents a modular runner for handling and slicing model outputs.
-
-  This abstract base class is designed to provide an interface for modular
-  parsing and processing of model outputs. Classes inheriting from it must
-  implement the specified abstract methods, defining how model outputs
-  should be handled and stored. The primary goal is to enable structured
-  parsing of outputs through a dictionary-based method mapping.
-
-  :ivar parser_method_dict: Mapping dictionary containing parser methods
-      for handling specific types of outputs.
-  :type parser_method_dict: dict
-  """
-
-  @property
-  @abstractmethod
-  def parser_method_dict(self) -> dict:
-    pass
-
-  @parser_method_dict.setter
-  @abstractmethod
-  def parser_method_dict(self, value: dict) -> None:
-    pass
-
-  @abstractmethod
-  def _slice_outputs(self, model_outputs: np.ndarray) -> NumpyDict:
-    pass
-
-
-class ModelRunner(ModularRunner):
-  """
-  Abstract base class for managing and executing machine learning models.
-
-  Provides a common interface for loading models, preparing inputs, running
-  inference, and slicing/parsing outputs based on model metadata. Derived
-  classes implement the specifics of input preparation and model execution
-  for different frameworks (e.g., Tinygrad, ONNX).
-  """
-
-  def __init__(self):
-    """Initializes the model runner, loading the active model bundle."""
-    self.is_20hz: bool | None = None
-    self.is_20hz_3d: bool | None = None
-    self.models: dict[int, ModelData] = {}
-    self._model_data: ModelData | None = None  # Active model data for current operation
-    self._parser_method_dict: dict = {}
-    self.inputs: dict = {}
-    self._parser = None
-    self._load_models()
-    self._constants = None
-
-  @property
-  def constants(self):
-    return self._constants
-
-  @property
-  def parser_method_dict(self) -> dict:
-    """Returns the dictionary mapping model types to their respective parsing methods."""
-    return self._parser_method_dict
-
-  @parser_method_dict.setter
-  def parser_method_dict(self, value: dict) -> None:
-    """Sets the dictionary mapping model types to their respective parsing methods."""
-    self._parser_method_dict = value
-
-  def _load_models(self) -> None:
-    """Loads the active model bundle configuration and sets up ModelData."""
-    bundle = get_active_bundle()
-    if not bundle:
-      raise ValueError("No active model bundle found, why are we being executed?")
-
-    self.models = {model.type.raw: ModelData(model) for model in bundle.models}
-    self.is_20hz = bundle.is20hz
-    self.is_20hz_3d = False
-
-  @property
-  def input_shapes(self) -> ShapeDict:
-    """Returns the input shapes for the currently active model."""
-    if self._model_data:
-      return self._model_data.input_shapes
-    raise ValueError("Model data is not available. Ensure the model is loaded correctly.")
-
-  @property
-  def output_slices(self) -> SliceDict:
-    """Returns the output slices for the currently active model."""
-    if self._model_data:
-      return self._model_data.output_slices
-    raise ValueError("Model data is not available. Ensure the model is loaded correctly.")
-
-  @property
-  def vision_input_names(self) -> list[str]:
-    """Returns the list of vision input names from the input shapes."""
-    if self._model_data:
-      return list(self._model_data.input_shapes.keys())
-    raise ValueError("Model data is not available. Ensure the model is loaded correctly.")
-
-  @abstractmethod
-  def prepare_inputs(self, numpy_inputs: NumpyDict) -> dict:
-    """
-    Abstract method to prepare inputs for model inference.
-
-    :param numpy_inputs: Dictionary of numpy arrays for non-image inputs.
-    :return: Dictionary of prepared inputs ready for the model.
-    """
-    raise NotImplementedError
-
-  @abstractmethod
-  def _run_model(self) -> NumpyDict:
-    """
-    Abstract method to execute model inference with prepared inputs.
-
-    :return: Dictionary containing the model's raw output arrays.
-    """
-    raise NotImplementedError
-
-  def _slice_outputs(self, model_outputs: np.ndarray) -> NumpyDict:
-    """
-    Slices the raw model output array based on the output_slices metadata.
-
-    :param model_outputs: The raw numpy array output from the model.
-    :return: A dictionary where keys are output names and values are sliced numpy arrays.
-    """
-    if not self._model_data:
-      raise ValueError("Model data is not available. Ensure the model is loaded correctly.")
-    sliced_outputs = {k: model_outputs[np.newaxis, v] for k, v in self._model_data.output_slices.items()}
-    if SEND_RAW_PRED:
-      sliced_outputs['raw_pred'] = model_outputs.copy()  # Optionally include the full raw output
-    return sliced_outputs
-
-  def run_model(self) -> NumpyDict:
-    """
-    Executes the model inference pipeline: runs the model and parses outputs.
-
-    :return: Dictionary containing the final parsed model outputs.
-    """
-    return self._run_model()  # Parsing is handled within specific runner implementations
@@ -1,91 +0,0 @@
-import os
-from abc import ABC
-
-import numpy as np
-from openpilot.sunnypilot.modeld_v2.parse_model_outputs import Parser as CombinedParser
-from openpilot.sunnypilot.modeld_v2.parse_model_outputs_split import Parser as SplitParser
-from openpilot.sunnypilot.models.runners.constants import ModelType, NumpyDict
-from openpilot.sunnypilot.models.runners.model_runner import ModularRunner
-from openpilot.common.hardware.hw import Paths
-
-
-SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
-CUSTOM_MODEL_PATH = Paths.model_root()
-
-
-class OffPolicyTinygrad(ModularRunner, ABC):
-  """
-  A TinygradRunner specialized for off-policy models.
-
-  Uses a SplitParser to handle outputs specific to the off-policy part of a split model setup.
-  """
-  def __init__(self):
-    self._off_policy_parser = SplitParser()
-    self.parser_method_dict[ModelType.offPolicy] = self._parse_off_policy_outputs
-
-  def _parse_off_policy_outputs(self, model_outputs: np.ndarray) -> NumpyDict:
-    """Parses off-policy model outputs using SplitParser."""
-    result: NumpyDict = self._off_policy_parser.parse_policy_outputs(self._slice_outputs(model_outputs))
-    return result
-
-
-class OnPolicyTinygrad(ModularRunner, ABC):
-  """
-  A TinygradRunner specialized for on-policy models.
-
-  Uses a SplitParser to handle outputs specific to the on-policy part of a split model setup.
-  """
-  def __init__(self):
-    self._on_policy_parser = SplitParser()
-    self.parser_method_dict[ModelType.onPolicy] = self._parse_on_policy_outputs
-
-  def _parse_on_policy_outputs(self, model_outputs: np.ndarray) -> NumpyDict:
-    """Parses on-policy model outputs using SplitParser."""
-    result: NumpyDict = self._on_policy_parser.parse_policy_outputs(self._slice_outputs(model_outputs))
-    return result
-
-
-class PolicyTinygrad(ModularRunner, ABC):
-  """
-  A TinygradRunner specialized for policy-only models.
-
-  Uses a SplitParser to handle outputs specific to the policy part of a split model setup.
-  """
-  def __init__(self):
-    self._policy_parser = SplitParser()
-    self.parser_method_dict[ModelType.policy] = self._parse_policy_outputs
-
-  def _parse_policy_outputs(self, model_outputs: np.ndarray) -> NumpyDict:
-    """Parses policy model outputs using SplitParser."""
-    result: NumpyDict = self._policy_parser.parse_policy_outputs(self._slice_outputs(model_outputs))
-    return result
-
-class VisionTinygrad(ModularRunner, ABC):
-  """
-  A TinygradRunner specialized for vision-only models.
-
-  Uses a SplitParser to handle outputs specific to the vision part of a split model setup.
-  """
-  def __init__(self):
-    self._vision_parser = SplitParser()
-    self.parser_method_dict[ModelType.vision] = self._parse_vision_outputs
-
-  def _parse_vision_outputs(self, model_outputs: np.ndarray) -> NumpyDict:
-    """Parses vision model outputs using SplitParser."""
-    result: NumpyDict = self._vision_parser.parse_vision_outputs(self._slice_outputs(model_outputs))
-    return result
-
-class SupercomboTinygrad(ModularRunner, ABC):
-  """
-  A TinygradRunner specialized for vision-only models.
-
-  Uses a SplitParser to handle outputs specific to the vision part of a split model setup.
-  """
-  def __init__(self):
-    self._supercombo_parser = CombinedParser()
-    self.parser_method_dict[ModelType.supercombo] = self._parse_supercombo_outputs
-
-  def _parse_supercombo_outputs(self, model_outputs: np.ndarray) -> NumpyDict:
-    """Parses vision model outputs using SplitParser."""
-    result: NumpyDict = self._supercombo_parser.parse_outputs(self._slice_outputs(model_outputs))
-    return result
@@ -1,179 +0,0 @@
-import pickle
-
-import numpy as np
-from openpilot.sunnypilot.models.runners.constants import NumpyDict, ModelType, ShapeDict, CUSTOM_MODEL_PATH, SliceDict
-from openpilot.sunnypilot.models.runners.model_runner import ModelRunner
-from openpilot.sunnypilot.models.runners.tinygrad.model_types import PolicyTinygrad, VisionTinygrad, SupercomboTinygrad, OffPolicyTinygrad, OnPolicyTinygrad
-from openpilot.sunnypilot.models.split_model_constants import SplitModelConstants
-from openpilot.sunnypilot.modeld_v2.constants import ModelConstants
-
-from tinygrad.tensor import Tensor
-
-
-class TinygradRunner(ModelRunner, SupercomboTinygrad, PolicyTinygrad, VisionTinygrad, OffPolicyTinygrad, OnPolicyTinygrad):
-  """
-  A ModelRunner implementation for executing Tinygrad models.
-
-  Handles loading Tinygrad model artifacts (.pkl), preparing inputs as Tinygrad
-  Tensors (potentially using QCOM extensions on TICI), running inference,
-  and parsing the outputs.
-
-  :param model_type: The type of model (e.g., supercombo) to load and run.
-  """
-  def __init__(self, model_type: int = ModelType.supercombo):
-    ModelRunner.__init__(self)
-    SupercomboTinygrad.__init__(self)
-    PolicyTinygrad.__init__(self)
-    VisionTinygrad.__init__(self)
-    OffPolicyTinygrad.__init__(self)
-    OnPolicyTinygrad.__init__(self)
-    self._constants = ModelConstants
-    self._model_data = self.models.get(model_type)
-    if not self._model_data or not self._model_data.model:
-      raise ValueError(f"Model data for type {model_type} not available.")
-
-    artifact_filename = self._model_data.model.artifact.fileName
-    assert artifact_filename.endswith('_tinygrad.pkl'), \
-      f"Invalid model file {artifact_filename} for TinygradRunner"
-
-    model_pkl_path = f"{CUSTOM_MODEL_PATH}/{artifact_filename}"
-    with open(model_pkl_path, "rb") as f:
-      try:
-        # Load the compiled Tinygrad model runner function
-        self.model_run = pickle.load(f)
-      except FileNotFoundError as e:
-        # Provide a helpful error message if the model was built for a different platform
-        assert "/dev/kgsl-3d0" not in str(e), "Model was built on C3 or C3X, but is being loaded on PC"
-        raise
-
-    # Map input names to their required dtype and device from the loaded model
-    self.input_to_dtype = {}
-    self.input_to_device = {}
-    for idx, name in enumerate(self.model_run.captured.expected_names):
-      info = self.model_run.captured.expected_input_info[idx]
-      self.input_to_dtype[name] = info[2]  # dtype
-      self.input_to_device[name] = info[3]  # device
-    self._policy_cached = False
-
-  @property
-  def vision_input_names(self) -> list[str]:
-    """Returns the list of vision input names from the input shapes."""
-    return [name for name in self.input_shapes.keys() if 'img' in name]
-
-
-  def prepare_policy_inputs(self, numpy_inputs: NumpyDict):
-    if not self._policy_cached:
-      for key, value in numpy_inputs.items():
-        self.inputs[key] = Tensor(value, device='NPY').realize()
-      self._policy_cached = True
-
-  def prepare_inputs(self, numpy_inputs: NumpyDict) -> dict:
-    """Prepares all vision and policy inputs for the model."""
-    self.prepare_policy_inputs(numpy_inputs)
-    for key in self.vision_input_names:
-      if key in self.inputs:
-        self.inputs[key] = self.inputs[key].cast(self.input_to_dtype[key])
-    return self.inputs
-
-  def _run_model(self) -> NumpyDict:
-    """Runs the Tinygrad model inference and parses the outputs."""
-    outputs = self.model_run(**self.inputs).contiguous().realize().uop.base.buffer.numpy().flatten()
-    return self._parse_outputs(outputs)
-
-  def _parse_outputs(self, model_outputs: np.ndarray) -> NumpyDict:
-    """Parses the raw model outputs using the standard Parser."""
-    if self._model_data is None:
-      raise ValueError("Model data is not available. Ensure the model is loaded correctly.")
-
-    result: NumpyDict = self.parser_method_dict[self._model_data.model.type.raw](model_outputs)
-    return result
-
-
-class TinygradSplitRunner(ModelRunner):
-  """
-  A ModelRunner that coordinates separate TinygradVisionRunner and TinygradPolicyRunner instances.
-
-  Manages the execution of split vision and policy models, combining their inputs and outputs.
-  """
-  def __init__(self):
-    super().__init__()
-    self.is_20hz_3d = True
-    self.vision_runner = TinygradRunner(ModelType.vision)
-    self.policy_runner = TinygradRunner(ModelType.policy) if self.models.get(ModelType.policy) else None
-    self.off_policy_runner = TinygradRunner(ModelType.offPolicy) if self.models.get(ModelType.offPolicy) else None
-    self.on_policy_runner = TinygradRunner(ModelType.onPolicy) if self.models.get(ModelType.onPolicy) else None
-    self._constants = SplitModelConstants
-
-  def _run_model(self) -> NumpyDict:
-    """Runs both vision and policy models and merges their parsed outputs."""
-    vision_output = self.vision_runner.run_model()
-    outputs = {**vision_output}
-
-    if self.policy_runner:
-      policy_output = self.policy_runner.run_model()
-      outputs.update(policy_output)
-
-    if self.off_policy_runner:
-      off_policy_output = self.off_policy_runner.run_model()
-      if self.on_policy_runner:
-        off_policy_output.pop('plan', None)
-      outputs.update(off_policy_output)
-
-    if self.on_policy_runner:
-      on_policy_output = self.on_policy_runner.run_model()
-      outputs.update(on_policy_output)
-
-    if 'planplus' in outputs and 'plan' in outputs:
-      outputs['plan'] = outputs['plan'] + outputs['planplus']
-
-    return outputs
-
-  @property
-  def vision_input_names(self) -> list[str]:
-    """Returns the list of vision input names from the vision runner."""
-    return list(self.vision_runner.vision_input_names)
-
-  @property
-  def input_shapes(self) -> ShapeDict:
-    """Returns the combined input shapes from both vision and policy models."""
-    shapes = {**self.vision_runner.input_shapes}
-    if self.policy_runner:
-      shapes.update(self.policy_runner.input_shapes)
-    if self.off_policy_runner:
-      shapes.update(self.off_policy_runner.input_shapes)
-    if self.on_policy_runner:
-      shapes.update(self.on_policy_runner.input_shapes)
-    return shapes
-
-  @property
-  def output_slices(self) -> SliceDict:
-    """Returns the combined output slices from both vision and policy models."""
-    slices = {**self.vision_runner.output_slices}
-    if self.policy_runner:
-      slices.update(self.policy_runner.output_slices)
-    if self.off_policy_runner:
-      slices.update(self.off_policy_runner.output_slices)
-    if self.on_policy_runner:
-      slices.update(self.on_policy_runner.output_slices)
-    return slices
-
-  def prepare_inputs(self, numpy_inputs: NumpyDict) -> dict:
-    """Prepares inputs for both vision and policy models."""
-    if self.policy_runner:
-      self.policy_runner.prepare_policy_inputs(numpy_inputs)
-
-    for key in self.vision_input_names:
-      if key in self.inputs:
-        self.vision_runner.inputs[key] = self.inputs[key].cast(self.vision_runner.input_to_dtype[key])
-
-    inputs = {**self.vision_runner.inputs}
-    if self.policy_runner:
-      inputs.update(self.policy_runner.inputs)
-
-    if self.off_policy_runner:
-      self.off_policy_runner.prepare_policy_inputs(numpy_inputs)
-      inputs.update(self.off_policy_runner.inputs)
-    if self.on_policy_runner:
-      self.on_policy_runner.prepare_policy_inputs(numpy_inputs)
-      inputs.update(self.on_policy_runner.inputs)
-    return inputs
@@ -43,6 +43,7 @@ class SplitModelConstants:
  LANE_LINES_WIDTH = 2
  ROAD_EDGES_WIDTH = 2
  PLAN_WIDTH = 15
+  ACTION_WIDTH = 2
  DESIRE_PRED_WIDTH = 8
  LAT_PLANNER_SOLUTION_WIDTH = 4
  DESIRED_CURV_WIDTH = 1
@@ -8,6 +8,7 @@ from typing import Any

 from opendbc.car import structs
 from opendbc.car.interfaces import CarInterfaceBase
+from opendbc.car.toyota.values import CAR as TOYOTA_CAR
 from openpilot.common.params import Params
 from openpilot.common.swaglog import cloudlog
 from openpilot.sunnypilot.selfdrive.controls.lib.nnlc.helpers import get_nn_model_path
@@ -69,6 +70,32 @@ def _initialize_torque_lateral_control(CI: CarInterfaceBase, CP: structs.CarPara
    CI.configure_torque_tune(CP.carFingerprint, CP.lateralTuning)


+_PRIUS_TSS2_PID_KP_BP = [1.0, 1.5, 2.0, 3.0, 5.0, 7.5, 10.0, 15.0, 30.0]
+_PRIUS_TSS2_PID_KI_BP = [1.0, 1.5, 2.0, 3.0, 5.0, 7.5, 10.0, 15.0, 30.0]
+_PRIUS_TSS2_PID_KP_V = [0.1304, 0.1409, 0.1357, 0.1409, 0.15, 0.1614, 0.1826, 0.2348, 0.4696]
+_PRIUS_TSS2_PID_KI_V = [0.00016, 0.00035, 0.00063, 0.00141, 0.00391, 0.0088, 0.01565, 0.03522, 0.14087]
+_PRIUS_TSS2_PID_KF = 4e-05
+
+
+def _enforce_prius_tss2_pid_lateral_control(CP: structs.CarParams, params: Params = None) -> bool:
+  if params is None:
+    params = Params()
+
+  if CP.carFingerprint != TOYOTA_CAR.TOYOTA_PRIUS_TSS2:
+    return False
+
+  return params.get_bool("ToyotaPriusTss2Pid")
+
+
+def _initialize_prius_tss2_pid_lateral_control(CP: structs.CarParams) -> None:
+  CP.lateralTuning.init('pid')
+  CP.lateralTuning.pid.kpBP = _PRIUS_TSS2_PID_KP_BP
+  CP.lateralTuning.pid.kpV = _PRIUS_TSS2_PID_KP_V
+  CP.lateralTuning.pid.kiBP = _PRIUS_TSS2_PID_KI_BP
+  CP.lateralTuning.pid.kiV = _PRIUS_TSS2_PID_KI_V
+  CP.lateralTuning.pid.kf = _PRIUS_TSS2_PID_KF
+
+
 def _cleanup_unsupported_params(CP: structs.CarParams, CP_SP: structs.CarParamsSP, params: Params = None) -> None:
  if params is None:
    params = Params()
@@ -95,8 +122,15 @@ def _cleanup_unsupported_params(CP: structs.CarParams, CP_SP: structs.CarParamsS
 def setup_interfaces(CI: CarInterfaceBase, params: Params = None) -> None:
  enforce_torque = _enforce_torque_lateral_control(CI.CP, params)
  nnlc_enabled = _initialize_neural_network_lateral_control(CI.CP, CI.CP_SP, params)
+  prius_tss2_pid_enabled = _enforce_prius_tss2_pid_lateral_control(CI.CP, params)
+  if prius_tss2_pid_enabled:
+    # Prius TSS2 PID toggle takes priority over NNLC/EnforceTorqueControl for this car.
+    enforce_torque = False
+    nnlc_enabled = False
  _initialize_intelligent_cruise_button_management(CI.CP, CI.CP_SP, params)
  _initialize_torque_lateral_control(CI, CI.CP, enforce_torque, nnlc_enabled)
+  if prius_tss2_pid_enabled:
+    _initialize_prius_tss2_pid_lateral_control(CI.CP)
  _cleanup_unsupported_params(CI.CP, CI.CP_SP)

  try:
@@ -123,12 +157,16 @@ def initialize_params(params) -> list[dict[str, Any]]:
  # tesla
  keys.extend([
    "TeslaCoopSteering",
+    "TeslaMadsScreenButton",
  ])

  # toyota
  keys.extend([
    "ToyotaEnforceStockLongitudinal",
    "ToyotaStopAndGoHack",
+    "ToyotaEnhancedBsm",
+    "ToyotaAutoHold",
+    "ToyotaPriusTss2Pid",
  ])

  return [{k: params.get(k, return_default=True)} for k in keys]
@@ -0,0 +1,122 @@
+import numpy as np
+import pytest
+from opendbc.car import structs
+from openpilot.sunnypilot.selfdrive.car import interfaces as si
+
+
+class FakeParams:
+  def __init__(self, values=None):
+    self.values = values or {}
+
+  def get_bool(self, key):
+    return bool(self.values.get(key, False))
+
+  def get(self, key, return_default=False):
+    return self.values.get(key)
+
+  def remove(self, key):
+    self.values.pop(key, None)
+
+
+class FakeCI:
+  def __init__(self, CP, CP_SP):
+    self.CP = CP
+    self.CP_SP = CP_SP
+    self.configure_torque_tune_calls = 0
+
+  def configure_torque_tune(self, fingerprint, tune):
+    self.configure_torque_tune_calls += 1
+    tune.init('torque')
+
+
+def make_prius_tss2_cp():
+  CP = structs.CarParams(carFingerprint='TOYOTA_PRIUS_TSS2', steerControlType=structs.CarParams.SteerControlType.torque)
+  CP.lateralTuning.init('torque')
+  return CP
+
+
+class TestPriusTss2PidGate:
+  def test_disabled_for_other_toyota_platforms(self):
+    CP = structs.CarParams(carFingerprint='TOYOTA_RAV4_TSS2')
+    assert si._enforce_prius_tss2_pid_lateral_control(CP, FakeParams({'ToyotaPriusTss2Pid': True})) is False
+
+  def test_disabled_when_param_off(self):
+    CP = make_prius_tss2_cp()
+    assert si._enforce_prius_tss2_pid_lateral_control(CP, FakeParams({'ToyotaPriusTss2Pid': False})) is False
+
+  def test_enabled_for_prius_tss2_with_param_on(self):
+    CP = make_prius_tss2_cp()
+    assert si._enforce_prius_tss2_pid_lateral_control(CP, FakeParams({'ToyotaPriusTss2Pid': True})) is True
+
+
+class TestPriusTss2PidApply:
+  def test_flips_union_and_sets_gains(self):
+    CP = make_prius_tss2_cp()
+    assert CP.lateralTuning.which() == 'torque'
+
+    si._initialize_prius_tss2_pid_lateral_control(CP)
+
+    assert CP.lateralTuning.which() == 'pid'
+    assert list(CP.lateralTuning.pid.kpV) == pytest.approx(si._PRIUS_TSS2_PID_KP_V)
+    assert list(CP.lateralTuning.pid.kiV) == pytest.approx(si._PRIUS_TSS2_PID_KI_V)
+    assert CP.lateralTuning.pid.kf == pytest.approx(si._PRIUS_TSS2_PID_KF)
+    # PIDController interp needs non-empty breakpoints matching V lists.
+    assert len(CP.lateralTuning.pid.kpBP) == len(CP.lateralTuning.pid.kpV)
+    assert len(CP.lateralTuning.pid.kiBP) == len(CP.lateralTuning.pid.kiV)
+
+  def test_kp_rises_toward_highway_not_boosted_at_low_speed(self):
+    """Real on-road data (route 550a71ee4c7a7fbe/00000549--01e8f2ab51) showed boosting kp below
+    5 m/s increased saturation and hunting rather than helping - the shape must rise toward highway
+    speed, matching every other real multi-breakpoint PID car's tune (GM Volt, Cadillac Escalade
+    ESV, Honda Civic 2022) and the LatControlTorqueV0-derived KP_INTERP shape, not the reverse."""
+    CP = make_prius_tss2_cp()
+    si._initialize_prius_tss2_pid_lateral_control(CP)
+
+    kp_parking_lot = np.interp(2.0, CP.lateralTuning.pid.kpBP, CP.lateralTuning.pid.kpV)
+    kp_cruise = np.interp(5.0, CP.lateralTuning.pid.kpBP, CP.lateralTuning.pid.kpV)
+    kp_highway = np.interp(30.0, CP.lateralTuning.pid.kpBP, CP.lateralTuning.pid.kpV)
+    assert kp_parking_lot < kp_cruise < kp_highway
+
+
+class TestSetupInterfacesPrecedence:
+  def test_pid_toggle_wins_over_nnlc_and_enforce_torque(self):
+    """The Prius TSS2 PID toggle must be the last thing to touch lateralTuning: if the user also
+    has NNLC and/or EnforceTorqueControl on, the union must still end up 'pid' and
+    configure_torque_tune must never run, or the car would silently keep driving on torque."""
+    CP = make_prius_tss2_cp()
+    CP_SP = structs.CarParamsSP()
+    CI = FakeCI(CP, CP_SP)
+    params = FakeParams({
+      'EnforceTorqueControl': True,
+      'NeuralNetworkLateralControl': True,
+      'ToyotaPriusTss2Pid': True,
+    })
+
+    si.setup_interfaces(CI, params)
+
+    assert CP.lateralTuning.which() == 'pid'
+    assert CI.configure_torque_tune_calls == 0
+
+  def test_other_toyota_platform_unaffected_by_toggle(self):
+    """The same param being on must not leak into a different car's tuning."""
+    CP = structs.CarParams(carFingerprint='TOYOTA_RAV4_TSS2', steerControlType=structs.CarParams.SteerControlType.torque)
+    CP.lateralTuning.init('torque')
+    CP_SP = structs.CarParamsSP()
+    CI = FakeCI(CP, CP_SP)
+    params = FakeParams({'ToyotaPriusTss2Pid': True})
+
+    si.setup_interfaces(CI, params)
+
+    assert CP.lateralTuning.which() == 'torque'
+    assert CI.configure_torque_tune_calls == 0
+
+  def test_toggle_off_leaves_torque_control_path_intact(self):
+    CP = make_prius_tss2_cp()
+    CP_SP = structs.CarParamsSP()
+    CI = FakeCI(CP, CP_SP)
+    params = FakeParams({'EnforceTorqueControl': True, 'ToyotaPriusTss2Pid': False})
+
+    si.setup_interfaces(CI, params)
+
+    assert CP.lateralTuning.which() == 'torque'
+    assert CI.configure_torque_tune_calls == 1
@@ -10,12 +10,14 @@ import openpilot.cereal.messaging as messaging
 from openpilot.cereal import log, custom

 from opendbc.car import structs
+from opendbc.car.toyota.values import CAR as TOYOTA_CAR
 from openpilot.common.params import Params
 from openpilot.common.swaglog import cloudlog
 from openpilot.sunnypilot import PARAMS_UPDATE_PERIOD
 from openpilot.sunnypilot.livedelay.helpers import get_lat_delay
 from openpilot.sunnypilot.modeld_v2.modeld_base import ModelStateBase
 from openpilot.sunnypilot.selfdrive.controls.lib.blinker_pause_lateral import BlinkerPauseLateral
+from openpilot.sunnypilot.selfdrive.controls.lib.latcontrol_pid_ext import LatControlPidSmooth
 from openpilot.sunnypilot.selfdrive.controls.lib.latcontrol_torque_v0 import LatControlTorque as LatControlTorqueV0


@@ -35,12 +37,15 @@ class ControlsExt(ModelStateBase):
    self.pm_services_ext = ['carControlSP']

  def initialize_lateral_control(self, lac, CI, dt):
+    if self.CP.lateralTuning.which() != 'torque':
+      if self.CP.carFingerprint == TOYOTA_CAR.TOYOTA_PRIUS_TSS2 and self.CP.lateralTuning.which() == 'pid':
+        return LatControlPidSmooth(self.CP, self.CP_SP, CI, dt)
+      return lac
+
    enforce_torque_control = self.params.get_bool("EnforceTorqueControl")
    torque_versions = self.params.get("TorqueControlTune")
    if not enforce_torque_control:
-      if self.CP.lateralTuning.which() == 'torque':
-        return LatControlTorqueV0(self.CP, self.CP_SP, CI, dt)  # FIXME-SP: revert when upstream fixes tuning issues with v1
-      return lac
+      return LatControlTorqueV0(self.CP, self.CP_SP, CI, dt)  # FIXME-SP: revert when upstream fixes tuning issues with v1

    if torque_versions == 0.0:  # v0
      return LatControlTorqueV0(self.CP, self.CP_SP, CI, dt)
@@ -0,0 +1,432 @@
+import math
+
+import numpy as np
+
+from opendbc.car.interfaces import ACCEL_MAX
+from openpilot.common.params import Params
+from openpilot.common.realtime import DT_MDL
+from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import LongitudinalPlanSource
+from openpilot.sunnypilot import get_sanitize_int_param
+from openpilot.sunnypilot.selfdrive.controls.lib.accel_controller.constants import (
+  CAP_FILTER_FRAMES, COMFORT_DECEL, DEPARTURE_MOTION_NOISE_FLOOR, LAUNCH_END_SPEED, LAUNCH_TARGET_HEADROOM, LAUNCH_TARGET_SLEW,
+  LEAD_BRAKING_ACCEL_THRESHOLD, LEAD_DROPOUT_COAST_TIME, LEAD_LOSS_HOLD_TIME, LEAD_MATCH_ACCEL_SLEW, LEAD_MATCH_GAP_GAIN, LEAD_MATCH_SPEED_HEADROOM,
+  LEAD_SWITCH_MAX_HOLD_TIME,
+  MATCHED_SPEED_DECEL_RATE, MPC_DECEL_JERK_COST_MULTIPLIER, MPC_DECEL_JERK_MAX_REQUIRED_DECEL, MPC_DECEL_JERK_MAX_REQUIRED_DECEL_RATE,
+  MPC_DECEL_JERK_MAX_TARGET_REDUCTION, MPC_DECEL_TREND_FRAMES, SPEED_RELIEF_DEADBAND, SPEED_RESTRICT_DEADBAND, TARGET_SPEED_ARM_MARGIN,
+  TARGET_RELEASE_SLEW, TARGET_SPEED_RESERVE, PLANNER_BRAKING_ACCEL_THRESHOLD, RADAR_STALE_TIMEOUT, STOP_HOLD_CREEP_DISTANCE, STOP_HOLD_EGO_SPEED,
+  STOP_HOLD_EXIT_FRAMES, STOP_HOLD_EXIT_SPEED, STOP_HOLD_MAX_LEAD_DISTANCE, VEGO_NOISE_TOLERANCE, PARAM_READ_INTERVAL, AccelProfile,
+  profile_accel_max, sanitize_profile,
+)
+from openpilot.sunnypilot.selfdrive.controls.lib.accel_controller.helpers import build_accel_ceiling, is_valid_context
+from openpilot.sunnypilot.selfdrive.controls.lib.accel_controller.lead import LeadPlan, calculate_lead_plan, has_radar_lead, is_lead_source
+from openpilot.sunnypilot.selfdrive.controls.lib.accel_controller.state import AccelControllerState, TargetState
+
+
+class AccelController:
+  def __init__(self, CP, dt: float = DT_MDL):
+    if not math.isfinite(dt) or dt <= 0.0:
+      raise ValueError("dt must be finite and positive")
+
+    self.dt = dt
+    self.delay = float(CP.longitudinalActuatorDelay) + DT_MDL
+    self.lead_loss_hold_frames = max(CAP_FILTER_FRAMES, math.ceil(LEAD_LOSS_HOLD_TIME / dt))
+    self.lead_dropout_coast_frames = max(self.lead_loss_hold_frames, math.ceil(LEAD_DROPOUT_COAST_TIME / dt))
+    self.lead_switch_max_hold_frames = max(self.lead_loss_hold_frames, math.ceil(LEAD_SWITCH_MAX_HOLD_TIME / dt))
+    self.radar_stale_frames = max(1, math.ceil(RADAR_STALE_TIMEOUT / dt))
+    self.params = Params()
+    self.available = bool(CP.openpilotLongitudinalControl)
+    self.enabled = False
+    self.profile = AccelProfile.normal
+    self._param_read_frames = max(1, int(round(PARAM_READ_INTERVAL / dt)))
+    self._param_frame = 0
+    self._jerk_smoothing_blocked = False
+    self._required_decel_samples: list[float] = []
+    self._required_decel_lead = -1
+    self._required_decel_lead_track_id = -1
+    self._lead_trend_warmup = False
+    self._cruise_accel_limited = False
+    self.target_state = TargetState()
+    self._held_lead_plan: LeadPlan | None = None
+    self.is_active = self.launching = self.departure_launching = False
+    self.output_v_target = 0.0
+    self.mpc_accel_max: tuple[float, ...] | None = None
+    self.cruise_accel_max: float | None = None
+    self.state = AccelControllerState.inactive
+    self.selected_lead = -1
+    self.selected_lead_track_id = -1
+    self.required_decel = 0.0
+
+  @property
+  def is_enabled(self) -> bool:
+    return self.available and self.enabled
+
+  def update_params(self) -> None:
+    if self._param_frame % self._param_read_frames == 0:
+      self.enabled = self.params.get_bool("AccelPersonalityEnabled")
+      self.profile = get_sanitize_int_param("AccelPersonality", AccelProfile.eco, AccelProfile.sport, self.params)
+    self._param_frame += 1
+
+  @staticmethod
+  def _profile(profile: int) -> int:
+    return sanitize_profile(profile)
+
+  @staticmethod
+  def get_profile_accel_max(profile: int, v_ego: float) -> float:
+    return profile_accel_max(profile, v_ego)
+
+  def _update_target(self, lead_plan: LeadPlan, base_speed: float, v_ego: float, profile: int, profile_max_accel: float,
+                     previous_should_stop: bool, previous_mpc_source, planner_speed: float, planner_accel: float) -> float:
+    state = self.target_state
+    lead_filter_ready = state.update_samples(lead_plan, self.dt)
+    state.active_frames += 1
+    has_lead = lead_plan.selected_lead >= 0
+    filtered_cap = state.filtered_cap
+    slot_changed = has_lead and state.selected_lead >= 0 and lead_plan.selected_lead != state.selected_lead
+    track_changed = (has_lead and state.selected_lead >= 0 and lead_plan.selected_lead == state.selected_lead
+      and lead_plan.selected_lead_track_id != state.selected_lead_track_id
+      and (state.selected_lead_track_id >= 0 or lead_plan.selected_lead_track_id >= 0))
+    false_relief = has_lead and math.isfinite(filtered_cap) and lead_plan.cap >= filtered_cap + SPEED_RELIEF_DEADBAND
+    guarded_restriction = state.state in (AccelControllerState.restrict, AccelControllerState.hold, AccelControllerState.release)
+    switched_to_relief = ((slot_changed or track_changed) and false_relief
+                          and (guarded_restriction or planner_accel <= PLANNER_BRAKING_ACCEL_THRESHOLD))
+    confirmed_relief = (not has_lead or (state.target_speed is not None and lead_plan.closing_speed <= 0.0
+                        and lead_plan.cap >= state.target_speed + SPEED_RELIEF_DEADBAND))
+    state.update_lead_switch_guard(switched_to_relief, confirmed_relief, slot_changed or track_changed or false_relief,
+                                   self.lead_loss_hold_frames, self.lead_switch_max_hold_frames)
+    if slot_changed or track_changed:
+      state.matched_lead = False
+      state.matched_accel_limit = None
+    if has_lead:
+      state.selected_lead = lead_plan.selected_lead
+      state.selected_lead_track_id = lead_plan.selected_lead_track_id
+    elif state.lead_loss_frames >= self.lead_loss_hold_frames:
+      state.reset_lead_switch_guard()
+      state.selected_lead = state.selected_lead_track_id = -1
+    departure_separation = (lead_plan.departure_lead_separations[lead_plan.departure_lead_index]
+                            if lead_plan.departure_lead_index >= 0 else math.inf)
+    stopped_lead_hold = (has_lead and lead_plan.has_nearly_stopped_lead
+      and (lead_plan.departure_cap < 0.50 or (state.lead_braking and departure_separation <= STOP_HOLD_MAX_LEAD_DISTANCE)))
+    invalid_lead = lead_plan.lead_status and not has_lead
+    prior_lead_context = is_lead_source(previous_mpc_source) or math.isfinite(filtered_cap) or state.lead_braking
+    previous_stop = previous_should_stop and prior_lead_context and (not has_lead or lead_plan.departure_lead_speed < STOP_HOLD_EXIT_SPEED)
+    stop_evidence = stopped_lead_hold or lead_plan.cap < 0.50 or filtered_cap < 0.50 or (previous_stop and not state.launching) or invalid_lead
+    departure_motion_confirmed = (state.launching and state.departure_launch and has_lead
+      and (state.departure.progress(lead_plan, DEPARTURE_MOTION_NOISE_FLOOR) or state.departure.recent_motion()))
+    if state.active_frames >= self.lead_loss_hold_frames and math.isfinite(filtered_cap) and has_lead and planner_accel <= PLANNER_BRAKING_ACCEL_THRESHOLD:
+      state.lead_braking = True
+    elif not has_lead and state.lead_loss_frames >= self.lead_loss_hold_frames:
+      state.lead_braking = False
+
+    if state.target_speed is None:
+      e2e_handoff = previous_mpc_source == LongitudinalPlanSource.e2e
+      seed_from_ego = has_lead and planner_accel > PLANNER_BRAKING_ACCEL_THRESHOLD and not e2e_handoff
+      state.target_speed = min(base_speed, v_ego) if seed_from_ego else base_speed
+      if seed_from_ego and v_ego >= LAUNCH_END_SPEED and lead_plan.closing_speed > 0.0:
+        state.arm_release_slew()
+      state.e2e_braking_handoff = e2e_handoff and planner_accel < 0.0
+      state.state = AccelControllerState.free
+      if v_ego < STOP_HOLD_EGO_SPEED and not stop_evidence:
+        state.target_speed = min(base_speed, v_ego + LAUNCH_TARGET_HEADROOM)
+        state.state = AccelControllerState.release
+        state.launching = True
+        state.departure_launch = False
+    elif state.e2e_braking_handoff and planner_accel >= 0.0:
+      state.e2e_braking_handoff = False
+
+    state.target_speed = min(state.target_speed, base_speed)
+    if v_ego < STOP_HOLD_EGO_SPEED and stop_evidence and not departure_motion_confirmed and state.state != AccelControllerState.stopHold:
+      state.enter_stop_hold(lead_plan)
+      return state.target_speed
+
+    if state.state == AccelControllerState.stopHold:
+      state.departure.backfill_references()
+      fast_departure = (has_lead and min(lead_plan.selected_lead_speed, lead_plan.departure_lead_speed) > STOP_HOLD_EXIT_SPEED
+        and lead_plan.departure_cap > STOP_HOLD_EXIT_SPEED)
+      raw_departure = fast_departure or not lead_plan.lead_status and state.lead_loss_frames >= self.lead_loss_hold_frames
+      departed = state.departure.progress(lead_plan, STOP_HOLD_CREEP_DISTANCE) or raw_departure
+      if fast_departure and state.departure_frames == 0:
+        state.departure.keep_latest_motion_sample()
+      state.departure_frames = state.departure_frames + 1 if departed else 0
+      state.target_speed = 0.0
+      fast_departure_confirmed = fast_departure and state.departure.recent_motion()
+      if state.departure_frames < STOP_HOLD_EXIT_FRAMES or fast_departure and not fast_departure_confirmed:
+        return state.target_speed
+      state.target_speed = base_speed
+      state.state = AccelControllerState.release
+      state.departure_frames = 0
+      state.launching = True
+      state.departure_launch = has_lead
+      return state.target_speed
+
+    if state.launching:
+      renewed_stop = (has_lead and not departure_motion_confirmed
+        and (lead_plan.cap < STOP_HOLD_EXIT_SPEED
+             or (lead_plan.has_nearly_stopped_lead and lead_plan.departure_cap < STOP_HOLD_EXIT_SPEED)))
+      guarded_departure_loss = state.departure_launch and not lead_plan.lead_status and state.lead_loss_frames < self.lead_loss_hold_frames
+      if invalid_lead:
+        state.launching = state.departure_launch = False
+        if v_ego < STOP_HOLD_EGO_SPEED:
+          state.enter_stop_hold(lead_plan)
+          return state.target_speed
+        state.state = AccelControllerState.hold
+        return state.target_speed
+      if guarded_departure_loss:
+        state.state = AccelControllerState.hold
+        return state.target_speed
+      if state.departure_launch and not has_lead:
+        state.departure_launch = False
+      if renewed_stop:
+        state.launching = state.departure_launch = False
+        if v_ego < STOP_HOLD_EGO_SPEED:
+          state.enter_stop_hold(lead_plan)
+          return state.target_speed
+      if state.launching:
+        if state.departure_launch:
+          state.target_speed = base_speed
+        else:
+          launch_target = min(base_speed, v_ego + LAUNCH_TARGET_HEADROOM)
+          state.target_speed = min(base_speed, max(state.target_speed, launch_target) + LAUNCH_TARGET_SLEW * self.dt)
+        if v_ego >= LAUNCH_END_SPEED:
+          state.launching = state.departure_launch = False
+
+    comfort_decel = COMFORT_DECEL[profile]
+    if (has_lead and not state.launching and state.state == AccelControllerState.restrict
+      and lead_plan.closing_speed <= 0.0 and v_ego >= state.filtered_lead_speed - VEGO_NOISE_TOLERANCE):
+      state.matched_lead = True
+    elif not has_lead and state.lead_loss_frames >= self.lead_loss_hold_frames:
+      state.matched_lead = False
+
+    lost_lead_source = is_lead_source(previous_mpc_source) and not has_lead and planner_speed < state.target_speed
+    if not has_lead and (state.matched_lead or lost_lead_source):
+      if lost_lead_source:
+        state.lead_dropout = True
+        state.target_speed = planner_speed
+        state.arm_release_slew()
+      state.state = AccelControllerState.hold
+      return state.target_speed
+
+    if state.matched_lead:
+      if math.isfinite(state.filtered_lead_speed):
+        recovery_speed = min(base_speed, state.filtered_lead_speed + min(LEAD_MATCH_SPEED_HEADROOM, LEAD_MATCH_GAP_GAIN * lead_plan.usable_gap))
+        desired_accel_limit = min(profile_max_accel, max(recovery_speed - v_ego, 0.0))
+      else:
+        desired_accel_limit = 0.0
+      if state.filtered_lead_accel < LEAD_BRAKING_ACCEL_THRESHOLD:
+        desired_accel_limit = profile_max_accel
+      if state.matched_accel_limit is None:
+        state.matched_accel_limit = profile_max_accel
+      if state.lead_switch_guard_frames > 0:
+        desired_accel_limit = min(desired_accel_limit, state.matched_accel_limit)
+      state.matched_accel_limit = min(profile_max_accel, float(np.clip(
+        desired_accel_limit, state.matched_accel_limit - LEAD_MATCH_ACCEL_SLEW * self.dt,
+        state.matched_accel_limit + LEAD_MATCH_ACCEL_SLEW * self.dt,
+      )))
+      matched_ceiling = min(base_speed, filtered_cap)
+      if matched_ceiling <= state.target_speed - SPEED_RESTRICT_DEADBAND:
+        state.target_speed = max(matched_ceiling, state.target_speed - MATCHED_SPEED_DECEL_RATE * self.dt)
+        state.arm_release_slew()
+        state.state = AccelControllerState.restrict
+      elif (state.lead_switch_guard_frames == 0 and matched_ceiling >= state.target_speed + SPEED_RELIEF_DEADBAND
+            and (state.lead_switch_elapsed_frames < self.lead_switch_max_hold_frames or planner_accel > PLANNER_BRAKING_ACCEL_THRESHOLD)):
+        state.target_speed = min(matched_ceiling, state.target_speed + profile_max_accel * self.dt)
+        state.state = AccelControllerState.free if state.target_speed >= base_speed - SPEED_RESTRICT_DEADBAND else AccelControllerState.release
+      else:
+        state.state = AccelControllerState.free if state.target_speed >= base_speed - SPEED_RESTRICT_DEADBAND else AccelControllerState.hold
+      if state.state == AccelControllerState.free:
+        state.reset_release_slew(state.target_speed)
+      else:
+        state.update_release_slew(matched_ceiling, math.isfinite(matched_ceiling) and state.target_speed == matched_ceiling)
+      return state.target_speed
+    state.matched_accel_limit = None
+
+    ceiling = min(base_speed, filtered_cap)
+    if lead_filter_ready and state.active_frames == CAP_FILTER_FRAMES // 2 + 1 and not state.launching and planner_speed < state.target_speed:
+      state.target_speed = max(planner_speed, state.target_speed - comfort_decel * self.dt)
+
+    if ceiling <= state.target_speed - SPEED_RESTRICT_DEADBAND or (state.state == AccelControllerState.restrict and ceiling < state.target_speed):
+      state.target_speed = max(ceiling, state.target_speed - comfort_decel * self.dt)
+      state.arm_release_slew()
+      state.state = AccelControllerState.restrict
+      return state.target_speed
+
+    filter_warmup = has_lead and not math.isfinite(filtered_cap)
+    guarded_lead_loss = not has_lead and state.lead_loss_frames < (self.lead_dropout_coast_frames if state.lead_dropout else self.lead_loss_hold_frames)
+    if (filter_warmup or guarded_lead_loss) and state.target_speed < base_speed - SPEED_RESTRICT_DEADBAND:
+      state.state = AccelControllerState.hold
+      return state.target_speed
+
+    confirmed_clear_road = not math.isfinite(filtered_cap) and not guarded_lead_loss
+    relief = (not has_lead or lead_plan.closing_speed <= 0.0) and planner_accel > PLANNER_BRAKING_ACCEL_THRESHOLD
+    continuing_release = state.release_slew_armed and ceiling > state.target_speed
+    if relief and (continuing_release or ceiling >= state.target_speed + SPEED_RELIEF_DEADBAND
+                   or (confirmed_clear_road and ceiling > state.target_speed)):
+      if state.lead_switch_guard_frames == 0:
+        timed_out = state.lead_switch_elapsed_frames >= self.lead_switch_max_hold_frames
+        if not state.release_slew_armed and (timed_out or (state.release_settle_speed is not None
+                                                          and ceiling - state.target_speed > TARGET_RELEASE_SLEW * self.dt)):
+          state.arm_release_slew(force=True)
+        release_rate = comfort_decel if timed_out else TARGET_RELEASE_SLEW
+        state.target_speed = min(ceiling, state.target_speed + release_rate * self.dt) if state.release_slew_armed else ceiling
+      if state.release_slew_armed and state.target_speed < ceiling:
+        state.state = AccelControllerState.release
+      else:
+        state.state = AccelControllerState.free if state.target_speed >= base_speed - SPEED_RESTRICT_DEADBAND else AccelControllerState.hold
+    else:
+      state.state = AccelControllerState.free if state.target_speed >= base_speed - SPEED_RESTRICT_DEADBAND else AccelControllerState.hold
+    if state.target_speed >= base_speed:
+      state.lead_dropout = False
+      state.reset_release_slew(state.target_speed)
+    else:
+      state.update_release_slew(ceiling, math.isfinite(ceiling) and state.target_speed == ceiling)
+    return state.target_speed
+
+  def _update_freshness(self, radar_fresh: bool) -> None:
+    self.target_state.stale_frames = 0 if radar_fresh else self.target_state.stale_frames + 1
+    if self.target_state.stale_frames >= self.radar_stale_frames:
+      self.target_state = TargetState()
+
+  def reset(self) -> None:
+    self.target_state = TargetState()
+    self._held_lead_plan = None
+    self._jerk_smoothing_blocked = False
+    self._required_decel_samples.clear()
+    self._required_decel_lead = self._required_decel_lead_track_id = -1
+    self._lead_trend_warmup = False
+    self._cruise_accel_limited = False
+    self.is_active = self.launching = self.departure_launching = False
+    self.output_v_target = 0.0
+    self.mpc_accel_max = None
+    self.cruise_accel_max = None
+    self.state = AccelControllerState.inactive
+    self.selected_lead = -1
+    self.selected_lead_track_id = -1
+    self.required_decel = 0.0
+
+  def update(self, radar_state, *, base_speed: float, v_ego: float, a_ego: float, follow_personality, acc_selected: bool,
+             engaged: bool, cruise_initialized: bool, stock_accel_max: float, previous_should_stop: bool, radar_fresh: bool = True,
+             previous_mpc_source=None, planner_speed: float | None = None, planner_accel: float = 0.0) -> None:
+    self.profile = self._profile(self.profile)
+    sanitized_v_ego = max(v_ego, 0.0) if math.isfinite(v_ego) and v_ego >= -VEGO_NOISE_TOLERANCE else v_ego
+    profile_max_accel = self.get_profile_accel_max(self.profile, sanitized_v_ego)
+    stock_accel_max = float(stock_accel_max)
+    positive_accel_max = (max(0.0, min(profile_max_accel, stock_accel_max, ACCEL_MAX))
+                          if math.isfinite(profile_max_accel) and math.isfinite(stock_accel_max) else math.nan)
+    planner_speed = sanitized_v_ego if planner_speed is None else planner_speed
+    valid_context = is_valid_context(base_speed, sanitized_v_ego, a_ego, planner_speed, planner_accel, stock_accel_max, self.delay,
+                                     engaged, cruise_initialized)
+    enabled_context = valid_context and self.is_enabled and bool(acc_selected)
+    if enabled_context and radar_fresh:
+      lead_plan = calculate_lead_plan(radar_state, sanitized_v_ego, a_ego, self.delay, self.profile, follow_personality)
+      self._held_lead_plan = lead_plan
+    elif enabled_context and self._held_lead_plan is not None:
+      lead_plan = self._held_lead_plan
+    else:
+      lead_plan = LeadPlan(lead_status=has_radar_lead(radar_state))
+      self._held_lead_plan = None
+
+    if enabled_context:
+      self._update_freshness(radar_fresh)
+    active = enabled_context and (radar_fresh or self.target_state.target_speed is not None)
+    if active and radar_fresh:
+      target_speed = self._update_target(
+        lead_plan, base_speed, sanitized_v_ego, self.profile, profile_max_accel, previous_should_stop,
+        previous_mpc_source, planner_speed, planner_accel,
+      )
+    elif active:
+      target_speed = self.target_state.target_speed
+    else:
+      self.target_state = TargetState()
+      target_speed = base_speed
+
+    if not radar_fresh and not active:
+      self._held_lead_plan = None
+      lead_plan = LeadPlan(lead_status=has_radar_lead(radar_state))
+
+    state = self.target_state
+    stop_hold_active = active and state.state == AccelControllerState.stopHold
+    matched_limit_active = active and state.matched_lead and state.matched_accel_limit is not None and not state.e2e_braking_handoff
+    lead_accel_request = active and lead_plan.selected_lead >= 0 and lead_plan.closing_speed <= 0.0 and planner_accel >= 0.0
+    profile_limit_active = active and not stop_hold_active and (state.launching or not lead_plan.lead_status or lead_accel_request)
+    if matched_limit_active:
+      effective_accel_max = min(positive_accel_max, state.matched_accel_limit)
+    elif profile_limit_active:
+      effective_accel_max = positive_accel_max
+    else:
+      effective_accel_max = math.inf
+    mpc_accel_max = build_accel_ceiling(effective_accel_max, planner_accel) if matched_limit_active or profile_limit_active else None
+    guarded_lead_loss = not lead_plan.lead_status and state.selected_lead >= 0 and state.lead_loss_frames < self.lead_loss_hold_frames
+    lead_context = lead_plan.lead_status or math.isfinite(state.filtered_cap) or guarded_lead_loss
+    reserve_eligible = active and lead_context and not stop_hold_active and not state.launching and not state.e2e_braking_handoff
+    reserve_can_arm = reserve_eligible and state.lead_switch_guard_frames == 0
+    if not lead_context:
+      state.speed_reserve_armed = state.speed_reserve_suppressed = False
+    else:
+      if state.lead_switch_guard_frames > 0 and planner_accel <= PLANNER_BRAKING_ACCEL_THRESHOLD:
+        state.speed_reserve_suppressed = True
+      elif state.lead_switch_guard_frames == 0 and state.state != AccelControllerState.restrict:
+        state.speed_reserve_suppressed = False
+      if (reserve_can_arm and not state.speed_reserve_armed and math.isfinite(state.filtered_cap)
+        and state.filtered_cap <= target_speed + TARGET_SPEED_ARM_MARGIN):
+        state.speed_reserve_armed = True
+
+    output_target = 0.0 if stop_hold_active else target_speed
+    if reserve_eligible and state.speed_reserve_armed and not state.speed_reserve_suppressed:
+      output_target = max(0.0, output_target - TARGET_SPEED_RESERVE)
+
+    self.is_active = active
+    self.launching = active and state.launching
+    self.departure_launching = self.launching and state.departure_launch
+    self.output_v_target = output_target
+    self.mpc_accel_max = mpc_accel_max
+    start_cruise_accel_limit = (active and state.state == AccelControllerState.free and lead_plan.lead_status
+                                and lead_plan.closing_speed > 0.0 and planner_accel >= 0.0
+                                and previous_mpc_source == LongitudinalPlanSource.cruise)
+    keep_cruise_accel_limit = (self._cruise_accel_limited and active and lead_context and state.state == AccelControllerState.free
+                               and not state.e2e_braking_handoff)
+    self._cruise_accel_limited = start_cruise_accel_limit or keep_cruise_accel_limit
+    self.cruise_accel_max = positive_accel_max if self._cruise_accel_limited else None
+    self.state = state.state
+    self.selected_lead = lead_plan.selected_lead
+    self.selected_lead_track_id = lead_plan.selected_lead_track_id
+    self.required_decel = lead_plan.required_decel
+
+  def get_jerk_cost_multiplier(self, actuating: bool, prev_accel_constraint: bool, target_reduction: float, previous_mpc_failed: bool) -> float:
+    lead_restriction = (actuating and prev_accel_constraint and self.state == AccelControllerState.restrict and self.selected_lead >= 0
+      and not self.launching and target_reduction > 1e-6)
+    same_lead = self.selected_lead == self._required_decel_lead and self.selected_lead_track_id == self._required_decel_lead_track_id
+    lead_changed = lead_restriction and self._required_decel_lead >= 0 and not same_lead
+    if lead_changed:
+      self._lead_trend_warmup = True
+    elif not lead_restriction:
+      self._lead_trend_warmup = False
+    if not lead_restriction or not same_lead or not math.isfinite(self.required_decel):
+      self._required_decel_samples.clear()
+    if lead_restriction and math.isfinite(self.required_decel):
+      self._required_decel_samples.append(self.required_decel)
+      if len(self._required_decel_samples) > MPC_DECEL_TREND_FRAMES:
+        self._required_decel_samples.pop(0)
+    self._required_decel_lead = self.selected_lead if lead_restriction else -1
+    self._required_decel_lead_track_id = self.selected_lead_track_id if lead_restriction else -1
+
+    history = self._required_decel_samples
+    history_ready = len(history) == MPC_DECEL_TREND_FRAMES
+    tightening_lead = (history_ready
+      and (history[-1] - history[0]) / (self.dt * (len(history) - 1)) > MPC_DECEL_JERK_MAX_REQUIRED_DECEL_RATE
+      and sum(after > before for before, after in zip(history[:-1], history[1:], strict=True)) >= 2)
+    modest_decel = (lead_restriction and target_reduction < MPC_DECEL_JERK_MAX_TARGET_REDUCTION
+      and 0.0 < self.required_decel < MPC_DECEL_JERK_MAX_REQUIRED_DECEL)
+    smoothing_eligible = modest_decel and (not self._lead_trend_warmup or history_ready) and not tightening_lead
+    if history_ready:
+      self._lead_trend_warmup = False
+    if previous_mpc_failed or (lead_restriction and not self._jerk_smoothing_blocked and (not modest_decel or tightening_lead)):
+      self._jerk_smoothing_blocked = True
+    elif not lead_restriction:
+      self._jerk_smoothing_blocked = False
+    return MPC_DECEL_JERK_COST_MULTIPLIER if smoothing_eligible and not self._jerk_smoothing_blocked else 1.0
+
+  def update_should_stop(self, should_stop: bool) -> bool:
+    if not self.is_active:
+      return should_stop
+    if self.departure_launching:
+      return False
+    return should_stop or self.state == AccelControllerState.stopHold
@@ -0,0 +1,76 @@
+import math
+
+import numpy as np
+
+from openpilot.cereal import custom
+
+
+AccelProfile = custom.LongitudinalPlanSP.AccelController.Profile
+ACCEL_PROFILES = tuple(AccelProfile.schema.enumerants.values())
+
+COMFORT_DECEL = {
+  AccelProfile.eco: 0.25,
+  AccelProfile.normal: 0.30,
+  AccelProfile.sport: 0.35,
+}
+
+ACCEL_PROFILE_MAX_BP = [0.0, 3.0, 10.0, 25.0, 40.0]
+ACCEL_PROFILE_MAX_V = {
+  AccelProfile.eco: [1.65, 1.30, 0.72, 0.32, 0.16],
+  AccelProfile.normal: [1.80, 1.50, 0.97, 0.48, 0.30],
+  AccelProfile.sport: [2.00, 1.90, 1.15, 0.68, 0.42],
+}
+
+CAP_FILTER_FRAMES = 5
+LEAD_LOSS_HOLD_TIME = 0.50
+LEAD_DROPOUT_COAST_TIME = 1.50
+LEAD_SWITCH_MAX_HOLD_TIME = 6.0
+SPEED_RESTRICT_DEADBAND = 0.15
+SPEED_RELIEF_DEADBAND = 0.35
+TARGET_RELEASE_SLEW = 8.75
+TARGET_SPEED_ARM_MARGIN = 1.0
+TARGET_SPEED_RESERVE = 0.10
+LAUNCH_TARGET_HEADROOM = 3.0
+LAUNCH_TARGET_SLEW = 8.75
+LAUNCH_END_SPEED = 3.0
+ACCEL_LIMIT_HORIZON_JERK = 1.0
+LEAD_MATCH_GAP_GAIN = 0.04
+LEAD_MATCH_SPEED_HEADROOM = 1.25
+LEAD_MATCH_ACCEL_SLEW = 0.25
+MATCHED_SPEED_DECEL_RATE = 0.50
+PLANNER_BRAKING_ACCEL_THRESHOLD = -0.11
+LEAD_BRAKING_ACCEL_THRESHOLD = -0.11
+MPC_DECEL_JERK_COST_MULTIPLIER = 1.05
+MPC_DECEL_JERK_MAX_REQUIRED_DECEL = 0.80
+MPC_DECEL_JERK_MAX_REQUIRED_DECEL_RATE = 0.35
+MPC_DECEL_JERK_MAX_TARGET_REDUCTION = 9.0
+MPC_DECEL_TREND_FRAMES = 4
+
+STOP_HOLD_EGO_SPEED = 0.30
+STOPPED_LEAD_SPEED = 0.30
+STOP_HOLD_EXIT_SPEED = 0.80
+STOP_HOLD_EXIT_FRAMES = 4
+STOP_HOLD_CREEP_SPEED = 0.15
+STOP_HOLD_CREEP_DISTANCE = 0.30
+DEPARTURE_MOTION_NOISE_FLOOR = 0.03
+DEPARTURE_MOTION_STEP_MIN = 0.005
+STOP_HOLD_MAX_LEAD_DISTANCE = 30.0
+STOP_GAP_RESERVE = 0.75
+STOP_GAP_RESERVE_LEAD_SPEED = 2.0
+STOP_GAP_RESERVE_DECEL_BP = (0.30, 0.80)
+
+RADAR_STALE_TIMEOUT = 0.50
+MAX_LEAD_ACCEL_TAU = 10.0
+MIN_LEAD_SPEED = -1.0
+VEGO_NOISE_TOLERANCE = 0.10
+PARAM_READ_INTERVAL = 0.25
+
+
+def sanitize_profile(profile: int) -> int:
+  return profile if profile in ACCEL_PROFILES else AccelProfile.normal
+
+
+def profile_accel_max(profile: int, v_ego: float) -> float:
+  if not math.isfinite(v_ego):
+    return math.nan
+  return float(np.interp(max(v_ego, 0.0), ACCEL_PROFILE_MAX_BP, ACCEL_PROFILE_MAX_V[sanitize_profile(profile)]))
@@ -0,0 +1,22 @@
+import math
+
+import numpy as np
+
+from opendbc.car.interfaces import ACCEL_MIN, ACCEL_MAX
+from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import T_IDXS
+from openpilot.sunnypilot.selfdrive.controls.lib.accel_controller.constants import ACCEL_LIMIT_HORIZON_JERK, VEGO_NOISE_TOLERANCE
+
+
+def is_valid_context(base_speed: float, v_ego: float, a_ego: float, planner_speed: float, planner_accel: float, stock_accel_max: float,
+                     delay: float, engaged: bool, cruise_initialized: bool) -> bool:
+  values = (base_speed, v_ego, a_ego, planner_speed, planner_accel, stock_accel_max, delay)
+  return (engaged and cruise_initialized and base_speed >= 0.0 and v_ego >= -VEGO_NOISE_TOLERANCE
+          and planner_speed >= 0.0 and stock_accel_max >= 0.0 and delay >= 0.0 and all(math.isfinite(value) for value in values))
+
+
+def build_accel_ceiling(limit: float, planner_accel: float) -> tuple[float, ...] | None:
+  if limit >= ACCEL_MAX - 1e-9:
+    return None
+  a0 = float(np.clip(planner_accel, ACCEL_MIN, ACCEL_MAX))
+  ceiling = np.clip(np.maximum(limit, a0 - ACCEL_LIMIT_HORIZON_JERK * T_IDXS), 0.0, ACCEL_MAX)
+  return tuple(float(value) for value in ceiling)
@@ -0,0 +1,147 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+
+import math
+from typing import NamedTuple
+
+import numpy as np
+
+from openpilot.cereal import log
+from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import (
+  LongitudinalMpc, LongitudinalPlanSource, STOP_DISTANCE, T_IDXS, get_T_FOLLOW, get_stopped_equivalence_factor,
+)
+from openpilot.selfdrive.controls.radard import _LEAD_ACCEL_TAU
+from openpilot.sunnypilot.selfdrive.controls.lib.accel_controller.constants import (
+  COMFORT_DECEL, MAX_LEAD_ACCEL_TAU, MIN_LEAD_SPEED, STOP_GAP_RESERVE, STOP_GAP_RESERVE_DECEL_BP,
+  STOP_GAP_RESERVE_LEAD_SPEED, STOPPED_LEAD_SPEED, sanitize_profile,
+)
+
+
+class LeadPlan(NamedTuple):
+  cap: float = math.inf
+  selected_lead: int = -1
+  selected_lead_track_id: int = -1
+  selected_lead_speed: float = math.inf
+  selected_lead_accel: float = 0.0
+  departure_lead_index: int = -1
+  departure_lead_speed: float = math.inf
+  departure_cap: float = math.inf
+  departure_lead_speeds: tuple[float, float] = (math.inf, math.inf)
+  departure_lead_distances: tuple[float, float] = (-math.inf, -math.inf)
+  departure_lead_track_ids: tuple[int, int] = (-1, -1)
+  departure_lead_separations: tuple[float, float] = (-math.inf, -math.inf)
+  usable_gap: float = math.inf
+  closing_speed: float = 0.0
+  required_decel: float = 0.0
+  has_nearly_stopped_lead: bool = False
+  lead_status: bool = False
+
+
+def is_lead_source(source) -> bool:
+  return source in (LongitudinalPlanSource.lead0, LongitudinalPlanSource.lead1)
+
+
+def has_radar_lead(radar_state) -> bool:
+  return bool(radar_state.leadOne.present or radar_state.leadTwo.present)
+
+
+def _project_ego(v_ego: float, a_ego: float, delay: float) -> tuple[float, float]:
+  if a_ego < 0.0:
+    stop_time = -v_ego / a_ego if v_ego > 0.0 else 0.0
+    if stop_time <= delay:
+      distance = -v_ego**2 / (2.0 * a_ego) if v_ego > 0.0 else 0.0
+      return distance, 0.0
+  return max(v_ego * delay + 0.5 * a_ego * delay**2, 0.0), max(v_ego + a_ego * delay, 0.0)
+
+
+def _lead_values(lead) -> tuple[float, float, float, float] | None:
+  if not lead.present:
+    return None
+  d_rel, v_lead = float(lead.dRel), float(lead.vLeadK)
+  if not math.isfinite(d_rel) or d_rel < 0.0 or not math.isfinite(v_lead) or v_lead < MIN_LEAD_SPEED:
+    return None
+
+  a_lead = float(lead.aLeadK)
+  if not math.isfinite(a_lead):
+    a_lead = 0.0
+  a_lead_tau = float(lead.aLeadTau)
+  if not math.isfinite(a_lead_tau) or not 0.0 < a_lead_tau <= MAX_LEAD_ACCEL_TAU:
+    a_lead_tau = _LEAD_ACCEL_TAU
+  return d_rel, max(v_lead, 0.0), float(np.clip(a_lead, -10.0, 5.0)), a_lead_tau
+
+
+def calculate_lead_plan(radar_state, v_ego: float, a_ego: float, delay: float, profile: int,
+                        follow_personality=log.LongitudinalPersonality.standard) -> LeadPlan:
+  if not all(math.isfinite(value) for value in (v_ego, a_ego, delay)) or v_ego < 0.0 or delay < 0.0:
+    return LeadPlan()
+
+  leads = (radar_state.leadOne, radar_state.leadTwo)
+  lead_status = any(lead.present for lead in leads)
+  t_follow = get_T_FOLLOW(follow_personality)
+  if not math.isfinite(t_follow) or t_follow < 0.0:
+    return LeadPlan(lead_status=lead_status)
+
+  profile = sanitize_profile(profile)
+  x_ego, v_ego_delay = _project_ego(v_ego, a_ego, delay)
+  comfort_decel = COMFORT_DECEL[profile]
+  candidates: list[LeadPlan] = []
+  departure_candidates: list[tuple[float, int]] = []
+  departure_speeds = [math.inf, math.inf]
+  departure_distances = [-math.inf, -math.inf]
+  departure_track_ids = [-1, -1]
+  departure_separations = [-math.inf, -math.inf]
+  departure_caps = [math.inf, math.inf]
+
+  for lead_index, lead in enumerate(leads):
+    values = _lead_values(lead)
+    if values is None:
+      continue
+
+    d_rel, v_lead, a_lead, a_lead_tau = values
+    lead_xv = LongitudinalMpc.extrapolate_lead(d_rel, v_lead, a_lead, a_lead_tau)
+    x_lead = float(np.interp(delay, T_IDXS, lead_xv[:, 0]))
+    v_lead_delay = float(np.interp(delay, T_IDXS, lead_xv[:, 1]))
+    safety_gap = max(x_lead - x_ego - STOP_DISTANCE - t_follow * v_lead_delay, 0.0)
+    closing_speed = max(v_ego_delay - v_lead_delay, 0.0)
+    required_decel = 0.0 if closing_speed == 0.0 else math.inf if safety_gap == 0.0 else closing_speed**2 / (2.0 * safety_gap)
+    reserve = float(np.interp(v_lead_delay, (0.0, STOP_GAP_RESERVE_LEAD_SPEED), (STOP_GAP_RESERVE, 0.0)))
+    reserve_scale = float(np.interp(required_decel, STOP_GAP_RESERVE_DECEL_BP, (1.0, 0.0)))
+    usable_gap = max(safety_gap - reserve * reserve_scale, 0.0)
+    cap = v_lead_delay + math.sqrt(2.0 * comfort_decel * usable_gap)
+    departure_cap = v_lead_delay + math.sqrt(2.0 * comfort_decel * safety_gap)
+    separation = x_lead - x_ego
+    departure_distance = x_lead + float(get_stopped_equivalence_factor(v_lead_delay))
+
+    finite_values = (x_lead, v_lead_delay, safety_gap, usable_gap, closing_speed, cap, departure_cap, departure_distance)
+    if (not all(math.isfinite(value) and value >= 0.0 for value in finite_values) or math.isnan(required_decel)
+        or required_decel < 0.0 or not math.isfinite(separation)):
+      continue
+
+    track_id = max(int(lead.radarTrackId), -1) if math.isfinite(lead.radarTrackId) else -1
+    candidates.append(LeadPlan(
+      cap=cap, selected_lead=lead_index, selected_lead_track_id=track_id, selected_lead_speed=v_lead_delay, selected_lead_accel=a_lead,
+      usable_gap=usable_gap, closing_speed=closing_speed, required_decel=required_decel, lead_status=lead_status,
+    ))
+    departure_candidates.append((departure_distance, lead_index))
+    departure_speeds[lead_index] = v_lead_delay
+    departure_distances[lead_index] = d_rel
+    departure_track_ids[lead_index] = track_id
+    departure_separations[lead_index] = separation
+    departure_caps[lead_index] = departure_cap
+
+  if not candidates:
+    return LeadPlan(lead_status=lead_status)
+
+  selected = min(candidates, key=lambda candidate: candidate.cap)
+  departure_lead_index = min(departure_candidates, key=lambda candidate: candidate[0])[1]
+  departure_lead_speed = departure_speeds[departure_lead_index]
+  return selected._replace(
+    departure_lead_index=departure_lead_index, departure_lead_speed=departure_lead_speed,
+    departure_cap=departure_caps[departure_lead_index], departure_lead_speeds=tuple(departure_speeds),
+    departure_lead_distances=tuple(departure_distances), departure_lead_track_ids=tuple(departure_track_ids),
+    departure_lead_separations=tuple(departure_separations), has_nearly_stopped_lead=departure_lead_speed < STOPPED_LEAD_SPEED,
+  )
@@ -0,0 +1,191 @@
+import math
+from statistics import median
+
+import numpy as np
+
+from openpilot.cereal import custom
+from openpilot.sunnypilot.selfdrive.controls.lib.accel_controller.constants import (
+  CAP_FILTER_FRAMES, DEPARTURE_MOTION_NOISE_FLOOR, DEPARTURE_MOTION_STEP_MIN, SPEED_RELIEF_DEADBAND, STOP_HOLD_CREEP_DISTANCE,
+  STOP_HOLD_CREEP_SPEED, STOP_HOLD_EXIT_FRAMES,
+)
+from openpilot.sunnypilot.selfdrive.controls.lib.accel_controller.lead import LeadPlan
+
+
+AccelControllerState = custom.LongitudinalPlanSP.AccelController.State
+
+
+class DepartureTracker:
+  def __init__(self) -> None:
+    self.samples: list[list[float]] = [[], []]
+    self.motion_samples: list[float] = []
+    self.references: list[float | None] = [None, None]
+    self.track_ids = [-1, -1]
+
+  def separation(self, lead_index: int) -> float:
+    samples = self.samples[lead_index]
+    return float(median(samples)) if samples else -math.inf
+
+  def update(self, lead_plan: LeadPlan, dt: float) -> None:
+    for lead_index, distance in enumerate(lead_plan.departure_lead_distances):
+      if not math.isfinite(distance):
+        continue
+      samples = self.samples[lead_index]
+      track_id = lead_plan.departure_lead_track_ids[lead_index]
+      identity_changed = bool(samples) and track_id != self.track_ids[lead_index] and (track_id >= 0 or self.track_ids[lead_index] >= 0)
+      max_distance_step = max(STOP_HOLD_CREEP_DISTANCE / 2.0, 3.0 * lead_plan.departure_lead_speeds[lead_index] * dt)
+      geometry_jump = bool(samples) and abs(distance - samples[-1]) > max_distance_step
+      if identity_changed or geometry_jump:
+        samples.clear()
+        self.references[lead_index] = distance
+      samples.append(distance)
+      if len(samples) > CAP_FILTER_FRAMES:
+        samples.pop(0)
+      self.track_ids[lead_index] = track_id
+    lead_index = lead_plan.departure_lead_index
+    if lead_index >= 0:
+      distance = lead_plan.departure_lead_distances[lead_index]
+      samples = self.motion_samples
+      max_distance_step = max(STOP_HOLD_CREEP_DISTANCE / 2.0, 3.0 * lead_plan.departure_lead_speed * dt)
+      if samples and abs(distance - samples[-1]) > max_distance_step:
+        samples.clear()
+      samples.append(distance)
+      if len(samples) > CAP_FILTER_FRAMES:
+        samples.pop(0)
+
+  def seed(self, lead_plan: LeadPlan) -> None:
+    self.samples = [[], []]
+    self.motion_samples = []
+    self.references = [None, None]
+    self.track_ids = list(lead_plan.departure_lead_track_ids)
+    for lead_index, distance in enumerate(lead_plan.departure_lead_distances):
+      if math.isfinite(distance):
+        self.samples[lead_index].append(distance)
+        self.references[lead_index] = distance
+    if lead_plan.departure_lead_index >= 0:
+      self.motion_samples.append(lead_plan.departure_lead_distances[lead_plan.departure_lead_index])
+
+  def progress(self, lead_plan: LeadPlan, minimum_distance: float) -> bool:
+    lead_index = lead_plan.departure_lead_index
+    if lead_index < 0 or lead_plan.departure_lead_speed <= STOP_HOLD_CREEP_SPEED:
+      return False
+    reference = self.references[lead_index]
+    distance = self.separation(lead_index)
+    return reference is not None and distance - reference >= minimum_distance
+
+  def recent_motion(self) -> bool:
+    samples = self.motion_samples[-STOP_HOLD_EXIT_FRAMES:]
+    if len(samples) < STOP_HOLD_EXIT_FRAMES:
+      return False
+    deltas = np.diff(samples)
+    return bool(samples[-1] - samples[0] >= DEPARTURE_MOTION_NOISE_FLOOR and np.count_nonzero(deltas > DEPARTURE_MOTION_STEP_MIN) >= 2)
+
+  def backfill_references(self) -> None:
+    for lead_index in range(len(self.references)):
+      separation = self.separation(lead_index)
+      if math.isfinite(separation) and self.references[lead_index] is None:
+        self.references[lead_index] = separation
+
+  def keep_latest_motion_sample(self) -> None:
+    if self.motion_samples:
+      self.motion_samples = self.motion_samples[-1:]
+
+
+class TargetState:
+  def __init__(self) -> None:
+    self.cap_samples = [math.inf] * CAP_FILTER_FRAMES
+    self.lead_speed_samples = [math.inf] * CAP_FILTER_FRAMES
+    self.lead_accel_samples = [0.0] * CAP_FILTER_FRAMES
+    self.departure = DepartureTracker()
+    self.target_speed: float | None = None
+    self.state = AccelControllerState.inactive
+    self.departure_frames = self.active_frames = self.lead_loss_frames = self.release_settle_frames = 0
+    self.lead_switch_guard_frames = self.lead_switch_elapsed_frames = self.lead_switch_stable_frames = self.stale_frames = 0
+    self.selected_lead = self.selected_lead_track_id = -1
+    self.launching = self.departure_launch = self.matched_lead = self.lead_dropout = self.release_slew_armed = False
+    self.lead_braking = self.e2e_braking_handoff = self.speed_reserve_armed = False
+    self.speed_reserve_suppressed = False
+    self.matched_accel_limit: float | None = None
+    self.release_settle_speed: float | None = None
+
+  def reset_lead_switch_guard(self) -> None:
+    self.lead_switch_guard_frames = self.lead_switch_elapsed_frames = self.lead_switch_stable_frames = 0
+
+  def arm_release_slew(self, force: bool = False) -> None:
+    if self.release_slew_armed:
+      self.release_settle_frames = 0
+      return
+    if not force and self.release_settle_speed is not None and self.target_speed is not None:
+      if self.release_settle_speed - self.target_speed < SPEED_RELIEF_DEADBAND:
+        return
+    self.release_slew_armed = True
+    self.release_settle_frames = 0
+    self.release_settle_speed = None
+
+  def reset_release_slew(self, settled_speed: float | None = None) -> None:
+    self.release_slew_armed = False
+    self.release_settle_frames = 0
+    self.release_settle_speed = settled_speed
+
+  def update_release_slew(self, ceiling: float, settled: bool) -> None:
+    if not self.release_slew_armed:
+      return
+    if not settled:
+      self.release_settle_frames = 0
+    elif self.release_settle_speed is None or ceiling > self.release_settle_speed:
+      self.release_settle_frames = 1
+      self.release_settle_speed = ceiling
+    else:
+      self.release_settle_frames += 1
+    if self.release_settle_frames >= CAP_FILTER_FRAMES:
+      self.release_slew_armed = False
+      self.release_settle_frames = 0
+
+  def update_lead_switch_guard(self, arm: bool, confirmed: bool, unstable: bool, hold_frames: int, max_frames: int) -> None:
+    if self.lead_switch_elapsed_frames > 0:
+      self.lead_switch_stable_frames = 0 if unstable else self.lead_switch_stable_frames + 1
+      if self.lead_switch_guard_frames == 0 and self.lead_switch_stable_frames >= hold_frames:
+        self.reset_lead_switch_guard()
+    if arm and self.lead_switch_elapsed_frames == 0:
+      self.lead_switch_guard_frames, self.lead_switch_elapsed_frames = hold_frames, 1
+    elif self.lead_switch_guard_frames > 0:
+      self.lead_switch_elapsed_frames += 1
+      if self.lead_switch_elapsed_frames >= max_frames:
+        self.lead_switch_guard_frames = 0
+      else:
+        self.lead_switch_guard_frames = self.lead_switch_guard_frames - 1 if confirmed else hold_frames
+
+  @property
+  def filtered_cap(self) -> float:
+    return sorted(self.cap_samples)[CAP_FILTER_FRAMES // 2]
+
+  @property
+  def filtered_lead_speed(self) -> float:
+    return sorted(self.lead_speed_samples)[CAP_FILTER_FRAMES // 2]
+
+  @property
+  def filtered_lead_accel(self) -> float:
+    return sorted(self.lead_accel_samples)[CAP_FILTER_FRAMES // 2]
+
+  def update_samples(self, lead_plan: LeadPlan, dt: float) -> bool:
+    had_filtered_lead = math.isfinite(self.filtered_cap)
+    has_lead = lead_plan.selected_lead >= 0
+    self.cap_samples.append(lead_plan.cap if has_lead else math.inf)
+    self.lead_speed_samples.append(lead_plan.selected_lead_speed if has_lead else math.inf)
+    self.lead_accel_samples.append(lead_plan.selected_lead_accel if has_lead else 0.0)
+    self.cap_samples.pop(0)
+    self.lead_speed_samples.pop(0)
+    self.lead_accel_samples.pop(0)
+    self.lead_loss_frames = 0 if has_lead else self.lead_loss_frames + 1
+    self.departure.update(lead_plan, dt)
+    return not had_filtered_lead and math.isfinite(self.filtered_cap)
+
+  def enter_stop_hold(self, lead_plan: LeadPlan) -> None:
+    self.departure.seed(lead_plan)
+    self.target_speed = 0.0
+    self.state = AccelControllerState.stopHold
+    self.departure_frames = 0
+    self.launching = self.departure_launch = False
+    self.reset_release_slew()
+    self.matched_lead = self.speed_reserve_armed = self.speed_reserve_suppressed = False
+    self.matched_accel_limit = None
+    self.reset_lead_switch_guard()
@@ -0,0 +1,544 @@
+import inspect
+import math
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+
+from openpilot.cereal import custom, log, messaging
+from opendbc.car.interfaces import ACCEL_MAX, ACCEL_MIN
+from openpilot.common.realtime import DT_MDL
+from openpilot.selfdrive.controls.lib.longitudinal_planner import LongitudinalPlanner
+from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import N, LongitudinalMpc
+from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import LongitudinalPlanSource as MpcLongitudinalPlanSource
+from openpilot.sunnypilot.selfdrive.controls.lib.accel_controller.accel_controller import AccelController, AccelControllerState
+from openpilot.sunnypilot.selfdrive.controls.lib.accel_controller.constants import (
+  MPC_DECEL_JERK_COST_MULTIPLIER, MPC_DECEL_JERK_MAX_REQUIRED_DECEL, MPC_DECEL_JERK_MAX_TARGET_REDUCTION, AccelProfile,
+)
+from openpilot.sunnypilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import LongitudinalMpcSP
+from openpilot.sunnypilot.selfdrive.controls.lib.longitudinal_planner import LongitudinalPlannerSP, LongitudinalPlanSource
+
+
+def radar_state():
+  return messaging.new_message("radarState").radarState
+
+
+class PlannerSM(dict):
+  def __init__(self, radar_log_mono_time: int):
+    super().__init__(
+      radarState=radar_state(),
+      carState=SimpleNamespace(vEgo=10.0, aEgo=0.0, vCruise=20.0),
+      selfdriveState=SimpleNamespace(personality=0),
+      controlsState=SimpleNamespace(forceDecel=False),
+    )
+    self.valid = {"radarState": True}
+    self.alive = {"radarState": True}
+    self.logMonoTime = {"radarState": radar_log_mono_time}
+
+
+class ControllerStub:
+  def __init__(self, *, target_speed=15.0, active=True, mpc_accel_max=None, cruise_accel_max=None,
+               state=AccelControllerState.free, selected_lead=-1,
+               selected_lead_track_id=-1, launching=False, departure_launching=False, required_decel=0.0):
+    self.available = self.enabled = True
+    self.profile = AccelProfile.normal
+    self.output_v_target = target_speed
+    self.is_active = active
+    self.mpc_accel_max = mpc_accel_max
+    self.cruise_accel_max = cruise_accel_max
+    self.state = state
+    self.selected_lead = selected_lead
+    self.selected_lead_track_id = selected_lead_track_id
+    self.launching = launching
+    self.departure_launching = departure_launching
+    self.required_decel = required_decel
+    self.dt = DT_MDL
+    self._jerk_smoothing_blocked = False
+    self._required_decel_samples = []
+    self._required_decel_lead = -1
+    self._required_decel_lead_track_id = -1
+    self._lead_trend_warmup = False
+    self.update_kwargs = None
+    self.reset_calls = 0
+
+  def update(self, _radar_state, **kwargs):
+    self.update_kwargs = kwargs
+
+  @property
+  def is_enabled(self):
+    return self.available and self.enabled
+
+  def update_params(self):
+    pass
+
+  def reset(self):
+    self.reset_calls += 1
+
+  def get_jerk_cost_multiplier(self, *args):
+    return AccelController.get_jerk_cost_multiplier(self, *args)
+
+  def update_should_stop(self, should_stop):
+    return AccelController.update_should_stop(self, should_stop)
+
+
+def planner_for_mpc_test(*, target_speed=15.0, active=True, is_e2e=False, mpc_accel_max=None,
+                         cruise_accel_max=None,
+                         state=AccelControllerState.free, selected_lead=-1, launching=False,
+                         departure_launching=False, required_decel=0.0,
+                         mpc_source=MpcLongitudinalPlanSource.lead0):
+  planner = LongitudinalPlannerSP.__new__(LongitudinalPlannerSP)
+  is_e2e_calls = []
+  planner.is_e2e = lambda _sm: is_e2e_calls.append(True) or is_e2e
+  planner.output_v_target = 20.0
+  planner.output_should_stop = False
+  planner.allow_throttle = True
+  planner.a_desired = 0.0
+  planner.v_desired_filter = SimpleNamespace(x=10.0)
+  planner._radar_fresh_this_cycle = True
+  planner.mpc = SimpleNamespace(source=mpc_source, last_solution_status=0)
+  planner.accel_controller = ControllerStub(
+    target_speed=target_speed, active=active, state=state, selected_lead=selected_lead, launching=launching,
+    departure_launching=departure_launching, required_decel=required_decel, mpc_accel_max=mpc_accel_max,
+    cruise_accel_max=cruise_accel_max,
+  )
+  return planner, is_e2e_calls
+
+
+def prepare_controller_mpc(planner, *, mpc_v_cruise=20.0, force_decel=False):
+  configs = []
+  sm = {
+    "radarState": radar_state(),
+    "controlsState": SimpleNamespace(forceDecel=force_decel),
+    "carState": SimpleNamespace(vCruise=20.0, vEgo=10.0, aEgo=0.0),
+    "selfdriveState": SimpleNamespace(personality=0),
+  }
+  planner.mpc.set_accel_controller_params = lambda *args: configs.append(args)
+  is_e2e, target = planner.update_accel_controller(sm, mpc_v_cruise, True, ACCEL_MAX, False)
+  assert len(configs) == 1
+  return is_e2e, target, configs[0]
+
+
+def test_accel_controller_schema_contract():
+  expected = {"eco": 0, "normal": 1, "sport": 2}
+  state = {"inactive": 0, "free": 1, "restrict": 2, "hold": 3, "release": 4, "stopHold": 5}
+  accel_controller = custom.LongitudinalPlanSP.schema.fields["accelController"]
+  fields = custom.LongitudinalPlanSP.AccelController.schema.fields
+
+  assert accel_controller.proto.ordinal.explicit == 8
+  assert {name: field.proto.ordinal.explicit for name, field in fields.items()} == {
+    "enabled": 0, "active": 1, "shadowOnlyDEPRECATED": 2, "profile": 3, "state": 4,
+  }
+  assert fields["shadowOnlyDEPRECATED"].proto.slot.type.which() == "bool"
+  assert custom.LongitudinalPlanSP.AccelerationPersonality.schema.enumerants == expected
+  assert custom.LongitudinalPlanSP.AccelController.Profile.schema.enumerants == expected
+  assert custom.LongitudinalPlanSP.AccelController.State.schema.enumerants == state
+
+
+def test_accel_controller_schema_round_trip_and_toyota_compatibility():
+  message = custom.LongitudinalPlanSP.new_message()
+  message.accelController.enabled = True
+  message.accelController.active = True
+  message.accelController.profile = custom.LongitudinalPlanSP.AccelController.Profile.sport
+  message.accelController.state = custom.LongitudinalPlanSP.AccelController.State.release
+
+  with custom.LongitudinalPlanSP.from_bytes(message.to_bytes()) as reader:
+    assert reader.accelController.enabled and reader.accelController.active
+    assert reader.accelController.profile == custom.LongitudinalPlanSP.AccelController.Profile.sport
+    assert reader.accelController.state == custom.LongitudinalPlanSP.AccelController.State.release
+
+  from opendbc.car.toyota.carstate import AccelPersonality, CarState
+
+  assert AccelPersonality.schema.enumerants == {"eco": 0, "normal": 1, "sport": 2}
+  assert CarState.__module__ == "opendbc.car.toyota.carstate"
+
+
+def test_longitudinal_planner_sp_owns_accel_controller_integration():
+  assert "update_accel_controller" in LongitudinalPlannerSP.__dict__
+  assert "update_should_stop" in LongitudinalPlannerSP.__dict__
+
+
+def test_mpc_inherits_accel_controller_extension_without_changing_stock_signature_or_bounds():
+  assert LongitudinalMpc.__bases__ == (LongitudinalMpcSP,)
+  assert tuple(inspect.signature(LongitudinalMpc.update).parameters) == ("self", "radarstate", "v_cruise", "personality")
+  mpc = LongitudinalMpc()
+  radar = radar_state()
+  mpc.run = lambda: None
+
+  mpc.set_cur_state(10.0, 0.8)
+  mpc.update(radar, 30.0)
+  np.testing.assert_array_equal(mpc.params[:, 0], ACCEL_MIN)
+  np.testing.assert_array_equal(mpc.params[:, 1], ACCEL_MAX)
+  assert mpc.cruise_accel_max(1.6) == 1.6
+
+  mpc.set_accel_controller_params(None, 1.0, 0.4)
+  assert mpc.cruise_accel_max(1.6) == 0.4
+
+  requested_ceiling = tuple(np.full(N + 1, 0.4))
+  mpc.set_accel_controller_params(requested_ceiling, 1.0)
+  mpc.update(radar, 30.0)
+  np.testing.assert_array_equal(mpc.params[:, 0], ACCEL_MIN)
+  assert mpc.params[0, 1] == pytest.approx(0.8)
+  np.testing.assert_array_equal(mpc.params[1:, 1], requested_ceiling[1:])
+
+  for malformed_ceiling in ("bad", [0.4] * N, np.full(N + 1, math.nan), [10**10000] * (N + 1)):
+    mpc.set_accel_controller_params(malformed_ceiling, 1.0)
+    mpc.update(radar, 30.0)
+    np.testing.assert_array_equal(mpc.params[:, 0], ACCEL_MIN)
+    np.testing.assert_array_equal(mpc.params[:, 1], ACCEL_MAX)
+
+  mpc.set_accel_controller_params(None, 1.0)
+  mpc.update(radar, 30.0)
+  np.testing.assert_array_equal(mpc.params[:, 1], ACCEL_MAX)
+
+
+def test_mpc_jerk_cost_multiplier_is_backward_compatible_and_does_not_change_other_costs():
+  mpc = LongitudinalMpc.__new__(LongitudinalMpc)
+  LongitudinalMpcSP.__init__(mpc)
+  captured = []
+  mpc.set_cost_weights = lambda costs, constraints: captured.append((np.asarray(costs), np.asarray(constraints)))
+
+  mpc.set_weights(True, personality=log.LongitudinalPersonality.standard)
+  default_costs, default_constraints = captured[-1]
+  mpc.set_accel_controller_params(None, 1.0)
+  mpc.set_weights(True, personality=log.LongitudinalPersonality.standard)
+  explicit_costs, explicit_constraints = captured[-1]
+  mpc.set_accel_controller_params(None, 1.2)
+  mpc.set_weights(True, personality=log.LongitudinalPersonality.standard)
+  smoothed_costs, smoothed_constraints = captured[-1]
+
+  np.testing.assert_array_equal(explicit_costs, default_costs)
+  np.testing.assert_array_equal(explicit_constraints, default_constraints)
+  np.testing.assert_array_equal(smoothed_costs[:-1], default_costs[:-1])
+  assert smoothed_costs[-1] == pytest.approx(default_costs[-1] * 1.2)
+  np.testing.assert_array_equal(smoothed_constraints, default_constraints)
+
+  mpc.set_weights(False, personality=log.LongitudinalPersonality.standard)
+  assert captured[-1][0][-2] == 0.0
+  assert captured[-1][0][-1] == pytest.approx(default_costs[-1] * 1.2)
+
+
+def test_stock_planner_owns_mpc_solve():
+  source = inspect.getsource(LongitudinalPlanner.update)
+  assert source.count("self.mpc.set_weights(") == 1
+  assert source.count("self.mpc.set_cur_state(") == 1
+  assert source.count("self.mpc.update(") == 1
+
+
+def test_accel_controller_hook_only_configures_mpc():
+  radar = radar_state()
+  planner, _ = planner_for_mpc_test(active=False)
+  calls = []
+  planner.mpc = SimpleNamespace(
+    source=MpcLongitudinalPlanSource.cruise,
+    last_solution_status=0,
+    set_accel_controller_params=lambda accel_max, multiplier, cruise_accel_max: calls.append(
+      ("configure", accel_max, multiplier, cruise_accel_max)),
+    set_weights=lambda constraint, personality: calls.append(("weights", constraint, personality)),
+    set_cur_state=lambda speed, accel: calls.append(("state", speed, accel)),
+    update=lambda radar_arg, target, *, personality: calls.append(("update", radar_arg, target, personality)),
+  )
+  sm = {
+    "radarState": radar,
+    "controlsState": SimpleNamespace(forceDecel=False),
+    "carState": SimpleNamespace(vCruise=20.0, vEgo=10.0, aEgo=0.0),
+    "selfdriveState": SimpleNamespace(personality=2),
+  }
+  is_e2e, target = planner.update_accel_controller(sm, 17.5, True, ACCEL_MAX, False)
+
+  assert not is_e2e and target == 17.5
+  assert calls == [("configure", None, 1.0, None)]
+
+
+def test_active_acc_uses_target_and_ceiling_in_exactly_one_solve():
+  ceiling = tuple(np.linspace(0.8, 0.4, N + 1))
+  planner, mode_calls = planner_for_mpc_test(mpc_accel_max=ceiling)
+  is_e2e, target, config = prepare_controller_mpc(planner)
+
+  assert not is_e2e
+  assert len(mode_calls) == 1
+  assert target == 15.0
+  assert config == (ceiling, 1.0, None)
+
+
+def test_cruise_accel_ceiling_is_forwarded_to_mpc():
+  planner, _ = planner_for_mpc_test(cruise_accel_max=0.3)
+  _, _, config = prepare_controller_mpc(planner)
+  assert config == (None, 1.0, 0.3)
+
+
+def test_valid_lead_stop_hold_preplans_from_raw_target_without_an_accel_ceiling():
+  planner, _ = planner_for_mpc_test(
+    target_speed=0.0, mpc_accel_max=None, state=AccelControllerState.stopHold, selected_lead=0,
+  )
+  _, target, config = prepare_controller_mpc(planner)
+
+  assert target == 20.0
+  assert config == (None, 1.0, None)
+
+
+def test_missing_lead_stop_hold_keeps_zero_mpc_target_without_an_accel_ceiling():
+  planner, _ = planner_for_mpc_test(
+    target_speed=0.0, mpc_accel_max=None, state=AccelControllerState.stopHold, selected_lead=-1,
+  )
+  _, target, config = prepare_controller_mpc(planner)
+
+  assert target == 0.0
+  assert config == (None, 1.0, None)
+
+
+@pytest.mark.parametrize(
+  ("active", "departure_launching", "expected"),
+  [
+    (True, True, False),
+    (True, False, True),
+    (False, True, True),
+  ],
+)
+def test_only_confirmed_live_acc_departure_clears_should_stop(active, departure_launching, expected):
+  planner = LongitudinalPlannerSP.__new__(LongitudinalPlannerSP)
+  planner.accel_controller = ControllerStub(active=active, departure_launching=departure_launching, state=AccelControllerState.stopHold)
+  assert planner.update_should_stop(True) is expected
+  assert planner.update_should_stop(False) is (active and not departure_launching)
+
+
+@pytest.mark.parametrize(("active", "is_e2e"), [(False, False), (True, True)])
+def test_disabled_or_e2e_is_an_exact_mpc_bypass(active, is_e2e):
+  ceiling = tuple(np.linspace(0.8, 0.4, N + 1))
+  planner, mode_calls = planner_for_mpc_test(active=active, is_e2e=is_e2e, mpc_accel_max=ceiling)
+  returned_e2e, target, config = prepare_controller_mpc(planner)
+
+  assert returned_e2e is is_e2e
+  assert len(mode_calls) == 1
+  assert target == 20.0
+  assert config == (None, 1.0, None)
+
+
+def test_force_decel_target_remains_authoritative_and_disables_ceiling():
+  ceiling = tuple(np.linspace(0.8, 0.4, N + 1))
+  planner, mode_calls = planner_for_mpc_test(mpc_accel_max=ceiling)
+  _, target, config = prepare_controller_mpc(planner, mpc_v_cruise=0.0, force_decel=True)
+
+  assert len(mode_calls) == 1
+  assert target == 0.0
+  assert config == (None, 1.0, None)
+
+
+def test_previous_mpc_failure_gets_one_stock_recovery_cycle():
+  ceiling = tuple(np.linspace(0.8, 0.4, N + 1))
+  planner, mode_calls = planner_for_mpc_test(mpc_accel_max=ceiling)
+  controller = planner.accel_controller
+  planner.mpc.last_solution_status = 4
+
+  _, failed_target, failed_config = prepare_controller_mpc(planner)
+  assert controller.reset_calls == 1
+  assert len(mode_calls) == 1
+  assert failed_target == 20.0
+  assert failed_config == (None, 1.0, None)
+
+  planner.mpc.last_solution_status = 0
+  _, recovered_target, recovered_config = prepare_controller_mpc(planner)
+  assert controller.reset_calls == 1
+  assert len(mode_calls) == 2
+  assert recovered_target == 15.0
+  assert recovered_config == (ceiling, 1.0, None)
+
+
+@pytest.mark.parametrize(
+  "mpc_source",
+  (MpcLongitudinalPlanSource.cruise, MpcLongitudinalPlanSource.lead0, MpcLongitudinalPlanSource.lead1),
+)
+def test_routine_governor_restriction_forwards_the_jerk_cost_multiplier(mpc_source):
+  planner, _ = planner_for_mpc_test(
+    state=AccelControllerState.restrict, selected_lead=0, required_decel=0.30,
+    mpc_source=mpc_source,
+  )
+  _, target, config = prepare_controller_mpc(planner)
+
+  assert target == 15.0
+  assert config == (None, MPC_DECEL_JERK_COST_MULTIPLIER, None)
+
+
+def test_ineligible_required_decel_blocks_smoothing_only_until_the_restriction_episode_ends():
+  planner, _ = planner_for_mpc_test(
+    state=AccelControllerState.restrict, selected_lead=0, required_decel=0.30,
+  )
+  _, _, initial_config = prepare_controller_mpc(planner)
+  controller = planner.accel_controller
+  assert initial_config[1] == MPC_DECEL_JERK_COST_MULTIPLIER
+
+  controller.required_decel = MPC_DECEL_JERK_MAX_REQUIRED_DECEL
+  _, _, ineligible_config = prepare_controller_mpc(planner)
+  assert ineligible_config[1] == 1.0
+
+  controller.required_decel = 0.30
+  _, _, flicker_config = prepare_controller_mpc(planner)
+  assert flicker_config[1] == 1.0
+
+  controller.state = AccelControllerState.free
+  controller.output_v_target = 20.0
+  prepare_controller_mpc(planner)
+  controller.state = AccelControllerState.restrict
+  controller.output_v_target = 15.0
+  _, _, rearmed_config = prepare_controller_mpc(planner)
+  assert rearmed_config[1] == MPC_DECEL_JERK_COST_MULTIPLIER
+
+
+def test_consistently_tightening_lead_releases_smoothing_until_the_restriction_ends():
+  planner, _ = planner_for_mpc_test(
+    state=AccelControllerState.restrict, selected_lead=0, required_decel=0.18,
+  )
+  _, _, config = prepare_controller_mpc(planner)
+  controller = planner.accel_controller
+  multipliers = [config[1]]
+  for required_decel in (0.20, 0.23, 0.25):
+    controller.required_decel = required_decel
+    _, _, config = prepare_controller_mpc(planner)
+    multipliers.append(config[1])
+
+  assert multipliers == [MPC_DECEL_JERK_COST_MULTIPLIER] * 3 + [1.0]
+
+  controller.required_decel = 0.20
+  _, _, config = prepare_controller_mpc(planner)
+  assert config[1] == 1.0
+
+  controller.state = AccelControllerState.free
+  controller.output_v_target = 20.0
+  prepare_controller_mpc(planner)
+  controller.state = AccelControllerState.restrict
+  controller.output_v_target = 15.0
+  controller.required_decel = 0.18
+  _, _, config = prepare_controller_mpc(planner)
+  assert config[1] == MPC_DECEL_JERK_COST_MULTIPLIER
+
+
+def test_one_frame_required_decel_noise_does_not_disable_routine_smoothing():
+  planner, _ = planner_for_mpc_test(
+    state=AccelControllerState.restrict, selected_lead=0, required_decel=0.18,
+  )
+  _, _, config = prepare_controller_mpc(planner)
+  controller = planner.accel_controller
+  multipliers = [config[1]]
+  for required_decel in (0.24, 0.19, 0.22):
+    controller.required_decel = required_decel
+    _, _, config = prepare_controller_mpc(planner)
+    multipliers.append(config[1])
+
+  assert multipliers == [MPC_DECEL_JERK_COST_MULTIPLIER] * 4
+
+
+@pytest.mark.parametrize(
+  ("state", "selected_lead", "launching", "required_decel", "target_speed", "mpc_source"),
+  [
+    (AccelControllerState.free, 0, False, 0.30, 15.0, MpcLongitudinalPlanSource.cruise),
+    (AccelControllerState.hold, 0, False, 0.30, 15.0, MpcLongitudinalPlanSource.cruise),
+    (AccelControllerState.stopHold, 0, False, 0.30, 15.0, MpcLongitudinalPlanSource.cruise),
+    (AccelControllerState.restrict, -1, False, 0.30, 15.0, MpcLongitudinalPlanSource.cruise),
+    (AccelControllerState.restrict, 0, True, 0.30, 15.0, MpcLongitudinalPlanSource.cruise),
+    (AccelControllerState.restrict, 0, False, MPC_DECEL_JERK_MAX_REQUIRED_DECEL, 15.0, MpcLongitudinalPlanSource.cruise),
+    (AccelControllerState.restrict, 0, False, math.inf, 15.0, MpcLongitudinalPlanSource.cruise),
+    (AccelControllerState.restrict, 0, False, math.nan, 15.0, MpcLongitudinalPlanSource.cruise),
+    (AccelControllerState.restrict, 0, False, 0.0, 15.0, MpcLongitudinalPlanSource.cruise),
+    (AccelControllerState.restrict, 0, False, -0.01, 15.0, MpcLongitudinalPlanSource.cruise),
+    (AccelControllerState.restrict, 0, False, 0.30, 20.0 - MPC_DECEL_JERK_MAX_TARGET_REDUCTION, MpcLongitudinalPlanSource.cruise),
+    (AccelControllerState.restrict, 0, False, 0.30, 20.0, MpcLongitudinalPlanSource.cruise),
+    (AccelControllerState.restrict, 0, False, 0.30, 25.0, MpcLongitudinalPlanSource.cruise),
+  ],
+)
+def test_non_routine_or_stock_lead_states_keep_stock_jerk_cost(
+  state, selected_lead, launching, required_decel, target_speed, mpc_source,
+):
+  planner, _ = planner_for_mpc_test(
+    state=state, selected_lead=selected_lead, launching=launching,
+    required_decel=required_decel, target_speed=target_speed, mpc_source=mpc_source,
+  )
+  _, _, config = prepare_controller_mpc(planner)
+
+  assert config[1] == 1.0
+
+
+def test_controller_receives_previous_mpc_state_and_cached_radar_freshness():
+  planner, _ = planner_for_mpc_test(mpc_source=log.LongitudinalPlan.LongitudinalPlanSource.lead0)
+  planner._radar_fresh_this_cycle = True
+  planner.a_desired = -0.4
+  planner.v_desired_filter = SimpleNamespace(x=9.5)
+  prepare_controller_mpc(planner)
+  received = planner.accel_controller.update_kwargs
+
+  assert received["previous_mpc_source"] == log.LongitudinalPlan.LongitudinalPlanSource.lead0
+  assert received["planner_speed"] == 9.5
+  assert received["planner_accel"] == -0.4
+  assert received["radar_fresh"] is True
+
+
+def test_controller_is_disabled_when_openpilot_longitudinal_control_is_unavailable():
+  controller = AccelController(SimpleNamespace(longitudinalActuatorDelay=0.1, openpilotLongitudinalControl=False))
+  controller.enabled = True
+  assert not controller.is_enabled
+
+
+def test_radar_freshness_is_computed_once_and_shared_with_dec_and_controller():
+  planner = LongitudinalPlannerSP.__new__(LongitudinalPlannerSP)
+  planner._radar_log_mono_time = None
+  planner._radar_fresh_this_cycle = True
+  planner.events_sp = SimpleNamespace(clear=lambda: None)
+  dec_freshness = []
+  planner.dec = SimpleNamespace(update=lambda _sm, *, radar_fresh, planner_accel: dec_freshness.append(radar_fresh))
+  planner.e2e_alerts_helper = SimpleNamespace(update=lambda *_args: None)
+  planner.output_a_target = 0.0
+  planner.output_v_target = 20.0
+  planner.output_should_stop = False
+  planner.allow_throttle = True
+  planner.a_desired = 0.0
+  planner.v_desired_filter = SimpleNamespace(x=10.0)
+  planner.mpc = SimpleNamespace(
+    source=log.LongitudinalPlan.LongitudinalPlanSource.cruise, last_solution_status=0,
+    set_accel_controller_params=lambda *_args: None,
+  )
+  planner.is_e2e = lambda _sm: False
+  planner.accel_controller = ControllerStub(target_speed=20.0, active=False)
+
+  sm = PlannerSM(100)
+  for expected in (True, False):
+    planner.update(sm)
+    planner.update_accel_controller(sm, 20.0, True, ACCEL_MAX, False)
+    assert dec_freshness[-1] is expected and planner.accel_controller.update_kwargs["radar_fresh"] is expected
+
+  sm.logMonoTime["radarState"] = 101
+  planner.update(sm)
+  planner.update_accel_controller(sm, 20.0, True, ACCEL_MAX, False)
+  assert dec_freshness[-1] is True and planner.accel_controller.update_kwargs["radar_fresh"] is True
+
+
+def test_accel_controller_status_publishes_minimal_fields():
+  planner = LongitudinalPlannerSP.__new__(LongitudinalPlannerSP)
+  planner.source = LongitudinalPlanSource.cruise
+  planner.output_v_target = 20.0
+  planner.output_a_target = 0.0
+  planner.events_sp = SimpleNamespace(to_msg=list)
+  planner.dec = SimpleNamespace(mode=lambda: "acc", enabled=lambda: False, active=lambda: False)
+  planner.accel_controller = ControllerStub(active=False, state=AccelControllerState.restrict)
+  planner.scc = SimpleNamespace(
+    vision=SimpleNamespace(state=0, output_v_target=20.0, output_a_target=0.0, current_lat_acc=0.0, max_pred_lat_acc=0.0, is_enabled=False, is_active=False),
+    map=SimpleNamespace(state=0, output_v_target=20.0, output_a_target=0.0, is_enabled=False, is_active=False),
+  )
+  planner.resolver = SimpleNamespace(
+    speed_limit=0.0, speed_limit_last=0.0, speed_limit_final=0.0, speed_limit_final_last=0.0,
+    speed_limit_valid=False, speed_limit_last_valid=False, speed_limit_offset=0.0, distance=0.0,
+    source=custom.LongitudinalPlanSP.SpeedLimit.Source.none,
+  )
+  planner.sla = SimpleNamespace(
+    state=custom.LongitudinalPlanSP.SpeedLimit.AssistState.disabled, is_enabled=False, is_active=False,
+    output_v_target=20.0, output_a_target=0.0,
+  )
+  planner.e2e_alerts_helper = SimpleNamespace(green_light_alert=False, lead_depart_alert=False)
+  sent = {}
+  planner.publish_longitudinal_plan_sp(
+    SimpleNamespace(all_checks=lambda service_list: True),
+    SimpleNamespace(send=lambda service, message: sent.update({service: message})),
+  )
+
+  telemetry = sent["longitudinalPlanSP"].longitudinalPlanSP.accelController
+  assert telemetry.enabled and not telemetry.active
+  assert telemetry.profile == int(AccelProfile.normal)
+  assert telemetry.state == int(AccelControllerState.restrict)
+  assert set(custom.LongitudinalPlanSP.AccelController.schema.fields) == {"enabled", "active", "shadowOnlyDEPRECATED", "profile", "state"}
@@ -0,0 +1,185 @@
+import pytest
+
+from opendbc.car import DT_CTRL, gen_empty_fingerprint, structs
+from opendbc.car.car_helpers import interfaces
+from opendbc.car.ford.values import CAR as FORD
+from opendbc.car.gm.values import CAR as GM
+from opendbc.car.honda.values import CAR as HONDA
+from opendbc.car.hyundai.values import CAR as HYUNDAI
+from opendbc.car.rivian.values import CAR as RIVIAN
+from opendbc.car.toyota.values import CAR as TOYOTA
+from opendbc.car.volkswagen.values import CAR as VOLKSWAGEN
+from openpilot.selfdrive.controls.lib.drive_helpers import should_stop
+from openpilot.selfdrive.controls.lib.longcontrol import LongControl, LongCtrlState
+from openpilot.sunnypilot.selfdrive.controls.lib.longcontrol import LongControlSP
+
+
+VEHICLES = (TOYOTA.TOYOTA_RAV4_TSS2, HONDA.HONDA_ACCORD, HONDA.HONDA_CIVIC_2022, GM.CHEVROLET_BOLT_EUV,
+            HYUNDAI.HYUNDAI_SONATA, FORD.FORD_ESCAPE_MK4, VOLKSWAGEN.VOLKSWAGEN_ARTEON_MK1, RIVIAN.RIVIAN_R1)
+ROUTE_STOP_ONSETS = (
+  (0.290, -0.497, -0.270, -0.302), (0.464, -0.223, -0.264, -0.292), (0.467, -0.582, -0.316, -0.359),
+  (0.530, -0.311, -0.309, -0.333), (0.581, -0.467, -0.312, -0.352), (0.398, -0.557, -0.311, -0.348),
+  (0.517, -0.290, -0.301, -0.327), (0.312, -0.420, -0.271, -0.304), (0.474, -0.509, -0.303, -0.347),
+  (0.241, -0.554, -0.573, -0.617), (0.292, -0.154, -0.302, -0.326),
+)
+
+
+def get_car_params(candidate):
+  fingerprint = gen_empty_fingerprint()
+  interface = interfaces[candidate]
+  CP = interface.get_params(candidate, fingerprint, [], True, False, False)
+  return CP, interface.get_params_sp(CP, candidate, fingerprint, [], True, False, False)
+
+
+def make_car_state(v_ego=0.2, a_ego=0.0, standstill=False):
+  return structs.CarState(vEgo=float(v_ego), aEgo=float(a_ego), standstill=standstill)
+
+
+def make_control(candidate, initial_accel=-0.33):
+  CP, CP_SP = get_car_params(candidate)
+  control = LongControl(CP, CP_SP)
+  control.long_control_state = LongCtrlState.pid
+  control.last_output_accel = initial_accel
+  return CP, control
+
+
+def stock_stopping_output(output_accel, stop_accel):
+  return min(output_accel, 0.0) - DT_CTRL if output_accel > stop_accel else output_accel
+
+
+def test_stop_threshold_remains_unchanged():
+  assert should_stop(0.24, 0.0)
+  assert not should_stop(0.26, 0.0)
+  assert not should_stop(0.24, 0.1)
+
+
+def test_longcontrol_uses_sunnypilot_extension():
+  assert LongControl.__bases__ == (LongControlSP,)
+
+
+@pytest.mark.parametrize("candidate", VEHICLES)
+@pytest.mark.parametrize(("v_ego", "a_ego", "a_target", "initial_accel"), ROUTE_STOP_ONSETS)
+def test_logged_stop_onsets_hold_the_existing_brake(candidate, v_ego, a_ego, a_target, initial_accel):
+  _, control = make_control(candidate, initial_accel)
+  output = control.update(True, make_car_state(v_ego, a_ego), a_target, True, (-3.5, 2.0))
+  assert control.long_control_state == LongCtrlState.stopping
+  assert output == pytest.approx(initial_accel)
+
+
+@pytest.mark.parametrize("candidate", VEHICLES)
+def test_urgent_braking_matches_the_stock_ramp(candidate):
+  CP, control = make_control(candidate)
+  CS = make_car_state(0.8, -0.1)
+  output = control.last_output_accel
+
+  for _ in range(round(1.0 / DT_CTRL)):
+    output = control.update(True, CS, -3.0, True, (-3.5, 2.0))
+
+  expected = -0.33
+  for _ in range(round(1.0 / DT_CTRL)):
+    expected = stock_stopping_output(expected, CP.stopAccel)
+  assert output == pytest.approx(expected)
+
+
+@pytest.mark.parametrize("candidate", VEHICLES)
+def test_stronger_planner_brake_matches_the_stock_ramp(candidate):
+  CP, control = make_control(candidate)
+  outputs = [control.update(True, make_car_state(0.3, -0.3), -1.0, True, (-3.5, 2.0)) for _ in range(10)]
+  expected = []
+  output = -0.33
+  for _ in range(10):
+    output = stock_stopping_output(output, CP.stopAccel)
+    expected.append(output)
+  assert outputs == pytest.approx(expected)
+
+
+@pytest.mark.parametrize("candidate", VEHICLES)
+def test_insufficient_deceleration_uses_the_stock_ramp_immediately(candidate):
+  CP, control = make_control(candidate)
+  output = control.update(True, make_car_state(0.6, -0.1), -0.1, True, (-3.5, 2.0))
+  assert output == pytest.approx(stock_stopping_output(-0.33, CP.stopAccel))
+
+
+def test_deceleration_noise_cannot_release_the_brake():
+  _, control = make_control(TOYOTA.TOYOTA_RAV4_TSS2)
+  outputs = [control.update(True, make_car_state(0.3, -0.3 if frame % 2 else 0.0), -0.1, True, (-3.5, 2.0)) for frame in range(40)]
+  assert all(current <= previous for previous, current in zip(outputs[:-1], outputs[1:], strict=True))
+
+
+def test_planner_noise_cannot_release_the_brake():
+  _, control = make_control(TOYOTA.TOYOTA_RAV4_TSS2)
+  outputs = [control.update(True, make_car_state(0.3, -0.3), -1.0 if frame % 2 else -0.1, True, (-3.5, 2.0)) for frame in range(40)]
+  assert all(current <= previous for previous, current in zip(outputs[:-1], outputs[1:], strict=True))
+
+
+@pytest.mark.parametrize(("v_ego", "a_ego", "a_target"), ((float("nan"), -0.3, -0.1), (0.3, float("nan"), -0.1), (0.3, -0.3, float("nan"))))
+def test_invalid_state_uses_the_stock_ramp(v_ego, a_ego, a_target):
+  CP, control = make_control(TOYOTA.TOYOTA_RAV4_TSS2)
+  output = control.update(True, make_car_state(v_ego, a_ego), a_target, True, (-3.5, 2.0))
+  assert output == pytest.approx(stock_stopping_output(-0.33, CP.stopAccel))
+
+
+@pytest.mark.parametrize(("speed", "initial_accel", "grade_accel", "actuator_lag"), (
+  (0.24, 0.0, 0.0, 0.15), (0.464, -0.223, 0.0, 0.25), (0.53, -0.31, 0.0, 0.35),
+  (0.24, 0.0, 0.49, 0.15), (0.53, -0.31, 0.49, 0.25), (0.6, -0.3, 0.49, 0.35), (0.6, -0.3, 0.49, 0.5),
+))
+def test_smooth_stop_distance_is_bounded(speed, initial_accel, grade_accel, actuator_lag):
+  _, control = make_control(TOYOTA.TOYOTA_RAV4_TSS2, initial_accel)
+  applied_accel = initial_accel
+  distance = 0.0
+
+  for _ in range(round(4.0 / DT_CTRL)):
+    command = control.update(True, make_car_state(speed, applied_accel), -0.1, True, (-3.5, 2.0))
+    applied_accel += DT_CTRL / actuator_lag * (command + grade_accel - applied_accel)
+    speed = max(0.0, speed + applied_accel * DT_CTRL)
+    distance += speed * DT_CTRL
+    if speed == 0.0:
+      break
+
+  assert speed == 0.0
+  assert distance < 1.0
+
+
+@pytest.mark.parametrize("candidate", VEHICLES)
+def test_standstill_uses_the_stock_ramp(candidate):
+  CP, control = make_control(candidate)
+  CS = make_car_state(0.0, 0.0, standstill=True)
+  outputs = [control.update(True, CS, 0.0, True, (-3.5, 2.0)) for _ in range(round(2.0 / DT_CTRL))]
+  expected = -0.33
+  for _ in range(round(2.0 / DT_CTRL)):
+    expected = stock_stopping_output(expected, CP.stopAccel)
+  assert outputs[0] == pytest.approx(stock_stopping_output(-0.33, CP.stopAccel))
+  assert outputs[-1] == pytest.approx(expected)
+
+
+@pytest.mark.parametrize(("v_ego", "a_ego", "standstill"), ((0.6, -0.1, False), (0.0, 0.0, True)))
+def test_stopping_never_releases_a_stronger_command(v_ego, a_ego, standstill):
+  _, control = make_control(TOYOTA.TOYOTA_RAV4_TSS2, -3.0)
+  output = control.update(True, make_car_state(v_ego, a_ego, standstill), 0.0, True, (-3.5, 2.0))
+  assert output == pytest.approx(-3.0)
+
+
+def test_reported_standstill_while_moving_can_hold_the_brake():
+  _, control = make_control(GM.CHEVROLET_BOLT_EUV)
+  output = control.update(True, make_car_state(0.3, -0.3, standstill=True), -0.1, True, (-3.5, 2.0))
+  assert output == pytest.approx(-0.33)
+
+
+def test_stopping_removes_positive_acceleration_immediately():
+  _, control = make_control(HYUNDAI.HYUNDAI_SONATA, 0.2)
+  output = control.update(True, make_car_state(0.2, -0.2), -0.1, True, (-3.5, 2.0))
+  assert output == pytest.approx(-DT_CTRL)
+
+
+def test_rollback_uses_the_stock_ramp():
+  CP, control = make_control(TOYOTA.TOYOTA_RAV4_TSS2)
+  output = control.update(True, make_car_state(-0.1, 0.1), -0.1, True, (-3.5, 2.0))
+  assert output == pytest.approx(stock_stopping_output(-0.33, CP.stopAccel))
+
+
+def test_departure_uses_the_stock_pid_path():
+  _, control = make_control(TOYOTA.TOYOTA_RAV4_TSS2)
+  control.long_control_state = LongCtrlState.stopping
+  output = control.update(True, make_car_state(0.0), 0.6, False, (-3.5, 2.0))
+  assert control.long_control_state == LongCtrlState.pid
+  assert output > 0.0
@@ -1,17 +1,48 @@
+from openpilot.common.realtime import DT_MDL
+
+
 class WMACConstants:
-  # Lead detection parameters
-  LEAD_WINDOW_SIZE = 6  # Stable detection window
-  LEAD_PROB = 0.45  # Balanced threshold for lead detection
+  TRAJECTORY_SIZE = 33
+  PARAM_READ_FRAMES = max(1, int(round(1.0 / DT_MDL)))

-  # Slow down detection parameters
-  SLOW_DOWN_WINDOW_SIZE = 5  # Responsive but stable
-  SLOW_DOWN_PROB = 0.3  # Balanced threshold for slow down scenarios
+  EMERGENCY_HOLD_FRAMES = max(1, int(round(0.75 / DT_MDL)))
+  MIN_MODE_DURATION = {'acc': max(1, int(round(0.6 / DT_MDL))), 'blended': max(1, int(round(0.5 / DT_MDL)))}
+  ENTER_BLENDED_FRAMES = max(1, int(round(0.4 / DT_MDL)))
+  EXIT_BLENDED_FRAMES = max(1, int(round(0.35 / DT_MDL)))
+  STANDSTILL_FRAMES = max(1, int(round(0.2 / DT_MDL)))

-  # Optimized slow down distance curve - smooth and progressive
+  LEAD_PROB = 0.45
+  LEAD_EXIT_PROB = 0.25
+  LEAD_RISE_RATE = 1.0
+  LEAD_FALL_RATE = 0.35
+  RADAR_LEAD_CONTINUITY_FRAMES = max(1, int(round(1.0 / DT_MDL)))
+  RADAR_LEAD_DROPOUT_FRAMES = max(1, int(round(0.2 / DT_MDL)))
+  RADAR_STALE_FRAMES = max(1, int(round(0.5 / DT_MDL)))
+
+  SLOW_DOWN_PROB = 0.5
+  SLOW_DOWN_EXIT_PROB = 0.4
+  SLOW_DOWN_RISE_RATE = 0.65
+  SLOW_DOWN_FALL_RATE = 0.15
  SLOW_DOWN_BP = [0., 10., 20., 30., 40., 50., 55., 60.]
  SLOW_DOWN_DIST = [32., 46., 64., 86., 108., 130., 145., 165.]
+  URGENT_SLOW_DOWN_PROB = 0.85

-  # Slowness detection parameters
-  SLOWNESS_WINDOW_SIZE = 10  # Stable slowness detection
-  SLOWNESS_PROB = 0.55  # Clear threshold for slowness
-  SLOWNESS_CRUISE_OFFSET = 1.025  # Conservative cruise speed offset
+  MODEL_DECEL_START = -0.5
+  MODEL_DECEL_RANGE = 2.0
+  MODEL_DECEL_TREND_FRAMES = 4
+  MODEL_DECEL_TREND_ACCEL = -0.075
+  MODEL_DECEL_TREND_RATE = 0.35
+  MODEL_DECEL_TREND_MAX_MPC_ACCEL = 0.075
+  MODEL_DECEL_TREND_MAX_COMMAND_STEP = 0.15
+  MODEL_DECEL_TREND_RELEASE_ACCEL = -0.02
+  ENDPOINT_URGENCY_GAIN = 1.3
+  CRITICAL_ENDPOINT_FACTOR = 0.3
+  CRITICAL_URGENCY_GAIN = 1.5
+  SPEED_URGENCY_MIN = 25.0
+  SPEED_URGENCY_RANGE = 80.0
+
+  SLOWNESS_PROB = 0.55
+  SLOWNESS_EXIT_PROB = 0.45
+  SLOWNESS_RISE_RATE = 0.35
+  SLOWNESS_FALL_RATE = 0.5
+  SLOWNESS_CRUISE_OFFSET = 1.025
@@ -6,129 +6,119 @@ See the LICENSE.md file in the root directory for more details.
 """
 # Version = 2025-6-30

+from collections import deque
+import math
+from typing import Literal
+
 from openpilot.cereal import messaging
-from opendbc.car import structs
 from numpy import interp
+from opendbc.car import structs
 from openpilot.common.params import Params
 from openpilot.common.realtime import DT_MDL
 from openpilot.sunnypilot.selfdrive.controls.lib.dec.constants import WMACConstants
-from typing import Literal

-# d-e2e, from modeldata.h
-TRAJECTORY_SIZE = 33
-SET_MODE_TIMEOUT = 15
-
-# Define the valid mode types
 ModeType = Literal['acc', 'blended']


-class SmoothKalmanFilter:
-  """Enhanced Kalman filter with smoothing for stable decision making."""
+def clip01(value: float) -> float:
+  return max(0.0, min(1.0, float(value)))

-  def __init__(self, initial_value=0, measurement_noise=0.1, process_noise=0.01,
-               alpha=1.0, smoothing_factor=0.85):
-    self.x = initial_value
-    self.P = 1.0
-    self.R = measurement_noise
-    self.Q = process_noise
-    self.alpha = alpha
-    self.smoothing_factor = smoothing_factor
-    self.initialized = False
-    self.history = []
-    self.max_history = 10
-    self.confidence = 0.0

-  def add_data(self, measurement):
-    if len(self.history) >= self.max_history:
-      self.history.pop(0)
-    self.history.append(measurement)
+class SmoothedSignal:
+  def __init__(self, rise_rate: float, fall_rate: float, initial_value: float = 0.0):
+    self.rise_rate = clip01(rise_rate)
+    self.fall_rate = clip01(fall_rate)
+    self.value = clip01(initial_value)

-    if not self.initialized:
-      self.x = measurement
-      self.initialized = True
-      self.confidence = 0.1
-      return
+  def update(self, measurement: float) -> float:
+    measurement = clip01(measurement)
+    rate = self.rise_rate if measurement > self.value else self.fall_rate
+    self.value += (measurement - self.value) * rate
+    return self.value

-    self.P = self.alpha * self.P + self.Q
+  def reset(self, value: float = 0.0) -> None:
+    self.value = clip01(value)

-    K = self.P / (self.P + self.R)
-    effective_K = K * (1.0 - self.smoothing_factor) + self.smoothing_factor * 0.1

-    innovation = measurement - self.x
-    self.x = self.x + effective_K * innovation
-    self.P = (1 - effective_K) * self.P
+class HysteresisSignal:
+  def __init__(self, enter_threshold: float, exit_threshold: float, rise_rate: float, fall_rate: float):
+    self.enter_threshold = clip01(enter_threshold)
+    self.exit_threshold = clip01(exit_threshold)
+    self.filter = SmoothedSignal(rise_rate, fall_rate)
+    self.active = False

-    if abs(innovation) < 0.1:
-      self.confidence = min(1.0, self.confidence + 0.05)
-    else:
-      self.confidence = max(0.1, self.confidence - 0.02)
+  def update(self, measurement: float) -> bool:
+    value = self.filter.update(measurement)
+    threshold = self.exit_threshold if self.active else self.enter_threshold
+    self.active = value > threshold
+    return self.active

-  def get_value(self):
-    return self.x if self.initialized else None
+  def reset(self) -> None:
+    self.filter.reset()
+    self.active = False

-  def get_confidence(self):
-    return self.confidence
-
-  def reset_data(self):
-    self.initialized = False
-    self.history = []
-    self.confidence = 0.0
+  @property
+  def value(self) -> float:
+    return self.filter.value


 class ModeTransitionManager:
-  """Manages smooth transitions between driving modes with hysteresis."""
-
  def __init__(self):
    self.current_mode: ModeType = 'acc'
-    self.mode_confidence = {'acc': 1.0, 'blended': 0.0}
-    self.transition_timeout = 0
-    self.min_mode_duration = 10
    self.mode_duration = 0
-    self.emergency_override = False
+    self._pending_mode: ModeType = 'acc'
+    self._pending_count = 0
+    self._blended_hold_frames = 0

-  def request_mode(self, mode: ModeType, confidence: float = 1.0, emergency: bool = False):
-    # Emergency override for critical situations (stops, collisions)
-    if emergency:
-      self.emergency_override = True
-      self.current_mode = mode
-      self.transition_timeout = SET_MODE_TIMEOUT
-      self.mode_duration = 0
+  def request_mode(self, mode: ModeType, immediate: bool = False, hold_frames: int = 0, cancel_hold: bool = False) -> None:
+    if immediate:
+      self._blended_hold_frames = max(self._blended_hold_frames, hold_frames) if mode == 'blended' else 0
+      self._pending_mode = mode
+      self._pending_count = 0
+      self._switch_mode(mode)
      return

-    self.mode_confidence[mode] = min(1.0, self.mode_confidence[mode] + 0.1 * confidence)
-    for m in self.mode_confidence:
-      if m != mode:
-        self.mode_confidence[m] = max(0.0, self.mode_confidence[m] - 0.05)
+    if cancel_hold and mode == 'acc':
+      self._blended_hold_frames = 0

-    # Require minimum duration in current mode (unless emergency)
-    if self.mode_duration < self.min_mode_duration and not self.emergency_override:
+    if self._blended_hold_frames > 0:
+      mode = 'blended'
+
+    if mode == self.current_mode:
+      self._pending_mode = mode
+      self._pending_count = 0
      return

-    # Hysteresis: higher threshold for mode changes
-    confidence_threshold = 0.6 if mode != self.current_mode else 0.3  # Lower threshold for faster response
+    if mode != self._pending_mode:
+      self._pending_mode = mode
+      self._pending_count = 1
+    else:
+      self._pending_count += 1

-    if self.mode_confidence[mode] > confidence_threshold:
-      if mode != self.current_mode and self.transition_timeout == 0:
-        self.transition_timeout = SET_MODE_TIMEOUT
-        self.current_mode = mode
-        self.mode_duration = 0
+    if self.mode_duration < WMACConstants.MIN_MODE_DURATION[self.current_mode]:
+      return

-  def update(self):
-    if self.transition_timeout > 0:
-      self.transition_timeout -= 1
+    required_count = WMACConstants.ENTER_BLENDED_FRAMES if mode == 'blended' else WMACConstants.EXIT_BLENDED_FRAMES
+    if self._pending_count >= required_count:
+      self._switch_mode(mode)
+
+  def update(self) -> None:
+    if self._blended_hold_frames > 0:
+      self._blended_hold_frames -= 1
    self.mode_duration += 1

-    # Reset emergency override after some time
-    if self.emergency_override and self.mode_duration > 20:
-      self.emergency_override = False
-
-    # Gradual confidence decay
-    for mode in self.mode_confidence:
-      self.mode_confidence[mode] *= 0.98
-
  def get_mode(self) -> ModeType:
    return self.current_mode

+  def _switch_mode(self, mode: ModeType) -> None:
+    if mode == self.current_mode:
+      return
+
+    self.current_mode = mode
+    self.mode_duration = 0
+    self._pending_mode = mode
+    self._pending_count = 0
+

 class DynamicExperimentalController:
  def __init__(self, CP: structs.CarParams, mpc, params=None):
@@ -142,35 +132,32 @@ class DynamicExperimentalController:

    self._mode_manager = ModeTransitionManager()

-    # Smooth filters for stable decision making with faster response for critical scenarios
-    self._lead_filter = SmoothKalmanFilter(
-      measurement_noise=0.15,
-      process_noise=0.05,
-      alpha=1.02,
-      smoothing_factor=0.8
+    self._lead_tracker = HysteresisSignal(
+      enter_threshold=WMACConstants.LEAD_PROB,
+      exit_threshold=WMACConstants.LEAD_EXIT_PROB,
+      rise_rate=WMACConstants.LEAD_RISE_RATE,
+      fall_rate=WMACConstants.LEAD_FALL_RATE,
+    )
+    self._slow_down_tracker = HysteresisSignal(
+      enter_threshold=WMACConstants.SLOW_DOWN_PROB,
+      exit_threshold=WMACConstants.SLOW_DOWN_EXIT_PROB,
+      rise_rate=WMACConstants.SLOW_DOWN_RISE_RATE,
+      fall_rate=WMACConstants.SLOW_DOWN_FALL_RATE,
+    )
+    self._slowness_tracker = HysteresisSignal(
+      enter_threshold=WMACConstants.SLOWNESS_PROB,
+      exit_threshold=WMACConstants.SLOWNESS_EXIT_PROB,
+      rise_rate=WMACConstants.SLOWNESS_RISE_RATE,
+      fall_rate=WMACConstants.SLOWNESS_FALL_RATE,
    )

-    self._slow_down_filter = SmoothKalmanFilter(
-      measurement_noise=0.1,
-      process_noise=0.1,
-      alpha=1.05,
-      smoothing_factor=0.7
-    )
-
-    self._slowness_filter = SmoothKalmanFilter(
-      measurement_noise=0.1,
-      process_noise=0.06,
-      alpha=1.015,
-      smoothing_factor=0.92
-    )
-
-    self._mpc_fcw_filter = SmoothKalmanFilter(
-      measurement_noise=0.2,
-      process_noise=0.1,
-      alpha=1.1,
-      smoothing_factor=0.5
-    )
    self._has_lead_filtered = False
+    self._has_any_lead = False
+    self._has_current_radar_acc_lead = False
+    self._has_radar_acc_lead = False
+    self._radar_acc_lead_frames = 0
+    self._radar_fresh = True
+    self._radar_stale_frames = 0
    self._has_slow_down = False
    self._has_slowness = False
    self._has_mpc_fcw = False
@@ -179,13 +166,18 @@ class DynamicExperimentalController:
    self._has_standstill = False
    self._mpc_fcw_crash_cnt = 0
    self._standstill_count = 0
-    # debug
+
    self._endpoint_x = float('inf')
    self._expected_distance = 0.0
    self._trajectory_valid = False
+    self._raw_urgency = 0.0
+    self._model_accel_samples = deque(maxlen=WMACConstants.MODEL_DECEL_TREND_FRAMES)
+    self._model_decel_trending = False
+    self._model_decel_latched = False
+    self._planner_accel = math.nan

  def _read_params(self) -> None:
-    if self._frame % int(1. / DT_MDL) == 0:
+    if self._frame % WMACConstants.PARAM_READ_FRAMES == 0:
      self._enabled = self._params.get_bool("DynamicExperimentalControl")

  def mode(self) -> str:
@@ -198,191 +190,202 @@ class DynamicExperimentalController:
    return self._active

  def set_mpc_fcw_crash_cnt(self) -> None:
-    """Set MPC FCW crash count"""
    self._mpc_fcw_crash_cnt = self._mpc.crash_cnt

-  def _update_calculations(self, sm: messaging.SubMaster) -> None:
+  def _update_calculations(self, sm: messaging.SubMaster, radar_fresh: bool) -> None:
    car_state = sm['carState']
-    lead_one = sm['radarState'].leadOne
+    radar_state = sm['radarState']
+    lead_one = radar_state.leadOne
+    lead_two = radar_state.leadTwo
    md = sm['modelV2']

    self._v_ego_kph = car_state.vEgo * 3.6
    self._v_cruise_kph = car_state.vCruise
    self._has_standstill = car_state.standstill

-    # standstill detection
    if self._has_standstill:
-      self._standstill_count = min(20, self._standstill_count + 1)
+      self._standstill_count = min(WMACConstants.STANDSTILL_FRAMES * 3, self._standstill_count + 1)
    else:
      self._standstill_count = max(0, self._standstill_count - 1)

-    # Lead detection
-    self._lead_filter.add_data(float(lead_one.present))
-    lead_value = self._lead_filter.get_value() or 0.0
-    self._has_lead_filtered = lead_value > WMACConstants.LEAD_PROB
-
-    # MPC FCW detection
-    fcw_filtered_value = self._mpc_fcw_filter.get_value() or 0.0
-    self._mpc_fcw_filter.add_data(float(self._mpc_fcw_crash_cnt > 0))
-    self._has_mpc_fcw = fcw_filtered_value > 0.5
-
-    # Slow down detection
+    self._radar_fresh = bool(radar_fresh)
+    if self._radar_fresh:
+      self._radar_stale_frames = 0
+      self._has_lead_filtered = self._lead_tracker.update(float(lead_one.present))
+      self._has_any_lead = bool(lead_one.present or lead_two.present)
+      self._has_current_radar_acc_lead = bool(max(self._radar_acc_lead_score(lead_one), self._radar_acc_lead_score(lead_two)))
+      self._update_radar_acc_lead()
+    else:
+      self._radar_stale_frames += 1
+      self._has_current_radar_acc_lead = False
+      if self._radar_stale_frames < WMACConstants.RADAR_STALE_FRAMES:
+        self._update_radar_acc_lead()
+      else:
+        self._lead_tracker.reset()
+        self._has_lead_filtered = False
+        self._has_any_lead = False
+        self._has_radar_acc_lead = False
+        self._radar_acc_lead_frames = 0
+    self._has_mpc_fcw = self._mpc_fcw_crash_cnt > 0
    self._calculate_slow_down(md)

-    # Slowness detection
-    if not (self._standstill_count > 5) and not self._has_slow_down:
+    if self._standstill_count > WMACConstants.STANDSTILL_FRAMES or self._has_slow_down:
+      self._slowness_tracker.reset()
+      self._has_slowness = False
+    else:
      current_slowness = float(self._v_ego_kph <= (self._v_cruise_kph * WMACConstants.SLOWNESS_CRUISE_OFFSET))
-      self._slowness_filter.add_data(current_slowness)
-      slowness_value = self._slowness_filter.get_value() or 0.0
+      self._has_slowness = self._slowness_tracker.update(current_slowness)

-      # Hysteresis for slowness
-      threshold = WMACConstants.SLOWNESS_PROB * (0.8 if self._has_slowness else 1.1)
-      self._has_slowness = slowness_value > threshold
-
-  def _calculate_slow_down(self, md):
-    """Calculate urgency based on trajectory endpoint vs expected distance."""
-
-    # Reset to safe defaults
-    urgency = 0.0
+  def _calculate_slow_down(self, md) -> None:
    self._endpoint_x = float('inf')
+    self._expected_distance = 0.0
    self._trajectory_valid = False

-    #Require exact trajectory size
-    position_valid = len(md.position.x) == TRAJECTORY_SIZE
-    orientation_valid = len(md.orientation.x) == TRAJECTORY_SIZE
+    self._update_model_decel_trend(md)
+    urgency = self._model_action_urgency(md)
+    position_valid = len(md.position.x) == WMACConstants.TRAJECTORY_SIZE

-    if not (position_valid and orientation_valid):
-      # Invalid trajectory - this itself might indicate a stop scenario
-      # Apply moderate urgency for incomplete trajectories at speed
-      if self._v_ego_kph > 20.0:
-        urgency = 0.3
+    if position_valid:
+      self._trajectory_valid = True
+      self._endpoint_x = md.position.x[WMACConstants.TRAJECTORY_SIZE - 1]
+      self._expected_distance = interp(self._v_ego_kph, WMACConstants.SLOW_DOWN_BP, WMACConstants.SLOW_DOWN_DIST)
+      urgency = max(urgency, self._endpoint_urgency(self._endpoint_x, self._expected_distance))

-      self._slow_down_filter.add_data(urgency)
-      urgency_filtered = self._slow_down_filter.get_value() or 0.0
-      self._has_slow_down = urgency_filtered > WMACConstants.SLOW_DOWN_PROB
-      self._urgency = urgency_filtered
+    self._raw_urgency = clip01(urgency)
+    self._has_slow_down = self._slow_down_tracker.update(self._raw_urgency)
+    self._urgency = self._slow_down_tracker.value
+
+  def _update_model_decel_trend(self, md) -> None:
+    try:
+      desired_accel = float(md.action.desiredAcceleration)
+    except (AttributeError, OverflowError, TypeError, ValueError):
+      desired_accel = math.nan
+    if not math.isfinite(desired_accel):
+      self._reset_model_decel_trend()
+    else:
+      self._model_accel_samples.append(desired_accel)
+    history = tuple(self._model_accel_samples)
+    self._model_decel_trending = (len(history) == self._model_accel_samples.maxlen
+                                  and history[-1] <= WMACConstants.MODEL_DECEL_TREND_ACCEL
+                                  and (history[0] - history[-1]) / (DT_MDL * (len(history) - 1)) > WMACConstants.MODEL_DECEL_TREND_RATE
+                                  and all(after <= before for before, after in zip(history[:-1], history[1:], strict=True))
+                                  and sum(after < before for before, after in zip(history[:-1], history[1:], strict=True)) >= 2)
+    if len(history) == self._model_accel_samples.maxlen and all(
+      accel >= WMACConstants.MODEL_DECEL_TREND_RELEASE_ACCEL for accel in history
+    ):
+      self._model_decel_latched = False
+
+  def _reset_model_decel_trend(self) -> None:
+    self._model_accel_samples.clear()
+    self._model_decel_trending = False
+    self._model_decel_latched = False
+
+  def _radar_acc_lead_score(self, lead_one) -> float:
+    radar_track_id = int(getattr(lead_one, 'radarTrackId', -1))
+    return float(lead_one.present and (bool(getattr(lead_one, 'radar', False)) or radar_track_id >= 0))
+
+  def _update_radar_acc_lead(self) -> None:
+    if self._has_current_radar_acc_lead:
+      self._radar_acc_lead_frames = WMACConstants.RADAR_LEAD_CONTINUITY_FRAMES
+      self._has_radar_acc_lead = True
      return

-    # We have a valid full trajectory
-    self._trajectory_valid = True
+    if not self._has_any_lead:
+      self._radar_acc_lead_frames = min(self._radar_acc_lead_frames, WMACConstants.RADAR_LEAD_DROPOUT_FRAMES)

-    # Use the exact endpoint (33rd point, index 32)
-    endpoint_x = md.position.x[TRAJECTORY_SIZE - 1]
-    self._endpoint_x = endpoint_x
+    self._has_radar_acc_lead = self._radar_acc_lead_frames > 0
+    self._radar_acc_lead_frames = max(0, self._radar_acc_lead_frames - 1)

-    # Get expected distance based on current speed using tuned constants
-    expected_distance = interp(self._v_ego_kph,
-                               WMACConstants.SLOW_DOWN_BP,
-                               WMACConstants.SLOW_DOWN_DIST)
-    self._expected_distance = expected_distance
+  def _model_action_urgency(self, md) -> float:
+    action = getattr(md, 'action', None)
+    if action is None:
+      return 0.0

-    # Calculate urgency based on trajectory shortage
-    if endpoint_x < expected_distance:
-      shortage = expected_distance - endpoint_x
-      shortage_ratio = shortage / expected_distance
+    urgency = 1.0 if getattr(action, 'shouldStop', False) else 0.0
+    desired_accel = getattr(action, 'desiredAcceleration', 0.0)
+    if desired_accel < WMACConstants.MODEL_DECEL_START:
+      urgency = max(urgency, min(1.0, (WMACConstants.MODEL_DECEL_START - desired_accel) / WMACConstants.MODEL_DECEL_RANGE))
+    return urgency

-      # Base urgency on shortage ratio
-      urgency = min(1.0, shortage_ratio * 2.0)
+  def _endpoint_urgency(self, endpoint_x: float, expected_distance: float) -> float:
+    if endpoint_x >= expected_distance:
+      return 0.0

-      # Increase urgency for very short trajectories (imminent stops)
-      critical_distance = expected_distance * 0.3
-      if endpoint_x < critical_distance:
-        urgency = min(1.0, urgency * 2.0)
+    shortage_ratio = (expected_distance - endpoint_x) / expected_distance
+    urgency = min(1.0, shortage_ratio * WMACConstants.ENDPOINT_URGENCY_GAIN)

-      # Speed-based urgency adjustment
-      if self._v_ego_kph > 25.0:
-        speed_factor = 1.0 + (self._v_ego_kph - 25.0) / 80.0
-        urgency = min(1.0, urgency * speed_factor)
+    if endpoint_x < expected_distance * WMACConstants.CRITICAL_ENDPOINT_FACTOR:
+      urgency = min(1.0, urgency * WMACConstants.CRITICAL_URGENCY_GAIN)

-    # Apply filtering but with less smoothing for stops
-    self._slow_down_filter.add_data(urgency)
-    urgency_filtered = self._slow_down_filter.get_value() or 0.0
+    if self._v_ego_kph > WMACConstants.SPEED_URGENCY_MIN:
+      speed_factor = 1.0 + (self._v_ego_kph - WMACConstants.SPEED_URGENCY_MIN) / WMACConstants.SPEED_URGENCY_RANGE
+      urgency = min(1.0, urgency * speed_factor)

-    # Update state with lower threshold for better stop detection
-    self._has_slow_down = urgency_filtered > (WMACConstants.SLOW_DOWN_PROB * 0.8)
-    self._urgency = urgency_filtered
+    return urgency

-  def _radarless_mode(self) -> None:
-    """Radarless mode decision logic with emergency handling."""
+  def _model_decel_handoff_ready(self) -> bool:
+    try:
+      mpc_accel = float(self._mpc.a_solution[1])
+      return (math.isfinite(mpc_accel) and mpc_accel <= WMACConstants.MODEL_DECEL_TREND_MAX_MPC_ACCEL
+              and math.isfinite(self._planner_accel) and self._planner_accel <= WMACConstants.MODEL_DECEL_TREND_MAX_MPC_ACCEL
+              and self._planner_accel - self._model_accel_samples[-1] <= WMACConstants.MODEL_DECEL_TREND_MAX_COMMAND_STEP)
+    except (AttributeError, IndexError, OverflowError, TypeError, ValueError):
+      return False
+
+  def _desired_mode(self) -> tuple[ModeType, bool]:
+    standstill = self._standstill_count > WMACConstants.STANDSTILL_FRAMES
+    urgent_slow_down = self._has_slow_down and self._raw_urgency > WMACConstants.URGENT_SLOW_DOWN_PROB
+
+    if not self._CP.radarUnavailable and self._has_current_radar_acc_lead:
+      self._reset_model_decel_trend()
+      return 'acc', True
+
+    radar_stale = not self._radar_fresh if self._has_mpc_fcw else self._radar_stale_frames > 1
+    if (radar_stale or not self._has_any_lead) and (self._has_mpc_fcw or urgent_slow_down):
+      self._radar_acc_lead_frames = 0
+      self._has_radar_acc_lead = False
+      return 'blended', True
+
+    if not self._CP.radarUnavailable and self._has_radar_acc_lead:
+      self._reset_model_decel_trend()
+      return 'acc', True
+
+    entering_model_slowdown = self._model_decel_trending and self._model_decel_handoff_ready() and not self._model_decel_latched
+    self._model_decel_latched |= entering_model_slowdown
+    if self._model_decel_latched:
+      return 'blended', entering_model_slowdown

-    # EMERGENCY: MPC FCW - immediate blended mode
    if self._has_mpc_fcw:
-      self._mode_manager.request_mode('blended', confidence=1.0, emergency=True)
-      return
-
-    # Standstill: use blended
-    if self._standstill_count > 3:
-      self._mode_manager.request_mode('blended', confidence=0.9)
-      return
-
-    # Slow down scenarios: emergency for high urgency, normal for lower urgency
-    if self._has_slow_down:
-      if self._urgency > 0.7:
-        # Emergency: immediate blended mode for high urgency stops
-        self._mode_manager.request_mode('blended', confidence=1.0, emergency=True)
-      else:
-        # Normal: blended with urgency-based confidence
-        confidence = min(1.0, self._urgency * 1.5)
-        self._mode_manager.request_mode('blended', confidence=confidence)
-      return
-
-    # Driving slow: use ACC (but not if actively slowing down)
-    if self._has_slowness and not self._has_slow_down:
-      self._mode_manager.request_mode('acc', confidence=0.8)
-      return
-
-    # Default: ACC
-    self._mode_manager.request_mode('acc', confidence=0.7)
-
-  def _radar_mode(self) -> None:
-    """Radar mode with emergency handling."""
-
-    # EMERGENCY: MPC FCW - immediate blended mode
-    if self._has_mpc_fcw:
-      self._mode_manager.request_mode('blended', confidence=1.0, emergency=True)
-      return
-
-    # If lead detected and not in standstill: always use ACC
-    if self._has_lead_filtered and not (self._standstill_count > 3):
-      self._mode_manager.request_mode('acc', confidence=1.0)
-      return
-
-    # Slow down scenarios: emergency for high urgency, normal for lower urgency
-    if self._has_slow_down:
-      if self._urgency > 0.7:
-        # Emergency: immediate blended mode for high urgency stops
-        self._mode_manager.request_mode('blended', confidence=1.0, emergency=True)
-      else:
-        # Normal: blended with urgency-based confidence
-        confidence = min(1.0, self._urgency * 1.3)
-        self._mode_manager.request_mode('blended', confidence=confidence)
-      return
-
-    # Standstill: use blended
-    if self._standstill_count > 3:
-      self._mode_manager.request_mode('blended', confidence=0.9)
-      return
-
-    # Driving slow: use ACC (but not if actively slowing down)
-    if self._has_slowness and not self._has_slow_down:
-      self._mode_manager.request_mode('acc', confidence=0.8)
-      return
-
-    # Default: ACC
-    self._mode_manager.request_mode('acc', confidence=0.7)
-
-  def update(self, sm: messaging.SubMaster) -> None:
-    self._read_params()
-
-    self.set_mpc_fcw_crash_cnt()
-
-    self._update_calculations(sm)
+      return 'blended', True

    if self._CP.radarUnavailable:
-      self._radarless_mode()
-    else:
-      self._radar_mode()
+      if standstill or self._has_slow_down:
+        return 'blended', urgent_slow_down
+      return 'acc', False

-    self._mode_manager.update()
+    if standstill or self._has_slow_down:
+      return 'blended', urgent_slow_down
+
+    return 'acc', False
+
+  def update(self, sm: messaging.SubMaster, *, radar_fresh: bool = True, planner_accel: float | None = None) -> None:
+    self._read_params()
+    self.set_mpc_fcw_crash_cnt()
+    try:
+      self._planner_accel = float(planner_accel)
+    except (OverflowError, TypeError, ValueError):
+      self._planner_accel = math.nan
+    self._update_calculations(sm, radar_fresh)
    self._active = sm['selfdriveState'].experimentalMode and self._enabled
+    if not self._active:
+      model_decel_latched = self._model_decel_latched
+      self._reset_model_decel_trend()
+      if model_decel_latched:
+        self._mode_manager.request_mode('acc', immediate=True)
+
+    mode, immediate = self._desired_mode()
+    self._mode_manager.request_mode(mode, immediate=immediate, hold_frames=WMACConstants.EMERGENCY_HOLD_FRAMES,
+                                    cancel_hold=not self._CP.radarUnavailable and self._has_radar_acc_lead)
+    self._mode_manager.update()
+
    self._frame += 1
@@ -1,94 +0,0 @@
-import pytest
-
-from openpilot.sunnypilot.selfdrive.controls.lib.dec.dec import DynamicExperimentalController
-
-class MockLeadOne:
-  def __init__(self, status=0.0):
-    self.status = status
-
-class MockRadarState:
-  def __init__(self, status=0.0):
-    self.leadOne = MockLeadOne(status=status)
-
-class MockCarState:
-  def __init__(self, vEgo=0.0, vCruise=0.0, standstill=False):
-    self.vEgo = vEgo
-    self.vCruise = vCruise
-    self.standstill = standstill
-
-class MockModelData:
-  def __init__(self, valid=True):
-    size = 33 if valid else 10  # incomplete if invalid
-    self.position = type("Pos", (), {"x": [0.0] * size})()
-    self.orientation = type("Ori", (), {"x": [0.0] * size})()
-
-class MockSelfDriveState:
-  def __init__(self, experimentalMode=False):
-    self.experimentalMode = experimentalMode
-
-class MockParams:
-  def get_bool(self, name):
-    return True
-
-@pytest.fixture
-def default_sm():
-  sm = {
-    'carState': MockCarState(vEgo=10.0, vCruise=20.0),
-    'radarState': MockRadarState(status=1.0),
-    'modelV2': MockModelData(valid=True),
-    'selfdriveState': MockSelfDriveState(experimentalMode=True),
-  }
-  return sm
-
-@pytest.fixture
-def mock_cp():
-  class CP:
-    radarUnavailable = False
-  return CP()
-
-@pytest.fixture
-def mock_mpc():
-  class MPC:
-    crash_cnt = 0
-  return MPC()
-
-# Fake Kalman Filter that always returns a given value
-class FakeKalman:
-  def __init__(self, value=1.0):
-    self.value = value
-  def add_data(self, v): pass
-  def get_value(self): return self.value
-  def get_confidence(self): return 1.0
-  def reset_data(self): pass
-
-def test_initial_mode_is_acc(mock_cp, mock_mpc):
-  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
-  assert controller.mode() == "acc"
-
-def test_standstill_triggers_blended(mock_cp, mock_mpc, default_sm):
-  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
-  default_sm['carState'].standstill = True
-  for _ in range(10):
-    controller.update(default_sm)
-  assert controller.mode() == "blended"
-
-def test_emergency_blended_on_fcw(mock_cp, mock_mpc, default_sm):
-  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
-  mock_mpc.crash_cnt = 1  # simulate FCW
-  for _ in range(2):
-    controller.update(default_sm)
-  assert controller.mode() == "blended"
-
-def test_radarless_slowdown_triggers_blended(mock_cp, mock_mpc, default_sm):
-  mock_cp.radarUnavailable = True
-  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
-
-  # Force conditions to simulate slowdown
-  controller._slow_down_filter = FakeKalman(value=1.0)  # ty: ignore[invalid-assignment]
-  controller._v_ego_kph = 35.0
-  default_sm['modelV2'] = MockModelData(valid=False)  # Incomplete trajectory
-
-  for _ in range(3):
-    controller.update(default_sm)
-
-  assert controller.mode() == "blended"
@@ -0,0 +1,633 @@
+import pytest
+
+from openpilot.sunnypilot.selfdrive.controls.lib.dec.constants import WMACConstants
+from openpilot.sunnypilot.selfdrive.controls.lib.dec.dec import DynamicExperimentalController, HysteresisSignal
+
+
+class MockLeadOne:
+  def __init__(self, status=0.0, dRel=30.0, vRel=0.0, radar=False, radarTrackId=-1):
+    self.present = status
+    self.dRel = dRel
+    self.vRel = vRel
+    self.radar = radar
+    self.radarTrackId = radarTrackId
+
+
+class MockRadarState:
+  def __init__(self, status=0.0, dRel=30.0, vRel=0.0, radar=False, radarTrackId=-1, leadTwo=None):
+    self.leadOne = MockLeadOne(status=status, dRel=dRel, vRel=vRel, radar=radar, radarTrackId=radarTrackId)
+    self.leadTwo = leadTwo if leadTwo is not None else MockLeadOne()
+
+
+class MockCarState:
+  def __init__(self, vEgo=0.0, vCruise=0.0, standstill=False):
+    self.vEgo = vEgo
+    self.vCruise = vCruise
+    self.standstill = standstill
+
+
+class MockAction:
+  def __init__(self, desiredAcceleration=0.0, shouldStop=False):
+    self.desiredAcceleration = desiredAcceleration
+    self.shouldStop = shouldStop
+
+
+class MockModelData:
+  def __init__(self, valid=True, endpoint_x=200.0, orientation_valid=None, desired_acceleration=0.0, should_stop=False):
+    position_size = 33 if valid else 10
+    orientation_size = position_size if orientation_valid is None else (33 if orientation_valid else 10)
+    position_x = [0.0] * position_size
+    if position_x:
+      position_x[-1] = endpoint_x
+    self.position = type("Pos", (), {"x": position_x})()
+    self.orientation = type("Ori", (), {"x": [0.0] * orientation_size})()
+    self.acceleration = type("Accel", (), {"x": [0.0] * position_size})()
+    self.action = MockAction(desired_acceleration, should_stop)
+
+
+class MockSelfDriveState:
+  def __init__(self, experimentalMode=False):
+    self.experimentalMode = experimentalMode
+
+
+class MockParams:
+  def get_bool(self, name):
+    return True
+
+
+@pytest.fixture
+def default_sm():
+  sm = {
+    'carState': MockCarState(vEgo=10.0, vCruise=20.0),
+    'radarState': MockRadarState(status=1.0, radar=True, radarTrackId=7),
+    'modelV2': MockModelData(valid=True),
+    'selfdriveState': MockSelfDriveState(experimentalMode=True),
+  }
+  return sm
+
+
+@pytest.fixture
+def mock_cp():
+  class CP:
+    radarUnavailable = False
+  return CP()
+
+
+@pytest.fixture
+def mock_mpc():
+  class MPC:
+    crash_cnt = 0
+    a_solution = [0.0, 0.0]
+  return MPC()
+
+
+def test_initial_mode_is_acc(mock_cp, mock_mpc):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  assert controller.mode() == "acc"
+
+
+def test_standstill_triggers_blended(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=0.0)
+  default_sm['carState'].standstill = True
+  for _ in range(20):
+    controller.update(default_sm)
+  assert controller.mode() == "blended"
+
+
+def test_emergency_blended_on_fcw(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=0.0)
+  mock_mpc.crash_cnt = 1
+  controller.update(default_sm)
+  assert controller.mode() == "blended"
+
+
+def test_radarless_slowdown_triggers_blended(mock_cp, mock_mpc, default_sm):
+  mock_cp.radarUnavailable = True
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=0.0)
+  default_sm['modelV2'] = MockModelData(valid=True, endpoint_x=0.0)
+
+  controller.update(default_sm)
+
+  assert controller.mode() == "blended"
+
+
+def test_valid_position_with_missing_orientation_can_trigger_slowdown(mock_cp, mock_mpc, default_sm):
+  mock_cp.radarUnavailable = True
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=0.0)
+  default_sm['modelV2'] = MockModelData(valid=True, endpoint_x=0.0, orientation_valid=False)
+
+  controller.update(default_sm)
+
+  assert controller._trajectory_valid
+  assert controller.mode() == "blended"
+
+
+def test_incomplete_position_does_not_trigger_slowdown(mock_cp, mock_mpc, default_sm):
+  mock_cp.radarUnavailable = True
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=0.0)
+  default_sm['modelV2'] = MockModelData(valid=False, endpoint_x=0.0)
+
+  for _ in range(3):
+    controller.update(default_sm)
+
+  assert not controller._trajectory_valid
+  assert not controller._has_slow_down
+  assert controller.mode() == "acc"
+
+
+def test_slowdown_hysteresis_prevents_threshold_chatter():
+  signal = HysteresisSignal(enter_threshold=0.5, exit_threshold=0.4, rise_rate=1.0, fall_rate=1.0)
+
+  assert signal.update(0.55)
+  assert signal.update(0.45)
+  assert not signal.update(0.35)
+
+
+def test_model_should_stop_triggers_blended_without_valid_trajectory(mock_cp, mock_mpc, default_sm):
+  mock_cp.radarUnavailable = True
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=0.0)
+  default_sm['modelV2'] = MockModelData(valid=False, should_stop=True)
+
+  controller.update(default_sm)
+
+  assert not controller._trajectory_valid
+  assert controller.mode() == "blended"
+
+
+def test_confirmed_model_decel_trend_enters_blended_before_a_large_command(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=0.0)
+
+  for desired_acceleration in (-0.02, -0.05, -0.08):
+    default_sm['modelV2'] = MockModelData(valid=False, desired_acceleration=desired_acceleration)
+    controller.update(default_sm, planner_accel=0.0)
+    assert controller.mode() == "acc"
+
+  default_sm['modelV2'] = MockModelData(valid=False, desired_acceleration=-0.12)
+  controller.update(default_sm, planner_accel=0.0)
+
+  assert controller._model_decel_trending
+  assert not controller._has_slow_down
+  assert controller.mode() == "blended"
+
+
+def test_confirmed_model_decel_handoff_stays_latched_through_a_plateau(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=0.0)
+
+  for desired_acceleration in (-0.02, -0.05, -0.08, -0.12):
+    default_sm['modelV2'] = MockModelData(valid=False, desired_acceleration=desired_acceleration)
+    controller.update(default_sm, planner_accel=0.0)
+
+  for _ in range(WMACConstants.EMERGENCY_HOLD_FRAMES + WMACConstants.EXIT_BLENDED_FRAMES + 1):
+    default_sm['modelV2'] = MockModelData(valid=False, desired_acceleration=-0.12)
+    controller.update(default_sm, planner_accel=0.0)
+
+  assert not controller._model_decel_trending
+  assert controller._model_decel_latched
+  assert controller.mode() == "blended"
+
+  for _ in range(WMACConstants.MODEL_DECEL_TREND_FRAMES + WMACConstants.EXIT_BLENDED_FRAMES):
+    default_sm['modelV2'] = MockModelData(valid=False, desired_acceleration=0.0)
+    controller.update(default_sm, planner_accel=0.0)
+
+  assert not controller._model_decel_latched
+  assert controller.mode() == "acc"
+
+
+def test_model_decel_trend_never_overrides_a_radar_lead(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+
+  for desired_acceleration in (-0.02, -0.05, -0.08, -0.12):
+    default_sm['modelV2'] = MockModelData(valid=False, desired_acceleration=desired_acceleration)
+    controller.update(default_sm)
+
+  assert not controller._model_accel_samples
+  assert not controller._model_decel_latched
+  assert controller._has_radar_acc_lead
+  assert controller.mode() == "acc"
+
+
+def test_radar_acquisition_clears_a_latched_model_decel_handoff(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=0.0)
+  for desired_acceleration in (-0.02, -0.05, -0.08, -0.12):
+    default_sm['modelV2'] = MockModelData(valid=False, desired_acceleration=desired_acceleration)
+    controller.update(default_sm, planner_accel=0.0)
+  assert controller._model_decel_latched
+
+  default_sm['radarState'] = MockRadarState(status=1.0, radar=True, radarTrackId=7)
+  controller.update(default_sm, planner_accel=0.0)
+
+  assert not controller._model_accel_samples
+  assert not controller._model_decel_latched
+  assert controller.mode() == "acc"
+
+
+def test_model_decel_trend_does_not_accumulate_while_dec_is_inactive(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=0.0)
+  default_sm['selfdriveState'].experimentalMode = False
+  for desired_acceleration in (-0.02, -0.05, -0.08, -0.12):
+    default_sm['modelV2'] = MockModelData(valid=False, desired_acceleration=desired_acceleration)
+    controller.update(default_sm, planner_accel=0.0)
+
+  assert not controller._model_accel_samples
+  assert not controller._model_decel_latched
+
+  default_sm['selfdriveState'].experimentalMode = True
+  controller.update(default_sm, planner_accel=0.0)
+  assert not controller._model_decel_trending
+  assert controller.mode() == "acc"
+
+
+def test_disabling_dec_clears_a_latched_model_decel_mode(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=0.0)
+  for desired_acceleration in (-0.02, -0.05, -0.08, -0.12):
+    default_sm['modelV2'] = MockModelData(valid=False, desired_acceleration=desired_acceleration)
+    controller.update(default_sm, planner_accel=0.0)
+  assert controller._model_decel_latched
+  assert controller.mode() == "blended"
+
+  default_sm['selfdriveState'].experimentalMode = False
+  default_sm['modelV2'] = MockModelData(valid=False, desired_acceleration=0.0)
+  controller.update(default_sm, planner_accel=0.0)
+
+  assert not controller._model_decel_latched
+  assert controller.mode() == "acc"
+
+
+def test_model_decel_trend_waits_while_mpc_is_accelerating(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=0.0)
+  mock_mpc.a_solution[1] = 0.5
+
+  for desired_acceleration in (-0.02, -0.05, -0.08, -0.12):
+    default_sm['modelV2'] = MockModelData(valid=False, desired_acceleration=desired_acceleration)
+    controller.update(default_sm, planner_accel=0.0)
+
+  assert controller._model_decel_trending
+  assert controller.mode() == "acc"
+
+
+def test_steep_model_decel_trend_defers_to_the_existing_urgent_path(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=0.0)
+
+  for desired_acceleration in (0.0, -0.2, -0.4, -0.6):
+    default_sm['modelV2'] = MockModelData(valid=False, desired_acceleration=desired_acceleration)
+    controller.update(default_sm, planner_accel=0.05)
+
+  assert controller._model_decel_trending
+  assert controller.mode() == "acc"
+
+
+def test_model_decel_trend_waits_while_the_planner_is_accelerating(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=0.0)
+
+  for desired_acceleration in (-0.02, -0.05, -0.08, -0.12):
+    default_sm['modelV2'] = MockModelData(valid=False, desired_acceleration=desired_acceleration)
+    controller.update(default_sm, planner_accel=0.2)
+
+  assert controller._model_decel_trending
+  assert controller.mode() == "acc"
+
+
+def test_alternating_model_accel_noise_does_not_trigger_an_early_handoff(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=0.0)
+
+  for desired_acceleration in (0.0, -0.2, 0.0, -0.2):
+    default_sm['modelV2'] = MockModelData(valid=False, desired_acceleration=desired_acceleration)
+    controller.update(default_sm)
+
+  assert not controller._model_decel_trending
+  assert controller.mode() == "acc"
+
+
+def test_radar_lead_keeps_acc_over_model_slowdown(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=1.0, radar=True, radarTrackId=7)
+  default_sm['modelV2'] = MockModelData(valid=True, endpoint_x=0.0)
+
+  for _ in range(3):
+    controller.update(default_sm)
+
+  assert controller._has_slow_down
+  assert controller._has_radar_acc_lead
+  assert controller.mode() == "acc"
+
+
+def test_far_radar_lead_always_uses_acc(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=1.0, dRel=120.0, vRel=0.0, radar=True)
+  default_sm['modelV2'] = MockModelData(valid=True, endpoint_x=0.0)
+
+  controller.update(default_sm)
+
+  assert controller._has_lead_filtered
+  assert controller._has_radar_acc_lead
+  assert controller.mode() == "acc"
+
+
+def test_radar_acquisition_immediately_returns_blended_to_acc(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=0.0)
+  default_sm['modelV2'] = MockModelData(valid=True, endpoint_x=0.0)
+  controller.update(default_sm)
+  assert controller.mode() == "blended"
+
+  default_sm['radarState'] = MockRadarState(status=1.0, dRel=120.0, radar=True, radarTrackId=7)
+  controller.update(default_sm)
+
+  assert controller._has_radar_acc_lead
+  assert controller.mode() == "acc"
+
+  default_sm['radarState'] = MockRadarState(status=0.0)
+  default_sm['modelV2'] = MockModelData(valid=True)
+  for _ in range(20):
+    controller.update(default_sm)
+  assert controller.mode() == "acc"
+
+
+def test_close_vision_only_lead_can_use_blended(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=1.0, dRel=30.0, vRel=-5.0)
+  default_sm['modelV2'] = MockModelData(valid=True, endpoint_x=0.0)
+  controller.update(default_sm)
+
+  assert not controller._has_radar_acc_lead
+  assert controller.mode() == "blended"
+
+
+def test_second_radar_lead_forces_acc(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  lead_two = MockLeadOne(status=1.0, dRel=120.0, radar=True, radarTrackId=8)
+  default_sm['radarState'] = MockRadarState(status=1.0, dRel=30.0, vRel=-5.0, leadTwo=lead_two)
+  default_sm['modelV2'] = MockModelData(valid=True, endpoint_x=0.0)
+  controller.update(default_sm)
+
+  assert controller._has_radar_acc_lead
+  assert controller.mode() == "acc"
+
+
+def test_second_vision_only_lead_does_not_force_acc(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  lead_two = MockLeadOne(status=1.0, dRel=20.0, vRel=-10.0)
+  default_sm['radarState'] = MockRadarState(status=0.0, leadTwo=lead_two)
+  default_sm['modelV2'] = MockModelData(valid=True, endpoint_x=0.0)
+  controller.update(default_sm)
+
+  assert not controller._has_radar_acc_lead
+  assert controller.mode() == "blended"
+
+
+def test_inactive_lead_with_radar_marker_does_not_force_acc(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=0.0, radar=True, radarTrackId=7)
+  default_sm['modelV2'] = MockModelData(valid=True, endpoint_x=0.0)
+  controller.update(default_sm)
+
+  assert not controller._has_radar_acc_lead
+  assert controller.mode() == "blended"
+
+
+def test_radarless_car_ignores_marked_radar_track(mock_cp, mock_mpc, default_sm):
+  mock_cp.radarUnavailable = True
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=1.0, radar=True, radarTrackId=7)
+  default_sm['modelV2'] = MockModelData(valid=True, endpoint_x=0.0)
+  controller.update(default_sm)
+
+  assert controller._has_radar_acc_lead
+  assert controller.mode() == "blended"
+
+
+def test_closing_far_radar_lead_returns_to_acc(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=1.0, dRel=120.0, vRel=-25.0, radarTrackId=7)
+  default_sm['modelV2'] = MockModelData(valid=True, endpoint_x=0.0)
+
+  for _ in range(20):
+    controller.update(default_sm)
+
+  assert controller._has_radar_acc_lead
+  assert controller.mode() == "acc"
+
+
+def test_radar_lead_keeps_acc_over_fcw_and_standstill(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=1.0, radar=True, radarTrackId=7)
+  default_sm['carState'].standstill = True
+  default_sm['modelV2'] = MockModelData(valid=True, endpoint_x=0.0, should_stop=True)
+  mock_mpc.crash_cnt = 1
+
+  for _ in range(10):
+    controller.update(default_sm)
+
+  assert controller._has_lead_filtered
+  assert controller._has_mpc_fcw
+  assert controller.mode() == "acc"
+
+
+def test_lead_flicker_hold_prevents_one_frame_mode_flip(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=1.0, radar=True, radarTrackId=7)
+  default_sm['modelV2'] = MockModelData(valid=True, endpoint_x=50.0)
+  for _ in range(2):
+    controller.update(default_sm)
+  assert controller._has_slow_down
+
+  default_sm['radarState'] = MockRadarState(status=0.0)
+  controller.update(default_sm)
+
+  assert controller._has_lead_filtered
+  assert controller.mode() == "acc"
+
+
+def test_radar_lead_continuity_with_vision_fallback_expires_into_confirmed_transition(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=1.0, radar=True, radarTrackId=7)
+  default_sm['modelV2'] = MockModelData(valid=True, endpoint_x=50.0)
+  for _ in range(2):
+    controller.update(default_sm)
+  assert controller._has_slow_down
+
+  default_sm['radarState'] = MockRadarState(status=1.0)
+  for _ in range(WMACConstants.RADAR_LEAD_CONTINUITY_FRAMES):
+    controller.update(default_sm)
+    assert controller._has_radar_acc_lead
+    assert controller.mode() == "acc"
+
+  controller.update(default_sm)
+  assert not controller._has_radar_acc_lead
+  assert controller.mode() == "acc"
+
+  for _ in range(WMACConstants.ENTER_BLENDED_FRAMES - 1):
+    controller.update(default_sm)
+  assert controller.mode() == "blended"
+
+
+def test_radar_lead_short_dropout_guard_expires_without_any_lead(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=1.0, radar=True, radarTrackId=7)
+  controller.update(default_sm)
+
+  default_sm['radarState'] = MockRadarState(status=0.0)
+  for _ in range(WMACConstants.RADAR_LEAD_DROPOUT_FRAMES):
+    controller.update(default_sm)
+    assert controller._has_radar_acc_lead
+
+  controller.update(default_sm)
+  assert not controller._has_radar_acc_lead
+
+
+def test_one_stale_radar_frame_does_not_drop_acc_authority(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  controller.update(default_sm)
+
+  controller.update(default_sm, radar_fresh=False)
+
+  assert not controller._has_current_radar_acc_lead
+  assert controller._has_radar_acc_lead
+  assert controller._radar_acc_lead_frames == WMACConstants.RADAR_LEAD_CONTINUITY_FRAMES - 1
+  assert controller._radar_stale_frames == 1
+  assert controller.mode() == "acc"
+
+
+def test_one_stale_radar_frame_does_not_override_retained_lead_for_model_urgency(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  controller.update(default_sm)
+  default_sm['modelV2'] = MockModelData(valid=False, should_stop=True)
+
+  controller.update(default_sm, radar_fresh=False)
+  assert controller.mode() == "acc"
+
+  controller.update(default_sm, radar_fresh=False)
+  assert controller.mode() == "blended"
+
+
+def test_one_stale_radar_frame_does_not_delay_fcw(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  controller.update(default_sm)
+  mock_mpc.crash_cnt = 1
+
+  controller.update(default_sm, radar_fresh=False)
+
+  assert controller.mode() == "blended"
+
+
+def test_frozen_radar_marker_cannot_rearm_acc_authority(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  controller.update(default_sm)
+
+  for _ in range(WMACConstants.RADAR_STALE_FRAMES - 1):
+    controller.update(default_sm, radar_fresh=False)
+    assert controller._has_radar_acc_lead
+
+  controller.update(default_sm, radar_fresh=False)
+
+  assert not controller._has_current_radar_acc_lead
+  assert not controller._has_radar_acc_lead
+  assert not controller._has_any_lead
+  assert not controller._has_lead_filtered
+
+
+def test_fresh_radar_reacquisition_after_stale_timeout_is_immediate(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  controller.update(default_sm)
+  for _ in range(WMACConstants.RADAR_STALE_FRAMES):
+    controller.update(default_sm, radar_fresh=False)
+
+  default_sm['modelV2'] = MockModelData(valid=True, endpoint_x=0.0)
+  controller.update(default_sm, radar_fresh=False)
+  assert controller.mode() == "blended"
+
+  lead_two = MockLeadOne(status=1.0, radar=True, radarTrackId=8)
+  default_sm['radarState'] = MockRadarState(status=0.0, leadTwo=lead_two)
+  controller.update(default_sm, radar_fresh=True)
+
+  assert controller._radar_stale_frames == 0
+  assert controller._has_current_radar_acc_lead
+  assert controller.mode() == "acc"
+
+
+@pytest.mark.parametrize("urgent_source", ["fcw", "should_stop"])
+def test_no_lead_urgent_slowdown_bypasses_radar_dropout_guard(mock_cp, mock_mpc, default_sm, urgent_source):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=1.0, radar=True, radarTrackId=7)
+  controller.update(default_sm)
+
+  default_sm['radarState'] = MockRadarState(status=0.0)
+  if urgent_source == "fcw":
+    mock_mpc.crash_cnt = 1
+  else:
+    default_sm['modelV2'] = MockModelData(valid=False, should_stop=True)
+  controller.update(default_sm)
+
+  assert not controller._has_radar_acc_lead
+  assert controller.mode() == "blended"
+
+  mock_mpc.crash_cnt = 0
+  default_sm['modelV2'] = MockModelData(valid=True)
+  controller.update(default_sm)
+  assert controller.mode() == "blended"
+
+
+def test_lead_two_radar_authority_continues_with_vision_lead_one(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  lead_two = MockLeadOne(status=1.0, radar=True, radarTrackId=8)
+  default_sm['radarState'] = MockRadarState(status=0.0, leadTwo=lead_two)
+  default_sm['modelV2'] = MockModelData(valid=True, endpoint_x=0.0)
+  controller.update(default_sm)
+  assert controller._has_current_radar_acc_lead
+  assert controller.mode() == "acc"
+
+  default_sm['radarState'] = MockRadarState(status=1.0)
+  for _ in range(WMACConstants.RADAR_LEAD_CONTINUITY_FRAMES):
+    controller.update(default_sm)
+    assert controller._has_radar_acc_lead
+    assert controller.mode() == "acc"
+
+
+def test_alternating_radar_slots_keep_acc_authority(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['modelV2'] = MockModelData(valid=True, endpoint_x=0.0)
+
+  for frame in range(WMACConstants.RADAR_LEAD_CONTINUITY_FRAMES * 2):
+    if frame % 2 == 0:
+      default_sm['radarState'] = MockRadarState(status=1.0, radar=True, radarTrackId=7, leadTwo=MockLeadOne(status=1.0))
+    else:
+      default_sm['radarState'] = MockRadarState(status=1.0, leadTwo=MockLeadOne(status=1.0, radar=True, radarTrackId=8))
+    controller.update(default_sm)
+
+    assert controller._has_current_radar_acc_lead
+    assert controller.mode() == "acc"
+
+
+def test_radar_reacquisition_immediately_restores_acc_after_continuity_expiry(mock_cp, mock_mpc, default_sm):
+  controller = DynamicExperimentalController(mock_cp, mock_mpc, params=MockParams())
+  default_sm['radarState'] = MockRadarState(status=1.0, radar=True, radarTrackId=7)
+  default_sm['modelV2'] = MockModelData(valid=True, endpoint_x=0.0)
+  controller.update(default_sm)
+
+  default_sm['radarState'] = MockRadarState(status=1.0)
+  for _ in range(WMACConstants.RADAR_LEAD_CONTINUITY_FRAMES + 1):
+    controller.update(default_sm)
+  assert not controller._has_radar_acc_lead
+  assert controller.mode() == "blended"
+
+  lead_two = MockLeadOne(status=1.0, radar=True, radarTrackId=8)
+  default_sm['radarState'] = MockRadarState(status=1.0, leadTwo=lead_two)
+  controller.update(default_sm)
+
+  assert controller._has_current_radar_acc_lead
+  assert controller.mode() == "acc"
@@ -0,0 +1,32 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+import math
+
+from openpilot.common.pid import PIDController
+from openpilot.selfdrive.controls.lib.latcontrol_pid import LatControlPID
+
+DECAY_TAU = 2.0  # seconds; starting guess, not validated against a real car
+
+
+class DecayingIntegratorPIDController(PIDController):
+  def __init__(self, *args, decay_tau=DECAY_TAU, **kwargs):
+    super().__init__(*args, **kwargs)
+    self.decay_tau = decay_tau
+
+  def update(self, error, error_rate=0.0, speed=0.0, feedforward=0., freeze_integrator=False):
+    if freeze_integrator:
+      self.i *= math.exp(-self.i_dt / self.decay_tau)
+    return super().update(error, error_rate=error_rate, speed=speed, feedforward=feedforward, freeze_integrator=freeze_integrator)
+
+
+class LatControlPidSmooth(LatControlPID):
+  def __init__(self, CP, CP_SP, CI, dt, decay_tau=DECAY_TAU):
+    super().__init__(CP, CP_SP, CI, dt)
+    self.pid = DecayingIntegratorPIDController(
+      (CP.lateralTuning.pid.kpBP, CP.lateralTuning.pid.kpV),
+      (CP.lateralTuning.pid.kiBP, CP.lateralTuning.pid.kiV),
+      pos_limit=self.steer_max, neg_limit=-self.steer_max, decay_tau=decay_tau)
@@ -0,0 +1,16 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+
+STOPPING_DISTANCE = 0.75
+STOPPED_SPEED = 0.02
+STOPPING_TIME = 2.5
+
+
+class LongControlSP:
+  def should_hold_stopping(self, CS, a_target: float) -> bool:
+    return (self.last_output_accel <= 0.0 and a_target >= self.last_output_accel and CS.vEgo > STOPPED_SPEED and CS.aEgo < 0.0
+            and CS.vEgo <= -CS.aEgo * STOPPING_TIME and CS.vEgo ** 2 <= -2.0 * CS.aEgo * STOPPING_DISTANCE)
@@ -0,0 +1,46 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+
+import numpy as np
+
+from opendbc.car.interfaces import ACCEL_MIN, ACCEL_MAX
+
+
+class LongitudinalMpcSP:
+  def __init__(self) -> None:
+    self._accel_max_trajectory: tuple[float, ...] | None = None
+    self._cruise_accel_max: float | None = None
+    self._jerk_cost_multiplier = 1.0
+    self.last_solution_status = 0
+
+  def set_accel_controller_params(self, accel_max: tuple[float, ...] | None, jerk_cost_multiplier: float,
+                                  cruise_accel_max: float | None = None) -> None:
+    self._accel_max_trajectory = accel_max
+    self._cruise_accel_max = cruise_accel_max
+    self._jerk_cost_multiplier = jerk_cost_multiplier
+
+  def cruise_accel_max(self, stock_accel_max: float) -> float:
+    if self._cruise_accel_max is None or not np.isfinite(self._cruise_accel_max):
+      return stock_accel_max
+    return min(max(self._cruise_accel_max, 0.0), stock_accel_max)
+
+  def scale_jerk_cost(self, jerk_cost: float) -> float:
+    return jerk_cost * self._jerk_cost_multiplier
+
+  def apply_accel_limits(self) -> None:
+    if self._accel_max_trajectory is None:
+      return
+
+    accel_max = np.asarray(self._accel_max_trajectory)
+    if accel_max.shape != self.params[:, 1].shape or accel_max.dtype.kind not in "iuf" or not np.all(np.isfinite(accel_max)):
+      return
+
+    self.params[:, 1] = np.clip(accel_max, 0.0, ACCEL_MAX)
+    self.params[0, 1] = max(self.params[0, 1], float(np.clip(self.x0[2], ACCEL_MIN, ACCEL_MAX)))
+
+  def save_solution_status(self) -> None:
+    self.last_solution_status = self.solution_status
@@ -8,7 +8,9 @@ See the LICENSE.md file in the root directory for more details.
 from openpilot.cereal import messaging, custom
 from opendbc.car import structs
 from openpilot.common.constants import CV
-from openpilot.selfdrive.car.cruise import V_CRUISE_MAX
+from openpilot.common.realtime import DT_MDL
+from openpilot.selfdrive.car.cruise import V_CRUISE_MAX, V_CRUISE_UNSET
+from openpilot.sunnypilot.selfdrive.controls.lib.accel_controller.accel_controller import AccelController, AccelControllerState
 from openpilot.sunnypilot.selfdrive.controls.lib.dec.dec import DynamicExperimentalController
 from openpilot.sunnypilot.selfdrive.controls.lib.e2e_alerts_helper import E2EAlertsHelper
 from openpilot.sunnypilot.selfdrive.controls.lib.smart_cruise_control.smart_cruise_control import SmartCruiseControl
@@ -22,9 +24,10 @@ LongitudinalPlanSource = custom.LongitudinalPlanSP.LongitudinalPlanSource


 class LongitudinalPlannerSP:
-  def __init__(self, CP: structs.CarParams, CP_SP: structs.CarParamsSP, mpc):
+  def __init__(self, CP: structs.CarParams, CP_SP: structs.CarParamsSP, mpc, dt: float = DT_MDL):
+    self.mpc = mpc
+    self.accel_controller = AccelController(CP, dt=dt)
    self.events_sp = EventsSP()
-    self.resolver = SpeedLimitResolver()
    self.dec = DynamicExperimentalController(CP, mpc)
    self.scc = SmartCruiseControl()
    self.resolver = SpeedLimitResolver()
@@ -32,6 +35,8 @@ class LongitudinalPlannerSP:
    self.generation = int(model_bundle.generation) if (model_bundle := get_active_bundle()) else None
    self.source = LongitudinalPlanSource.cruise
    self.e2e_alerts_helper = E2EAlertsHelper()
+    self._radar_log_mono_time = None
+    self._radar_fresh_this_cycle = True

    self.output_v_target = 0.
    self.output_a_target = 0.
@@ -43,6 +48,37 @@ class LongitudinalPlannerSP:

    return experimental_mode and self.dec.mode() == "blended"

+  def update_accel_controller(self, sm: messaging.SubMaster, v_cruise: float, prev_accel_constraint: bool,
+                              stock_accel_max: float, reset_state: bool) -> tuple[bool, float]:
+    is_e2e = self.is_e2e(sm)
+    force_decel = sm['controlsState'].forceDecel
+    previous_mpc_failed = self.mpc.last_solution_status != 0
+    if previous_mpc_failed:
+      self.accel_controller.reset()
+
+    self.accel_controller.update(
+      sm['radarState'], base_speed=self.output_v_target, v_ego=sm['carState'].vEgo, a_ego=sm['carState'].aEgo,
+      follow_personality=sm['selfdriveState'].personality, acc_selected=not is_e2e and not previous_mpc_failed,
+      engaged=not reset_state and not force_decel, cruise_initialized=sm['carState'].vCruise != V_CRUISE_UNSET,
+      stock_accel_max=stock_accel_max if self.allow_throttle else 0.0, previous_should_stop=self.output_should_stop,
+      radar_fresh=self._radar_fresh_this_cycle, previous_mpc_source=self.mpc.source, planner_speed=self.v_desired_filter.x,
+      planner_accel=self.a_desired,
+    )
+    controller = self.accel_controller
+    actuating = controller.is_active and not is_e2e and not force_decel and not previous_mpc_failed
+    valid_lead_stop_hold = actuating and controller.state == AccelControllerState.stopHold and controller.selected_lead >= 0
+    controller_v_cruise = v_cruise if valid_lead_stop_hold else min(v_cruise, controller.output_v_target) if actuating else v_cruise
+    accel_max = controller.mpc_accel_max if actuating else None
+    cruise_accel_max = controller.cruise_accel_max if actuating else None
+    jerk_cost_multiplier = controller.get_jerk_cost_multiplier(
+      actuating, prev_accel_constraint, v_cruise - controller_v_cruise, previous_mpc_failed,
+    )
+    self.mpc.set_accel_controller_params(accel_max, jerk_cost_multiplier, cruise_accel_max)
+    return is_e2e, controller_v_cruise
+
+  def update_should_stop(self, should_stop: bool) -> bool:
+    return self.accel_controller.update_should_stop(should_stop)
+
  def update_targets(self, sm: messaging.SubMaster, v_ego: float, a_ego: float, v_cruise: float) -> tuple[float, float]:
    CS = sm['carState']
    v_cruise_cluster_kph = min(CS.vCruiseCluster, V_CRUISE_MAX)
@@ -73,9 +109,19 @@ class LongitudinalPlannerSP:
    self.output_v_target, self.output_a_target = targets[self.source]
    return self.output_v_target, self.output_a_target

+  def _update_radar_freshness(self, sm: messaging.SubMaster) -> bool:
+    radar_log_mono_time = sm.logMonoTime['radarState']
+    radar_healthy = sm.valid['radarState'] and sm.alive['radarState']
+    radar_advanced = self._radar_log_mono_time is None or radar_log_mono_time > self._radar_log_mono_time
+    if radar_advanced:
+      self._radar_log_mono_time = radar_log_mono_time
+    return radar_healthy and radar_advanced
+
  def update(self, sm: messaging.SubMaster) -> None:
+    self._radar_fresh_this_cycle = self._update_radar_freshness(sm)
+    self.accel_controller.update_params()
    self.events_sp.clear()
-    self.dec.update(sm)
+    self.dec.update(sm, radar_fresh=self._radar_fresh_this_cycle, planner_accel=self.output_a_target)
    self.e2e_alerts_helper.update(sm, self.events_sp)

  def publish_longitudinal_plan_sp(self, sm: messaging.SubMaster, pm: messaging.PubMaster) -> None:
@@ -95,6 +141,12 @@ class LongitudinalPlannerSP:
    dec.enabled = self.dec.enabled()
    dec.active = self.dec.active()

+    accelController = longitudinalPlanSP.accelController
+    accelController.enabled = self.accel_controller.is_enabled
+    accelController.active = self.accel_controller.is_active
+    accelController.profile = self.accel_controller.profile
+    accelController.state = self.accel_controller.state
+
    # Smart Cruise Control
    smartCruiseControl = longitudinalPlanSP.smartCruiseControl
    # Vision Control
@@ -0,0 +1,84 @@
+"""
+Copyright (c) 2021-, rav4kumar, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+import numpy as np
+
+from openpilot.common.constants import CV
+from openpilot.common.realtime import DT_MDL
+from openpilot.common.params import Params
+
+NEARSIDE_PROB = 0.2
+EDGE_PROB = 0.35
+EDGE_REACTION_TIME = 1.0
+EDGE_CLEAR_TIME = 0.3
+MIN_SPEED = 20 * CV.MPH_TO_MS
+
+
+class RoadEdgeLaneChangeController:
+  def __init__(self, desire_helper):
+    self.DH = desire_helper
+    self.params = Params()
+    self.enabled = self.params.get_bool("RoadEdgeLaneChangeEnabled")
+    self.param_read_counter = 0
+    self.left_edge_detected = False
+    self.right_edge_detected = False
+    self.left_edge_timer = 0.0
+    self.right_edge_timer = 0.0
+    self.left_clear_timer = 0.0
+    self.right_clear_timer = 0.0
+
+  def read_params(self) -> None:
+    self.enabled = self.params.get_bool("RoadEdgeLaneChangeEnabled")
+
+  def update_params(self) -> None:
+    if self.param_read_counter % 50 == 0:
+      self.read_params()
+    self.param_read_counter += 1
+
+  def reset(self) -> None:
+    self.left_edge_detected = False
+    self.right_edge_detected = False
+    self.left_edge_timer = 0.0
+    self.right_edge_timer = 0.0
+    self.left_clear_timer = 0.0
+    self.right_clear_timer = 0.0
+
+  def update(self, road_edge_stds, lane_line_probs, v_ego: float) -> None:
+    self.update_params()
+
+    if not self.enabled or v_ego < MIN_SPEED:
+      self.reset()
+      return
+
+    left_edge_prob = np.clip(1.0 - road_edge_stds[0], 0.0, 1.0)
+    right_edge_prob = np.clip(1.0 - road_edge_stds[1], 0.0, 1.0)
+    left_lane_prob = lane_line_probs[0]
+    right_lane_prob = lane_line_probs[3]
+
+    left_cond = left_edge_prob > EDGE_PROB and left_lane_prob < NEARSIDE_PROB and right_lane_prob >= left_lane_prob
+    right_cond = right_edge_prob > EDGE_PROB and right_lane_prob < NEARSIDE_PROB and left_lane_prob >= right_lane_prob
+
+    if left_cond:
+      self.left_edge_timer = min(self.left_edge_timer + DT_MDL, EDGE_REACTION_TIME + EDGE_CLEAR_TIME)
+      self.left_clear_timer = 0.0
+      if self.left_edge_timer > EDGE_REACTION_TIME:
+        self.left_edge_detected = True
+    else:
+      self.left_clear_timer += DT_MDL
+      if self.left_clear_timer > EDGE_CLEAR_TIME:
+        self.left_edge_timer = 0.0
+        self.left_edge_detected = False
+
+    if right_cond:
+      self.right_edge_timer = min(self.right_edge_timer + DT_MDL, EDGE_REACTION_TIME + EDGE_CLEAR_TIME)
+      self.right_clear_timer = 0.0
+      if self.right_edge_timer > EDGE_REACTION_TIME:
+        self.right_edge_detected = True
+    else:
+      self.right_clear_timer += DT_MDL
+      if self.right_clear_timer > EDGE_CLEAR_TIME:
+        self.right_edge_timer = 0.0
+        self.right_edge_detected = False
@@ -151,8 +151,8 @@ class SmartCruiseControlMap:
        a = 0.5 * TARGET_JERK
        b = self.a_ego
        c = self.v_ego - tv
-        t_a = -1 * ((b**2 - 4 * a * c) ** 0.5 + b) / 2 * a
-        t_b = ((b**2 - 4 * a * c) ** 0.5 - b) / 2 * a
+        t_a = -1 * ((b**2 - 4 * a * c) ** 0.5 + b) / (2 * a)
+        t_b = ((b**2 - 4 * a * c) ** 0.5 - b) / (2 * a)
        if not isinstance(t_a, complex) and t_a > 0:
          t = t_a
        else:
@@ -4,13 +4,17 @@ Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
 This file is part of sunnypilot and is licensed under the MIT License.
 See the LICENSE.md file in the root directory for more details.
 """
+import json
+import math
 import platform

+import pytest
+
 from openpilot.cereal import custom
 from openpilot.common.params import Params
 from openpilot.common.realtime import DT_MDL
 from openpilot.selfdrive.car.cruise import V_CRUISE_UNSET
-from openpilot.sunnypilot.selfdrive.controls.lib.smart_cruise_control.map_controller import SmartCruiseControlMap
+from openpilot.sunnypilot.selfdrive.controls.lib.smart_cruise_control.map_controller import R, SmartCruiseControlMap

 MapState = VisionState = custom.LongitudinalPlanSP.SmartCruiseControl.MapState

@@ -55,4 +59,17 @@ class TestSmartCruiseControlMap:
      self.scc_m.update(True, False, 0., 0., 0.)
    assert self.scc_m.state == VisionState.enabled

+  def test_moderate_curve(self):
+    # Regression: `... / 2 * a` parsed as `(.../2)*a` instead of `.../(2*a)`,
+    # making max_d ~11x too small so the moderate-curve branch never tripped.
+    # v_ego=25, a_ego=0, tv=24: fixed max_d≈45m vs buggy ≈4m at a 40m waypoint.
+    waypoint_lon_deg = (40.0 / R) * (180.0 / math.pi)
+    self.mem_params.put("LastGPSPosition", json.dumps({"latitude": 0.0, "longitude": 0.0}), block=True)
+    self.mem_params.put("MapTargetVelocities",
+                        json.dumps([{"latitude": 0.0, "longitude": waypoint_lon_deg, "velocity": 24.0}]), block=True)
+
+    self.scc_m.update(True, False, 25.0, 0.0, 30.0)
+
+    assert self.scc_m.v_target == pytest.approx(24.0)
+
  # TODO-SP: mock data from modelV2 to test other states
@@ -4,6 +4,7 @@ Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
 This file is part of sunnypilot and is licensed under the MIT License.
 See the LICENSE.md file in the root directory for more details.
 """
+from types import SimpleNamespace
 from typing import Any

 import numpy as np
@@ -15,8 +16,12 @@ from openpilot.common.params import Params
 from openpilot.common.realtime import DT_MDL
 from openpilot.selfdrive.car.cruise import V_CRUISE_UNSET
 from openpilot.selfdrive.modeld.constants import ModelConstants
+from openpilot.sunnypilot.selfdrive.controls.lib.longitudinal_planner import LongitudinalPlannerSP, LongitudinalPlanSource
 from openpilot.sunnypilot.selfdrive.controls.lib.smart_cruise_control import MIN_V
-from openpilot.sunnypilot.selfdrive.controls.lib.smart_cruise_control.vision_controller import SmartCruiseControlVision, _ENTERING_PRED_LAT_ACC_TH
+from openpilot.sunnypilot.selfdrive.controls.lib.smart_cruise_control.vision_controller import (
+  _A_LAT_REG_MAX, _BELOW_EGO_TARGET_RELEASE_RATE, _ENTERING_PRED_LAT_ACC_TH, _MIN_ACTIVATION_SPEED,
+  _RELIEF_CONFIRMATION_FRAMES, _TARGET_RELEASE_RATE, SmartCruiseControlVision,
+)

 VisionState = custom.LongitudinalPlanSP.SmartCruiseControl.VisionState

@@ -120,6 +125,21 @@ class TestSmartCruiseControlVision:
  def reset_params(self):
    self.params.put_bool("SmartCruiseControlVision", True, block=True)

+  def set_lat_accels(self, current: float, predicted: float, v_ego: float = 20., model_speed: float = 20.) -> None:
+    self.sm['controlsState'].curvature = current / v_ego**2
+    self.sm['modelV2'].velocity.x = [model_speed] * len(ModelConstants.T_IDXS)
+    self.sm['modelV2'].orientationRate.z = [predicted / model_speed] * len(ModelConstants.T_IDXS)
+
+  def update_lat_accels(self, current: float, predicted: float, cruise: float = 30., a_ego: float = 0.,
+                        v_ego: float = 20., model_speed: float = 20.) -> None:
+    self.set_lat_accels(current, predicted, v_ego, model_speed)
+    self.scc_v.update(self.sm, True, False, v_ego, a_ego, cruise)
+
+  def enter_curve(self, predicted: float = 2.2) -> None:
+    self.update_lat_accels(0.5, predicted)
+    self.update_lat_accels(0.5, predicted)
+    assert self.scc_v.state == VisionState.entering
+
  def test_initial_state(self):
    assert self.scc_v.state == VisionState.disabled
    assert not self.scc_v.is_active
@@ -145,6 +165,253 @@ class TestSmartCruiseControlVision:
      self.scc_v.update(self.sm, True, False, 0., 0., 0.)
    assert self.scc_v.state == VisionState.enabled

+  def test_unconfirmed_leaving_and_reentry_only_shape_speed(self):
+    self.enter_curve()
+    targets = [self.scc_v.output_v_target]
+
+    self.update_lat_accels(2., 2.2, a_ego=-0.8)
+    assert self.scc_v.state == VisionState.turning
+    assert self.scc_v.output_a_target == -0.8
+    targets.append(self.scc_v.output_v_target)
+
+    self.update_lat_accels(1.2, 1.2, a_ego=0.3)
+    assert self.scc_v.state == VisionState.leaving
+    assert self.scc_v.output_a_target == 0.3
+    targets.append(self.scc_v.output_v_target)
+
+    self.update_lat_accels(1., 3., a_ego=-1.2)
+    assert self.scc_v.state == VisionState.entering
+    assert self.scc_v.output_a_target == -1.2
+    targets.append(self.scc_v.output_v_target)
+
+    entering, turning, leaving, reentering = targets
+    assert turning == pytest.approx(entering)
+    assert 0. < leaving - turning <= _BELOW_EGO_TARGET_RELEASE_RATE * DT_MDL + 1e-9
+    assert reentering < leaving
+
+  def test_new_curve_interrupts_confirmed_release_immediately(self):
+    self.enter_curve()
+    for _ in range(_RELIEF_CONFIRMATION_FRAMES + 1):
+      self.update_lat_accels(0.8, 0.8)
+    releasing_v_target = self.scc_v.output_v_target
+    assert self.scc_v.state == VisionState.leaving
+
+    self.update_lat_accels(0.8, 3., a_ego=-0.7)
+    assert self.scc_v.state == VisionState.entering
+    assert self.scc_v.output_v_target < releasing_v_target
+    assert self.scc_v.output_a_target == -0.7
+
+  @pytest.mark.parametrize("planner_accel", (-2., -0.5, 0., 0.8))
+  def test_planner_acceleration_passes_through_exactly(self, planner_accel):
+    self.enter_curve()
+    self.update_lat_accels(0.5, 2.2, a_ego=planner_accel)
+    assert self.scc_v.output_a_target == planner_accel
+
+  def test_planner_acceleration_passes_through_all_states(self):
+    cases = (
+      (False, False, 0.5, 2.2, -0.2, VisionState.disabled),
+      (True, False, 0.5, 0.8, 0.1, VisionState.enabled),
+      (True, False, 0.5, 2.2, -0.4, VisionState.entering),
+      (True, False, 2., 2.2, -0.8, VisionState.turning),
+      (True, False, 1.2, 1.2, 0.3, VisionState.leaving),
+      (True, True, 1.2, 1.2, 0.6, VisionState.overriding),
+    )
+    for long_enabled, override, current, predicted, planner_accel, state in cases:
+      self.set_lat_accels(current, predicted)
+      self.scc_v.update(self.sm, long_enabled, override, 20., planner_accel, 30.)
+      assert self.scc_v.state == state
+      assert self.scc_v.output_a_target == planner_accel
+
+  def test_jitter_requires_confirmed_relief_then_releases_smoothly(self):
+    self.enter_curve()
+    previous_v_target = self.scc_v.output_v_target
+
+    for frame in range(_RELIEF_CONFIRMATION_FRAMES * 2):
+      self.update_lat_accels(1., 1.05 if frame % 2 == 0 else 1.15)
+      assert self.scc_v.state == VisionState.entering
+      assert self.scc_v.output_v_target >= previous_v_target
+      assert self.scc_v.output_v_target - previous_v_target <= _BELOW_EGO_TARGET_RELEASE_RATE * DT_MDL + 1e-9
+      previous_v_target = self.scc_v.output_v_target
+
+    for _ in range(_RELIEF_CONFIRMATION_FRAMES):
+      self.update_lat_accels(1.15, 0.8)
+      assert self.scc_v.state == VisionState.entering
+      assert 0. <= self.scc_v.output_v_target - previous_v_target <= _BELOW_EGO_TARGET_RELEASE_RATE * DT_MDL + 1e-9
+      previous_v_target = self.scc_v.output_v_target
+
+    release_cruise = 30.
+    for _ in range(_RELIEF_CONFIRMATION_FRAMES - 1):
+      self.update_lat_accels(0.8, 0.8, release_cruise)
+      assert self.scc_v.state == VisionState.entering
+      assert 0. <= self.scc_v.output_v_target - previous_v_target <= _BELOW_EGO_TARGET_RELEASE_RATE * DT_MDL + 1e-9
+      previous_v_target = self.scc_v.output_v_target
+
+    active_v_targets = [previous_v_target]
+    for _ in range(int((release_cruise - previous_v_target) / (_TARGET_RELEASE_RATE * DT_MDL)) + 10):
+      self.update_lat_accels(0.8, 0.8, release_cruise)
+      if not self.scc_v.is_active:
+        break
+      assert self.scc_v.state == VisionState.leaving
+      assert self.scc_v.output_v_target != V_CRUISE_UNSET
+      active_v_targets.append(self.scc_v.output_v_target)
+
+    assert self.scc_v.state == VisionState.enabled
+    assert self.scc_v.output_v_target == V_CRUISE_UNSET
+    assert active_v_targets[-1] == pytest.approx(release_cruise)
+    assert np.all((np.diff(active_v_targets) >= 0.) &
+                  (np.diff(active_v_targets) <= _BELOW_EGO_TARGET_RELEASE_RATE * DT_MDL + 1e-9))
+
+  def test_target_release_slows_after_reaching_ego_speed(self):
+    self.enter_curve()
+
+    for _ in range(100):
+      previous_v_target = self.scc_v.output_v_target
+      self.update_lat_accels(0.8, 0.8)
+      if previous_v_target >= self.scc_v.v_ego:
+        rise = self.scc_v.output_v_target - previous_v_target
+        assert 0. < rise <= _TARGET_RELEASE_RATE * DT_MDL + 1e-9
+        break
+    else:
+      pytest.fail("curve target did not release to ego speed")
+
+  def test_curve_target_is_independent_of_ego_speed(self):
+    model_speed = 24.
+    predicted_yaw_rate = 0.12
+    predicted_lat_accel = model_speed * predicted_yaw_rate
+    expected_v_target = (_A_LAT_REG_MAX / (predicted_yaw_rate / model_speed)) ** 0.5
+    targets = []
+
+    for v_ego in (18., 28.):
+      controller = SmartCruiseControlVision()
+      self.set_lat_accels(0.5, predicted_lat_accel, v_ego, model_speed)
+      controller.update(self.sm, True, False, v_ego, 0., 30.)
+      controller.update(self.sm, True, False, v_ego, 0., 30.)
+      assert controller.state == VisionState.entering
+      targets.append(controller.v_target)
+
+    assert targets[0] == pytest.approx(expected_v_target)
+    assert targets[1] == pytest.approx(expected_v_target)
+
+  def test_curve_target_respects_minimum_speed_floor(self):
+    model_speed = 10.
+    predicted_yaw_rate = 2.
+    self.set_lat_accels(0.5, model_speed * predicted_yaw_rate, model_speed=model_speed)
+    self.scc_v.update(self.sm, True, False, 20., 0., 30.)
+    self.scc_v.update(self.sm, True, False, 20., 0., 30.)
+
+    assert self.scc_v.state == VisionState.entering
+    assert self.scc_v.v_target < MIN_V
+    assert self.scc_v.output_v_target == pytest.approx(MIN_V)
+
+  @pytest.mark.parametrize(
+    ("velocities", "yaw_rates"),
+    [([], []), ([np.nan] * len(ModelConstants.T_IDXS), [np.nan] * len(ModelConstants.T_IDXS)), ([20.] * 5, [0.1] * 3)],
+    ids=("empty", "nonfinite", "mismatched"),
+  )
+  def test_model_vector_edges_remain_finite(self, velocities, yaw_rates):
+    self.sm['modelV2'].velocity.x = velocities
+    self.sm['modelV2'].orientationRate.z = yaw_rates
+    self.scc_v.update(self.sm, True, False, 20., 0., 30.)
+    self.scc_v.update(self.sm, True, False, 20., 0., 30.)
+
+    assert all(np.isfinite(value) for value in (
+      self.scc_v.current_lat_acc, self.scc_v.max_pred_lat_acc, self.scc_v.v_target,
+      self.scc_v.output_v_target, self.scc_v.output_a_target,
+    ))
+
+  @pytest.mark.parametrize("launch_speed", (5.75, 9.9, _MIN_ACTIVATION_SPEED))
+  def test_vision_control_does_not_steal_launch(self, launch_speed):
+    self.set_lat_accels(0.5, 3., launch_speed)
+    self.scc_v.update(self.sm, True, False, launch_speed, 0., 30.)
+    self.scc_v.update(self.sm, True, False, launch_speed, 0., 30.)
+
+    assert launch_speed <= _MIN_ACTIVATION_SPEED
+    assert self.scc_v.state == VisionState.enabled
+    assert not self.scc_v.is_active
+    assert self.scc_v.output_v_target == V_CRUISE_UNSET
+
+  def test_vision_control_can_activate_above_launch_range(self):
+    speed = _MIN_ACTIVATION_SPEED + 0.01
+    self.set_lat_accels(0.5, 3., speed)
+    self.scc_v.update(self.sm, True, False, speed, 0., 30.)
+    self.scc_v.update(self.sm, True, False, speed, 0., 30.)
+
+    assert self.scc_v.state == VisionState.entering
+    assert self.scc_v.is_active
+
+  def test_sequential_curve_tightens_immediately_and_releases_bounded(self):
+    self.enter_curve(3.)
+    for _ in range(20):
+      self.update_lat_accels(0.5, 3.)
+    restrictive_v_target = self.scc_v.output_v_target
+
+    self.update_lat_accels(0.5, 1.4, a_ego=0.4)
+    first_relief_v_target = self.scc_v.output_v_target
+    assert self.scc_v.state == VisionState.entering
+    assert 0. < first_relief_v_target - restrictive_v_target <= _BELOW_EGO_TARGET_RELEASE_RATE * DT_MDL + 1e-9
+    assert self.scc_v.output_a_target == 0.4
+
+    self.update_lat_accels(0.5, 1.4)
+    assert 0. <= self.scc_v.output_v_target - first_relief_v_target <= _BELOW_EGO_TARGET_RELEASE_RATE * DT_MDL + 1e-9
+
+    self.update_lat_accels(0.5, 3., a_ego=-0.6)
+    assert self.scc_v.state == VisionState.entering
+    assert self.scc_v.output_v_target == pytest.approx(restrictive_v_target)
+    assert self.scc_v.output_a_target == -0.6
+
+    for _ in range(4):
+      self.update_lat_accels(0.5, 1.4)
+      assert 0. < self.scc_v.output_v_target - restrictive_v_target <= _BELOW_EGO_TARGET_RELEASE_RATE * DT_MDL + 1e-9
+      self.update_lat_accels(0.5, 3.)
+      assert self.scc_v.output_v_target == pytest.approx(restrictive_v_target)
+
+  def test_acceleration_is_continuous_through_planner_arbitration(self):
+    car_control = messaging.new_message('carControl')
+    car_control.carControl.enabled = True
+    car_control.carControl.cruiseControl.override = False
+    self.sm['carControl'] = car_control.carControl
+    self.sm['carState'].vCruiseCluster = 108.
+
+    planner = LongitudinalPlannerSP.__new__(LongitudinalPlannerSP)
+    planner.scc = SimpleNamespace(
+      vision=self.scc_v,
+      map=SimpleNamespace(output_v_target=V_CRUISE_UNSET, output_a_target=0.),
+      update=lambda sm, enabled, override, v_ego, a_ego, v_cruise: self.scc_v.update(
+        sm, enabled, override, v_ego, a_ego, v_cruise),
+    )
+    planner.resolver = SimpleNamespace(
+      speed_limit_valid=False, speed_limit_last_valid=False, speed_limit=0., speed_limit_final_last=0., distance=0.,
+      update=lambda _v_ego, _sm: None,
+    )
+    planner.sla = SimpleNamespace(
+      output_v_target=V_CRUISE_UNSET, output_a_target=0., update=lambda *_args: None,
+    )
+    planner.events_sp = SimpleNamespace()
+
+    self.set_lat_accels(0.5, 2.2)
+    planner.update_targets(self.sm, 20., -0.8, 30.)
+    planner.update_targets(self.sm, 20., -0.8, 30.)
+    assert planner.source == LongitudinalPlanSource.sccVision
+    assert planner.output_a_target == -0.8
+
+    for planner_accel in (-2., 0.5, -0.2):
+      planner.update_targets(self.sm, 20., planner_accel, 30.)
+      assert planner.source == LongitudinalPlanSource.sccVision
+      assert planner.output_a_target == planner_accel
+
+    self.set_lat_accels(0.8, 0.8)
+    for _ in range(int(30. / (_TARGET_RELEASE_RATE * DT_MDL)) + 10):
+      planner.update_targets(self.sm, 20., 0.4, 30.)
+      assert planner.output_a_target == 0.4
+      if planner.source == LongitudinalPlanSource.cruise:
+        break
+    else:
+      pytest.fail("SCC Vision did not release to cruise")
+
+    planner.update_targets(self.sm, 20., 0.4, 30.)
+    assert self.scc_v.state == VisionState.enabled
+    assert planner.source == LongitudinalPlanSource.cruise
+
  @pytest.mark.parametrize(
    "case, should_enter",
    [
@@ -0,0 +1,82 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+import gc
+
+import numpy as np
+
+from openpilot.sunnypilot.selfdrive.controls.lib.longitudinal_planner import LongitudinalPlanSource
+from openpilot.sunnypilot.selfdrive.controls.lib.smart_cruise_control.vision_controller import _A_LAT_REG_MAX
+from openpilot.sunnypilot.selfdrive.test.longitudinal_maneuvers.plant import PlantSP as Plant
+
+
+def _run_constant_curve(*, scc_enabled: bool, cruise: float, duration: float = 70.) -> dict[str, np.ndarray]:
+  gc.collect()
+  curvature = 0.005
+  plant = Plant(lead_relevancy=False, speed=30., actuator_delay=0.15, actuator_lag=0.20)
+  planner = plant.planner
+  planner.accel_controller.enabled = False
+  planner.accel_controller.update_params = lambda: None
+  planner.dec._enabled = False
+  planner.dec._read_params = lambda: None
+  planner.scc.map.enabled = False
+  planner.scc.map.update_params = lambda: None
+  planner.scc.vision.enabled = scc_enabled
+  planner.scc.vision._update_params = lambda: None
+
+  if scc_enabled:
+    original_update_calculations = planner.scc.vision._update_calculations
+
+    def inject_constant_curvature(sm):
+      velocities = np.asarray(sm['modelV2'].velocity.x, dtype=float)
+      sm['modelV2'].orientationRate.z = (curvature * velocities).tolist()
+      sm['controlsState'].curvature = curvature
+      original_update_calculations(sm)
+
+    planner.scc.vision._update_calculations = inject_constant_curvature
+
+  original_update = planner.update
+
+  def enable_longitudinal(sm):
+    sm['carControl'].enabled = True
+    sm['carControl'].longActive = True
+    original_update(sm)
+
+  planner.update = enable_longitudinal
+  rows = []
+  while plant.current_time < duration:
+    output = plant.step(v_cruise=cruise)
+    rows.append((
+      plant.current_time, output['speed'], planner.mpc.last_solution_status, output['should_stop'],
+      planner.scc.vision.is_active, planner.source == LongitudinalPlanSource.sccVision,
+      planner.scc.vision.output_v_target,
+    ))
+
+  data = np.asarray(rows, dtype=float)
+  gc.collect()
+  return {
+    'time': data[:, 0], 'speed': data[:, 1], 'solver_status': data[:, 2], 'should_stop': data[:, 3],
+    'active': data[:, 4], 'scc_source': data[:, 5], 'target': data[:, 6],
+  }
+
+
+def test_constant_curve_recovers_like_stock_speed_cap():
+  target = (_A_LAT_REG_MAX / 0.005) ** 0.5
+  scc = _run_constant_curve(scc_enabled=True, cruise=30.)
+  stock = _run_constant_curve(scc_enabled=False, cruise=target)
+  scc_final = scc['speed'][scc['time'] >= 60.]
+  stock_final = stock['speed'][stock['time'] >= 60.]
+
+  assert not scc['solver_status'].any()
+  assert not stock['solver_status'].any()
+  assert not scc['should_stop'].any()
+  assert np.all(scc['active'][scc['time'] >= 60.])
+  assert np.all(scc['scc_source'][scc['time'] >= 60.])
+  assert np.allclose(scc['target'][scc['time'] >= 60.], target)
+  assert scc_final.min() >= target - 1.
+  assert abs(scc_final.mean() - stock_final.mean()) < 0.5
+  assert abs(scc_final.min() - stock_final.min()) < 1.
+  assert abs(scc_final.max() - stock_final.max()) < 1.
@@ -29,19 +29,11 @@ _FINISH_LAT_ACC_TH = 1.1  # Lat Acc threshold to trigger the end of the turn cyc

 _A_LAT_REG_MAX = 2.  # Maximum lateral acceleration

-_NO_OVERSHOOT_TIME_HORIZON = 4.  # s. Time to use for velocity desired based on a_target when not overshooting.
-
-# Lookup table for the minimum smooth deceleration during the ENTERING state
-# depending on the actual maximum absolute lateral acceleration predicted on the turn ahead.
-_ENTERING_SMOOTH_DECEL_V = [-0.2, -1.]  # min decel value allowed on ENTERING state
-_ENTERING_SMOOTH_DECEL_BP = [1.3, 3.]  # absolute value of lat acc ahead
-
-# Lookup table for the acceleration for the TURNING state
-# depending on the current lateral acceleration of the vehicle.
-_TURNING_ACC_V = [0.5, 0., -0.4]  # acc value
-_TURNING_ACC_BP = [1.5, 2.3, 3.]  # absolute value of current lat acc
-
-_LEAVING_ACC = 0.5  # Conformable acceleration to regain speed while leaving a turn.
+_RELIEF_CONFIRMATION_FRAMES = max(1, int(round(0.5 / DT_MDL)))
+_TARGET_RELEASE_RATE = 1.  # m/s^2
+_BELOW_EGO_TARGET_RELEASE_RATE = 3.  # m/s^2
+_MIN_PRED_SPEED = 1.  # m/s
+_MIN_ACTIVATION_SPEED = 10.  # m/s


 class SmartCruiseControlVision:
@@ -65,13 +57,26 @@ class SmartCruiseControlVision:
    self.state = VisionState.disabled
    self.current_lat_acc = 0.
    self.max_pred_lat_acc = 0.
+    self.relief_frames = 0
+
+  def _v_demand(self) -> float:
+    return max(MIN_V, min(self.v_target, self.v_cruise_setpoint))
+
+  def _released_v_target(self) -> float:
+    demand = self._v_demand()
+    if demand < self.output_v_target:
+      return demand
+    release_rate = _BELOW_EGO_TARGET_RELEASE_RATE if self.output_v_target < min(self.v_ego, demand) else _TARGET_RELEASE_RATE
+    return min(demand, self.output_v_target + release_rate * DT_MDL)

  def get_a_target_from_control(self) -> float:
-    return self.a_target
+    return self.a_ego

  def get_v_target_from_control(self) -> float:
    if self.is_active:
-      return max(self.v_target, MIN_V) + self.a_target * _NO_OVERSHOOT_TIME_HORIZON
+      if self.output_v_target == V_CRUISE_UNSET:
+        return self._v_demand()
+      return self._released_v_target()

    return V_CRUISE_UNSET

@@ -82,25 +87,27 @@ class SmartCruiseControlVision:
  def _update_calculations(self, sm: messaging.SubMaster) -> None:
    if not self.long_enabled:
      return
-    else:
-      rate_plan = np.array(np.abs(sm['modelV2'].orientationRate.z))
-      vel_plan = np.array(sm['modelV2'].velocity.x)

-      self.current_lat_acc = self.v_ego ** 2 * abs(sm['controlsState'].curvature)
+    rate_plan = np.asarray(np.abs(sm['modelV2'].orientationRate.z), dtype=float)
+    vel_plan = np.asarray(sm['modelV2'].velocity.x, dtype=float)
+    size = min(len(rate_plan), len(vel_plan))
+    rate_plan, vel_plan = rate_plan[:size], vel_plan[:size]
+    valid = np.isfinite(rate_plan) & np.isfinite(vel_plan) & (vel_plan >= _MIN_PRED_SPEED)

-      # get the maximum lat accel from the model
-      predicted_lat_accels = rate_plan * vel_plan
-      self.max_pred_lat_acc = np.percentile(predicted_lat_accels, 97)
-
-      # get the maximum curve based on the current velocity
-      v_ego = max(self.v_ego, 0.1)  # ensure a value greater than 0 for calculations
-      max_curve = self.max_pred_lat_acc / (v_ego**2)
-
-      # Get the target velocity for the maximum curve
-      self.v_target = (_A_LAT_REG_MAX / max_curve) ** 0.5
+    self.current_lat_acc = self.v_ego ** 2 * abs(sm['controlsState'].curvature)
+    self.max_pred_lat_acc = 0.
+    self.v_target = V_CRUISE_UNSET
+    if np.any(valid):
+      self.max_pred_lat_acc = float(np.percentile(rate_plan[valid] * vel_plan[valid], 97))
+      max_pred_curvature = float(np.percentile(rate_plan[valid] / vel_plan[valid], 97))
+      if max_pred_curvature > 0.:
+        self.v_target = min(float((_A_LAT_REG_MAX / max_pred_curvature) ** 0.5), V_CRUISE_UNSET)

  def _update_state_machine(self) -> tuple[bool, bool]:
    # ENABLED, ENTERING, TURNING, LEAVING, OVERRIDING
+    relief = self.current_lat_acc < _FINISH_LAT_ACC_TH and self.max_pred_lat_acc < _ABORT_ENTERING_PRED_LAT_ACC_TH
+    self.relief_frames = self.relief_frames + 1 if self.state in ACTIVE_STATES and relief else 0
+
    if self.state != VisionState.disabled:
      # longitudinal and feature disable always have priority in a non-disabled state
      if not self.long_enabled or not self.enabled:
@@ -112,7 +119,7 @@ class SmartCruiseControlVision:
        # ENABLED
        if self.state == VisionState.enabled:
          # Do not enter a turn control cycle if the speed is low.
-          if self.v_ego <= MIN_V:
+          if self.v_ego <= _MIN_ACTIVATION_SPEED:
            pass
          # If significant lateral acceleration is predicted ahead, then move to Entering turn state.
          elif self.max_pred_lat_acc >= _ENTERING_PRED_LAT_ACC_TH:
@@ -128,23 +135,26 @@ class SmartCruiseControlVision:
          # Transition to Turning if current lateral acceleration is over the threshold.
          if self.current_lat_acc >= _TURNING_LAT_ACC_TH:
            self.state = VisionState.turning
-          # Abort if the predicted lateral acceleration drops
-          elif self.max_pred_lat_acc < _ABORT_ENTERING_PRED_LAT_ACC_TH:
-            self.state = VisionState.enabled
+          # Begin releasing only after both current and predicted lateral acceleration stay clear.
+          elif self.relief_frames >= _RELIEF_CONFIRMATION_FRAMES:
+            self.state = VisionState.leaving

        # TURNING
        elif self.state == VisionState.turning:
-          # Transition to Leaving if current lateral acceleration drops below a threshold.
+          # Transition out of Turning if current lateral acceleration drops below a threshold.
          if self.current_lat_acc <= _LEAVING_LAT_ACC_TH:
-            self.state = VisionState.leaving
+            self.state = VisionState.entering if self.max_pred_lat_acc >= _ENTERING_PRED_LAT_ACC_TH else VisionState.leaving

        # LEAVING
        elif self.state == VisionState.leaving:
          # Transition back to Turning if current lateral acceleration goes back over the threshold.
          if self.current_lat_acc >= _TURNING_LAT_ACC_TH:
            self.state = VisionState.turning
-          # Finish if current lateral acceleration goes below a threshold.
-          elif self.current_lat_acc < _FINISH_LAT_ACC_TH:
+          # Start a new turn cycle immediately if another curve is predicted.
+          elif self.max_pred_lat_acc >= _ENTERING_PRED_LAT_ACC_TH:
+            self.state = VisionState.entering
+          # Finish after confirmed relief and a gradual release to the cruise setpoint.
+          elif self.relief_frames >= _RELIEF_CONFIRMATION_FRAMES and self.output_v_target >= self.v_cruise_setpoint:
            self.state = VisionState.enabled

    # DISABLED
@@ -157,32 +167,11 @@ class SmartCruiseControlVision:

    enabled = self.state in ENABLED_STATES
    active = self.state in ACTIVE_STATES
+    if not active:
+      self.relief_frames = 0

    return enabled, active

-  def _update_solution(self) -> float:
-    # DISABLED, ENABLED, OVERRIDING
-    if self.state not in ACTIVE_STATES:
-      # when not overshooting, calculate v_turn as the speed at the prediction horizon when following
-      # the smooth deceleration.
-      a_target = self.a_ego
-    # ENTERING
-    elif self.state == VisionState.entering:
-      # when not overshooting, target a smooth deceleration in preparation for a sharp turn to come.
-      a_target = np.interp(self.max_pred_lat_acc, _ENTERING_SMOOTH_DECEL_BP, _ENTERING_SMOOTH_DECEL_V)
-    # TURNING
-    elif self.state == VisionState.turning:
-      # When turning, we provide a target acceleration that is comfortable for the lateral acceleration felt.
-      a_target = np.interp(self.current_lat_acc, _TURNING_ACC_BP, _TURNING_ACC_V)
-    # LEAVING
-    elif self.state == VisionState.leaving:
-      # When leaving, we provide a comfortable acceleration to regain speed.
-      a_target = _LEAVING_ACC
-    else:
-      raise NotImplementedError(f"SCC-V state not supported: {self.state}")
-
-    return a_target
-
  def update(self, sm: messaging.SubMaster, long_enabled: bool, long_override: bool, v_ego: float, a_ego: float,
             v_cruise_setpoint: float) -> None:
    self.long_enabled = long_enabled
@@ -195,7 +184,7 @@ class SmartCruiseControlVision:
    self._update_calculations(sm)

    self.is_enabled, self.is_active = self._update_state_machine()
-    self.a_target = self._update_solution()
+    self.a_target = self.a_ego

    self.output_v_target = self.get_v_target_from_control()
    self.output_a_target = self.get_a_target_from_control()
@@ -91,7 +91,7 @@ class SpeedLimitAssist:

    self._plus_hold = 0.
    self._minus_hold = 0.
-    self._last_carstate_ts = 0.
+    self._release_toggle_prev = 0

    # TODO-SP: SLA's own output_a_target for planner
    # Solution functions mapped to respective states
@@ -146,16 +146,16 @@ class SpeedLimitAssist:
      set_speed_limit_assist_availability(self.CP, self.CP_SP, self.params)
      self.enabled = self.params.get("SpeedLimitMode", return_default=True) == Mode.assist

-  def update_car_state(self, CS: car.CarState) -> None:
+  def update_buttons(self, release_toggle: int) -> None:
+    released = self._release_toggle_prev ^ release_toggle
+    self._release_toggle_prev = release_toggle
+    if not released:
+      return
    now = time.monotonic()
-    self._last_carstate_ts = now
-
-    for b in CS.buttonEvents:
-      if not b.pressed:
-        if b.type in CRUISE_BUTTONS_PLUS:
-          self._plus_hold = max(self._plus_hold, now + CRUISE_BUTTON_CONFIRM_HOLD)
-        elif b.type in CRUISE_BUTTONS_MINUS:
-          self._minus_hold = max(self._minus_hold, now + CRUISE_BUTTON_CONFIRM_HOLD)
+    if any((released >> b) & 1 for b in CRUISE_BUTTONS_PLUS):
+      self._plus_hold = max(self._plus_hold, now + CRUISE_BUTTON_CONFIRM_HOLD)
+    if any((released >> b) & 1 for b in CRUISE_BUTTONS_MINUS):
+      self._minus_hold = max(self._minus_hold, now + CRUISE_BUTTON_CONFIRM_HOLD)

  def _get_button_release(self, req_plus: bool, req_minus: bool) -> bool:
    now = time.monotonic()
@@ -5,11 +5,14 @@ This file is part of sunnypilot and is licensed under the MIT License.
 See the LICENSE.md file in the root directory for more details.
 """

+import time
+
 import pytest

 from openpilot.cereal import custom
 from opendbc.car.car_helpers import interfaces
 from opendbc.car.rivian.values import CAR as RIVIAN
+from opendbc.car.structs import car
 from opendbc.car.tesla.values import CAR as TESLA
 from opendbc.car.toyota.values import CAR as TOYOTA
 from openpilot.common.constants import CV
@@ -21,9 +24,13 @@ from openpilot.sunnypilot.selfdrive.car import interfaces as sunnypilot_interfac
 from openpilot.sunnypilot.selfdrive.controls.lib.speed_limit import PCM_LONG_REQUIRED_MAX_SET_SPEED
 from openpilot.sunnypilot.selfdrive.controls.lib.speed_limit.common import Mode
 from openpilot.sunnypilot.selfdrive.controls.lib.speed_limit.speed_limit_assist import SpeedLimitAssist, \
-  PRE_ACTIVE_GUARD_PERIOD, ACTIVE_STATES
+  PRE_ACTIVE_GUARD_PERIOD, ACTIVE_STATES, CRUISE_BUTTON_CONFIRM_HOLD
+from openpilot.sunnypilot.selfdrive.selfdrived.button_state_tracker import ButtonStateTracker
 from openpilot.sunnypilot.selfdrive.selfdrived.events import EventsSP

+ButtonEvent = car.CarState.ButtonEvent
+ButtonType = car.CarState.ButtonEvent.Type
+
 SpeedLimitAssistState = custom.LongitudinalPlanSP.SpeedLimit.AssistState

 ALL_STATES = tuple(SpeedLimitAssistState.schema.enumerants.values())
@@ -276,3 +283,86 @@ class TestSpeedLimitAssist:
        assert self.sla.state in [SpeedLimitAssistState.preActive, SpeedLimitAssistState.active]
      elif initial_state in ACTIVE_STATES:
        assert self.sla.state in ACTIVE_STATES
+
+
+class TestButtonStateTrackerSLAIntegration:
+
+  def setup_method(self, method):
+    self.tracker = ButtonStateTracker()
+    self.params = Params()
+    self.params.put("IsReleaseSpBranch", True, block=True)
+    self.params.put("SpeedLimitMode", int(Mode.assist), block=True)
+    self.params.put_bool("IsMetric", False, block=True)
+    self.params.put("SpeedLimitOffsetType", 0, block=True)
+    self.params.put("SpeedLimitValueOffset", 0, block=True)
+
+    CarInterface = interfaces[DEFAULT_CAR]
+    CP = CarInterface.get_non_essential_params(DEFAULT_CAR)
+    CP.openpilotLongitudinalControl = True
+    CP_SP = CarInterface.get_non_essential_params_sp(CP, DEFAULT_CAR)
+    self.sla = SpeedLimitAssist(CP, CP_SP)
+
+  def _make_cs(self, events=None) -> car.CarState:
+    CS = car.CarState()
+    CS.buttonEvents = events or []
+    return CS
+
+  def _run_ctrl_frames(self, frames: list[car.CarState]) -> None:
+    for cs in frames:
+      self.tracker.update(cs)
+
+  def test_button_confirm_via_tracker(self) -> None:
+    self._run_ctrl_frames([
+      self._make_cs([ButtonEvent(type=ButtonType.accelCruise, pressed=True)]),
+      self._make_cs(),
+      self._make_cs([ButtonEvent(type=ButtonType.accelCruise, pressed=False)]),
+      self._make_cs(),
+      self._make_cs(),
+    ])
+    self.sla.update_buttons(self.tracker.release_toggle)
+    assert self.sla._get_button_release(req_plus=True, req_minus=False)
+
+  def test_rapid_press_release_between_polls(self) -> None:
+    self.sla.update_buttons(self.tracker.release_toggle)
+
+    self._run_ctrl_frames([
+      self._make_cs([ButtonEvent(type=ButtonType.decelCruise, pressed=True)]),
+      self._make_cs([ButtonEvent(type=ButtonType.decelCruise, pressed=False)]),
+      self._make_cs(),
+      self._make_cs(),
+      self._make_cs(),
+    ])
+    self.sla.update_buttons(self.tracker.release_toggle)
+    assert self.sla._get_button_release(req_plus=False, req_minus=True)
+
+  def test_multiple_releases_between_polls(self) -> None:
+    self.sla.update_buttons(self.tracker.release_toggle)
+
+    self._run_ctrl_frames([
+      self._make_cs([
+        ButtonEvent(type=ButtonType.accelCruise, pressed=True),
+        ButtonEvent(type=ButtonType.decelCruise, pressed=True),
+      ]),
+      self._make_cs([
+        ButtonEvent(type=ButtonType.accelCruise, pressed=False),
+        ButtonEvent(type=ButtonType.decelCruise, pressed=False),
+      ]),
+    ])
+    self.sla.update_buttons(self.tracker.release_toggle)
+    assert self.sla._get_button_release(req_plus=True, req_minus=False)
+    assert self.sla._get_button_release(req_plus=False, req_minus=True)
+
+  def test_no_false_positive_same_toggle(self) -> None:
+    self.sla.update_buttons(self.tracker.release_toggle)
+    self.sla.update_buttons(self.tracker.release_toggle)
+    assert not self.sla._get_button_release(req_plus=True, req_minus=False)
+    assert not self.sla._get_button_release(req_plus=False, req_minus=True)
+
+  def test_button_confirm_expires(self) -> None:
+    self._run_ctrl_frames([
+      self._make_cs([ButtonEvent(type=ButtonType.accelCruise, pressed=True)]),
+      self._make_cs([ButtonEvent(type=ButtonType.accelCruise, pressed=False)]),
+    ])
+    self.sla.update_buttons(self.tracker.release_toggle)
+    time.sleep(CRUISE_BUTTON_CONFIRM_HOLD + 0.1)
+    assert not self.sla._get_button_release(req_plus=True, req_minus=False)
@@ -1,10 +1,11 @@
 import pytest
 from openpilot.cereal import log, custom
 from openpilot.common.params import Params
-
 from openpilot.selfdrive.controls.lib.desire_helper import DesireHelper
 from openpilot.sunnypilot.selfdrive.controls.lib.lane_turn_desire import LaneTurnController, LANE_CHANGE_SPEED_MIN
 from openpilot.sunnypilot.selfdrive.controls.lib.auto_lane_change import AutoLaneChangeMode
+from openpilot.sunnypilot.selfdrive.controls.lib.relc import RoadEdgeLaneChangeController
+

 TurnDirection = custom.ModelDataV2SP.TurnDirection

@@ -107,7 +108,10 @@ def set_lane_turn_params():
 ])
 def test_desire_helper_integration(carstate, lateral_active, lane_change_prob, expected_desire, set_lane_turn_params):
  dh = DesireHelper()
+  relc = RoadEdgeLaneChangeController(dh)
+  relc.enabled = True
  dh.alc.lane_change_set_timer = AutoLaneChangeMode.NUDGE
  for _ in range(10):
-    dh.update(carstate, lateral_active, lane_change_prob)
+    dh.update(carstate, lateral_active, lane_change_prob,
+              left_edge_detected=relc.left_edge_detected, right_edge_detected=relc.right_edge_detected)
  assert dh.desire == expected_desire  # The first four tests were unit tests to test the controller, where this tests the integration in desire helpers
@@ -0,0 +1,73 @@
+import math
+
+import pytest
+
+from openpilot.sunnypilot.selfdrive.controls.lib.latcontrol_pid_ext import DecayingIntegratorPIDController
+
+RATE = 100
+DT = 1.0 / RATE
+
+
+def build_pid(decay_tau=2.0):
+  return DecayingIntegratorPIDController(0.05, 0.05, pos_limit=1.0, neg_limit=-1.0, rate=RATE, decay_tau=decay_tau)
+
+
+class TestDecayingIntegratorPIDController:
+  def test_accumulates_normally_when_not_frozen(self):
+    """Unfrozen behavior must be identical to stock PIDController - only freeze behavior changes."""
+    pid = build_pid()
+    for _ in range(50):
+      pid.update(error=1.0, speed=1.0, feedforward=0.0, freeze_integrator=False)
+    assert pid.i > 0
+
+  def test_decays_toward_zero_while_frozen(self):
+    pid = build_pid(decay_tau=2.0)
+    for _ in range(200):  # 2s build-up, well below saturation so anti-windup doesn't clip i
+      pid.update(error=1.0, speed=1.0, feedforward=0.0, freeze_integrator=False)
+    i_before = pid.i
+    assert i_before > 0
+
+    i_trace = []
+    for _ in range(600):  # 6s frozen = 3 time constants
+      pid.update(error=1.0, speed=1.0, feedforward=0.0, freeze_integrator=True)
+      i_trace.append(pid.i)
+
+    # monotonic decay toward zero, never grows, never flips sign
+    assert all(0 <= i_trace[k + 1] <= i_trace[k] for k in range(len(i_trace) - 1))
+    assert i_trace[-1] < 0.05 * i_before, "should be mostly decayed after 3 time constants"
+
+  def test_matches_exponential_decay_time_constant(self):
+    """Sanity-checks the decay is a real exp(-t/tau), not just 'decreasing'."""
+    pid = build_pid(decay_tau=2.0)
+    pid.i = 1.0
+
+    for _ in range(200):  # exactly one time constant (2s @ 100Hz)
+      pid.update(error=0.0, speed=1.0, feedforward=0.0, freeze_integrator=True)
+
+    assert pid.i == pytest.approx(math.exp(-1.0), rel=1e-3)
+
+  def test_no_discontinuity_at_freeze_transition(self):
+    """The whole point: control output must not jump the instant freeze conditions engage."""
+    pid = build_pid(decay_tau=2.0)
+    for _ in range(200):
+      pid.update(error=1.0, speed=1.0, feedforward=0.0, freeze_integrator=False)
+    control_before = pid.control
+
+    control_after = pid.update(error=1.0, speed=1.0, feedforward=0.0, freeze_integrator=True)
+
+    assert abs(control_after - control_before) < 0.01, "output jumped at the freeze transition"
+
+  def test_stale_integral_does_not_kick_back_in_on_unfreeze(self):
+    """The bug this exists to fix: after a long freeze, unfreezing must not suddenly reapply a
+    large stale integral untouched for however long the freeze lasted."""
+    pid = build_pid(decay_tau=2.0)
+    for _ in range(200):
+      pid.update(error=1.0, speed=1.0, feedforward=0.0, freeze_integrator=False)
+    i_peak = pid.i
+
+    for _ in range(1000):  # 10s frozen, ~5 time constants
+      pid.update(error=0.0, speed=1.0, feedforward=0.0, freeze_integrator=True)
+
+    # unfreeze: the resumed integral must be near zero, not the stale peak
+    pid.update(error=0.0, speed=1.0, feedforward=0.0, freeze_integrator=False)
+    assert abs(pid.i) < 0.01 * i_peak
@@ -0,0 +1,159 @@
+"""
+Closed-loop smoke test for the Prius TSS2 PID lateral-control toggle's starting gains
+(openpilot/sunnypilot/selfdrive/car/interfaces.py::_PRIUS_TSS2_PID_*).
+
+IMPORTANT LIMITATION: there is no real Prius TSS2 EPS actuator model anywhere in this repo (unlike
+the longitudinal plant model used by test_accel_controller_closed_loop.py, which was fit to logged
+routes). The actuator here is a generic, uncalibrated 2nd-order lag (see `SurrogateEpsActuator`) —
+it stands in for "some steering rack with plausible bandwidth," not this specific car's real EPS.
+
+This test can only prove the starting gains are stable and roughly critically damped against that
+generic surrogate. It CANNOT prove they are correctly tuned for a real Prius TSS2 — that requires
+on-road A/B via tools/lateral_maneuvers (see its README) before trusting this tune on its own.
+
+SCOPE: cruise-speed (20-30mph) only. The surrogate's steady-state gain is K = 1/(kf*v_ego**2) (see
+`SurrogateEpsActuator`), which blows up as v_ego -> 0 and produces meaningless multi-hundred-degree
+oscillation at parking-lot speed — an artifact of the surrogate, not of the kp/ki tune. This mirrors
+a real constraint: angle*v_ego**2 feedforward (and this kf calibration) is explicitly a higher-speed
+approximation (see the "25+mph" comment in latcontrol_torque_v0.py) — there's no valid basis here to
+simulate the low-speed "sharp turn" boost in _PRIUS_TSS2_PID_KP_BP/_V at all. That boost is only
+covered by the static shape check in test_prius_tss2_pid.py
+(test_kp_is_boosted_below_integrator_freeze_speed) — it has NOT been closed-loop or on-road
+verified. Validate it in a parking lot before trusting it anywhere faster.
+"""
+import math
+
+import numpy as np
+import pytest
+
+from opendbc.car import DT_CTRL
+from opendbc.car.car_helpers import interfaces as car_interfaces
+from opendbc.car.vehicle_model import VehicleModel
+from openpilot.selfdrive.controls.lib.latcontrol_pid import LatControlPID
+from openpilot.sunnypilot.selfdrive.car.interfaces import _initialize_prius_tss2_pid_lateral_control
+
+MPH_TO_MS = 0.44704
+DURATION_S = 6.0
+STEADY_WINDOW_S = 1.0
+TARGET_LAT_ACCEL = 1.5  # m/s^2, roughly a lateral_maneuvers "step" size
+
+
+class FakeCarState:
+  def __init__(self, v_ego):
+    self.vEgo = v_ego
+    self.steeringAngleDeg = 0.0
+    self.steeringRateDeg = 0.0
+    self.steeringPressed = False
+
+
+class FakeLiveParams:
+  roll = 0.0
+  angleOffsetDeg = 0.0
+
+
+class SurrogateEpsActuator:
+  """Generic critically-damped 2nd-order torque->angle lag. NOT fit to any real car.
+
+  The steady-state gain (deg per unit torque) is derived from the tune's own `kf`, i.e.
+  K = 1 / (kf * v_ego**2) — the same steady-state relationship LatControlPID's feedforward term
+  assumes (ff = kf * angle_deg * v_ego**2 ~= torque needed to hold that angle). A fixed, unrelated
+  gain guess saturated the actuator well below the test's target angle at 20-30mph — this ties the
+  surrogate to the one steady-state assumption already baked into the tune, so the test only
+  exercises kp/ki dynamic response and stability, not an arbitrary extra unknown.
+  """
+
+  def __init__(self, deg_per_unit_torque, natural_freq_hz=3.0, zeta=1.0):
+    self.wn = 2 * math.pi * natural_freq_hz
+    self.zeta = zeta
+    self.k = deg_per_unit_torque
+    self.angle = 0.0
+    self.rate = 0.0
+
+  def step(self, torque, dt):
+    accel = self.wn ** 2 * (self.k * torque - self.angle) - 2 * self.zeta * self.wn * self.rate
+    self.rate += accel * dt
+    self.angle += self.rate * dt
+    return self.angle, self.rate
+
+
+def run_closed_loop(CP, v_ego, target_lat_accel, duration_s=DURATION_S):
+  VM = VehicleModel(CP)
+  lac = LatControlPID(CP, structs_car_params_sp(), FakeCI(), DT_CTRL)
+  deg_per_unit_torque = 1.0 / (CP.lateralTuning.pid.kf * v_ego ** 2)
+  actuator = SurrogateEpsActuator(deg_per_unit_torque)
+  CS = FakeCarState(v_ego)
+  params = FakeLiveParams()
+
+  desired_curvature = -target_lat_accel / v_ego ** 2
+  desired_angle_deg = math.degrees(VM.get_steer_from_curvature(-desired_curvature, v_ego, 0.0))
+
+  n_steps = int(duration_s / DT_CTRL)
+  angle_trace = np.zeros(n_steps)
+  torque_trace = np.zeros(n_steps)
+
+  for i in range(n_steps):
+    output_torque, _, _ = lac.update(True, CS, VM, params, False, desired_curvature, None, False, 0.0)
+    output_torque = float(output_torque)
+    angle, rate = actuator.step(output_torque, DT_CTRL)
+    CS.steeringAngleDeg = float(angle)
+    CS.steeringRateDeg = float(rate)
+    angle_trace[i] = angle
+    torque_trace[i] = output_torque
+
+  return angle_trace, torque_trace, desired_angle_deg
+
+
+def structs_car_params_sp():
+  from opendbc.car import structs
+  return structs.CarParamsSP()
+
+
+class FakeCI:
+  @staticmethod
+  def get_steer_feedforward_function():
+    return lambda desired_angle, v_ego: desired_angle * (v_ego ** 2)
+
+
+def make_prius_tss2_cp():
+  CarInterface = car_interfaces['TOYOTA_PRIUS_TSS2']
+  CP = CarInterface.get_params('TOYOTA_PRIUS_TSS2', {0: {}, 1: {}, 2: {}}, [], alpha_long=False, is_release=False, docs=False)
+  _initialize_prius_tss2_pid_lateral_control(CP)
+  assert CP.lateralTuning.which() == 'pid'
+  return CP
+
+
+@pytest.mark.parametrize('v_mph', [20.0, 30.0])
+def test_starting_gains_settle_without_diverging(v_mph):
+  CP = make_prius_tss2_cp()
+  v_ego = v_mph * MPH_TO_MS
+
+  angle_trace, torque_trace, desired_angle_deg = run_closed_loop(CP, v_ego, TARGET_LAT_ACCEL)
+
+  assert np.all(np.isfinite(angle_trace)), "diverged/NaN — unsafe to ever test on-road"
+  assert np.all(np.abs(torque_trace) <= 1.0 + 1e-6), "output_torque exceeded steer_max=1.0 saturation bound"
+
+  steady_n = int(STEADY_WINDOW_S / DT_CTRL)
+  steady_angle = angle_trace[-steady_n:]
+  settle_error_deg = abs(np.mean(steady_angle) - desired_angle_deg)
+  oscillation_deg = np.ptp(steady_angle)
+
+  assert settle_error_deg < 1.0, f"steady-state tracking error too large: {settle_error_deg:.3f} deg (target {desired_angle_deg:.2f} deg)"
+  assert oscillation_deg < 0.5, f"sustained oscillation in tail window: {oscillation_deg:.3f} deg peak-to-peak (limit-cycle candidate)"
+
+
+def test_gains_are_not_a_no_op_sanity_check():
+  """Confirms this harness actually has teeth: gains far more aggressive than the shipped starting
+  point produce a limit cycle against the same surrogate actuator, so the tolerances above aren't
+  trivially satisfied by any input."""
+  CP = make_prius_tss2_cp()
+  CP.lateralTuning.pid.kpBP = [0.0]
+  CP.lateralTuning.pid.kpV = [1.5]   # 10x the cruise-speed kp
+  CP.lateralTuning.pid.kiBP = [0.0]
+  CP.lateralTuning.pid.kiV = [0.5]   # 10x the shipped ki
+  v_ego = 20.0 * MPH_TO_MS
+
+  angle_trace, _, desired_angle_deg = run_closed_loop(CP, v_ego, TARGET_LAT_ACCEL)
+
+  steady_n = int(STEADY_WINDOW_S / DT_CTRL)
+  oscillation_deg = np.ptp(angle_trace[-steady_n:])
+  assert oscillation_deg > 0.5, "expected an aggressive 10x-gain tune to visibly ring against this actuator; harness may not be sensitive"
@@ -0,0 +1,99 @@
+"""
+Copyright (c) 2021-, rav4kumar, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+import pytest
+
+from openpilot.common.realtime import DT_MDL
+from openpilot.selfdrive.controls.lib.desire_helper import DesireHelper
+from openpilot.sunnypilot.selfdrive.controls.lib.relc import (
+  RoadEdgeLaneChangeController, EDGE_REACTION_TIME, EDGE_CLEAR_TIME, MIN_SPEED,
+)
+
+V_HIGH = MIN_SPEED + 2.0
+V_LOW = MIN_SPEED - 1.0
+
+
+@pytest.fixture
+def relc(mocker):
+  mock_params = mocker.patch("openpilot.sunnypilot.selfdrive.controls.lib.relc.Params")
+  mock_params.return_value.get_bool.return_value = True
+  controller = RoadEdgeLaneChangeController(DesireHelper())
+  controller.enabled = True
+  return controller
+
+
+def drive(controller, road_edge_stds, lane_line_probs, seconds, v_ego=V_HIGH):
+  for _ in range(int(seconds / DT_MDL) + 1):
+    controller.update(road_edge_stds, lane_line_probs, v_ego)
+
+
+@pytest.mark.parametrize("road_edge_stds,lane_line_probs,attr", [
+  ([0.0, 0.9], [0.0, 0.8, 0.8, 0.8], "left_edge_detected"),
+  ([0.9, 0.0], [0.8, 0.8, 0.8, 0.0], "right_edge_detected"),
+])
+def test_edge_detection(relc, road_edge_stds, lane_line_probs, attr):
+  drive(relc, road_edge_stds, lane_line_probs, EDGE_REACTION_TIME + 0.1)
+  assert getattr(relc, attr)
+
+
+def test_edge_detection_requires_time(relc):
+  drive(relc, [0.0, 0.9], [0.0, 0.8, 0.8, 0.8], EDGE_REACTION_TIME - 0.05)
+  assert not relc.left_edge_detected
+
+
+def test_both_edges_detected(relc):
+  drive(relc, [0.0, 0.0], [0.0, 0.8, 0.8, 0.0], EDGE_REACTION_TIME + 0.1)
+  assert relc.left_edge_detected
+  assert relc.right_edge_detected
+
+
+def test_noise_doesnt_clear(relc):
+  edge = ([0.0, 0.9], [0.0, 0.8, 0.8, 0.8])
+  clear = ([0.9, 0.9], [0.8, 0.8, 0.8, 0.8])
+
+  drive(relc, *edge, EDGE_REACTION_TIME + 0.1)
+  assert relc.left_edge_detected
+
+  relc.update(*clear, V_HIGH)
+  relc.update(*edge, V_HIGH)
+  assert relc.left_edge_detected
+
+
+def test_clears_after_window(relc):
+  edge = ([0.0, 0.9], [0.0, 0.8, 0.8, 0.8])
+  clear = ([0.9, 0.9], [0.8, 0.8, 0.8, 0.8])
+
+  drive(relc, *edge, EDGE_REACTION_TIME + 0.1)
+  assert relc.left_edge_detected
+
+  drive(relc, *clear, EDGE_CLEAR_TIME + 0.05)
+  assert not relc.left_edge_detected
+  assert relc.left_edge_timer == 0.0
+
+
+def test_low_speed_skips(relc):
+  drive(relc, [0.0, 0.9], [0.0, 0.8, 0.8, 0.8], EDGE_REACTION_TIME + 0.1, v_ego=V_LOW)
+  assert not relc.left_edge_detected
+  assert relc.left_edge_timer == 0.0
+
+
+def test_speed_drop_resets(relc):
+  drive(relc, [0.0, 0.9], [0.0, 0.8, 0.8, 0.8], EDGE_REACTION_TIME + 0.1)
+  assert relc.left_edge_detected
+
+  relc.update([0.0, 0.9], [0.0, 0.8, 0.8, 0.8], V_LOW)
+  assert not relc.left_edge_detected
+
+
+def test_param_off_resets(relc):
+  drive(relc, [0.0, 0.9], [0.0, 0.8, 0.8, 0.8], EDGE_REACTION_TIME + 0.1)
+  assert relc.left_edge_detected
+
+  relc.params.get_bool.return_value = False
+  relc.read_params()
+  relc.update([0.0, 0.9], [0.0, 0.8, 0.8, 0.8], V_HIGH)
+  assert not relc.left_edge_detected
+  assert not relc.right_edge_detected
@@ -0,0 +1,109 @@
+from opendbc.car import structs
+
+from openpilot.sunnypilot.selfdrive.controls import controlsd_ext
+from openpilot.sunnypilot.selfdrive.controls.lib.latcontrol_pid_ext import LatControlPidSmooth
+
+
+class FakeParams:
+  def __init__(self, values=None):
+    self.values = values or {}
+
+  def get_bool(self, key):
+    return bool(self.values.get(key, False))
+
+  def get(self, key, return_default=False):
+    return self.values.get(key)
+
+
+class FakeCI:
+  def get_steer_feedforward_function(self):
+    return lambda desired_angle, v_ego: desired_angle * (v_ego ** 2)
+
+
+def make_ext(CP, params_values=None):
+  # Bypass __init__: it blocks on CarParamsSP over messaging, which isn't available in a unit test.
+  ext = controlsd_ext.ControlsExt.__new__(controlsd_ext.ControlsExt)
+  ext.CP = CP
+  ext.CP_SP = structs.CarParamsSP()
+  ext.params = FakeParams(params_values)
+  return ext
+
+
+def make_prius_tss2_pid_cp():
+  CP = structs.CarParams(carFingerprint='TOYOTA_PRIUS_TSS2')
+  CP.lateralTuning.init('pid')
+  CP.lateralTuning.pid.kpBP, CP.lateralTuning.pid.kpV = [0.0, 5.0], [0.30, 0.15]
+  CP.lateralTuning.pid.kiBP, CP.lateralTuning.pid.kiV = [0.0], [0.05]
+  CP.lateralTuning.pid.kf = 4e-05
+  return CP
+
+
+class TestInitializeLateralControlPidSmoothDispatch:
+  """The Prius TSS2 PID toggle's decaying-integrator variant must be scoped to exactly the one
+  (fingerprint, union) combination it applies to - never touch any other PID car's controller."""
+
+  def test_prius_tss2_pid_gets_smooth_variant(self):
+    ext = make_ext(make_prius_tss2_pid_cp())
+    lac = object()
+
+    result = ext.initialize_lateral_control(lac, FakeCI(), 0.01)
+
+    assert isinstance(result, LatControlPidSmooth)
+
+  def test_other_native_pid_car_is_untouched(self):
+    """A hypothetical other brand's native PID car must NOT get swapped to our variant just
+    because the union happens to be 'pid' - only our exact fingerprint qualifies."""
+    CP = structs.CarParams(carFingerprint='SOME_OTHER_PID_CAR')
+    CP.lateralTuning.init('pid')
+    ext = make_ext(CP)
+    lac = object()
+
+    result = ext.initialize_lateral_control(lac, FakeCI(), 0.01)
+
+    assert result is lac
+
+
+class TestInitializeLateralControlPidGuard:
+  """Regression test for the crash this toggle would otherwise cause: torque-only LatControl
+  variants read CP.lateralTuning.torque directly, which raises on a capnp union that's actually
+  'pid' (e.g. the Prius TSS2 PID toggle). initialize_lateral_control must never attempt that."""
+
+  def test_pid_union_returns_lac_unchanged_even_with_enforce_torque_on(self):
+    CP = structs.CarParams()
+    CP.lateralTuning.init('pid')
+    ext = make_ext(CP, {'EnforceTorqueControl': True, 'TorqueControlTune': 0.0})
+
+    lac = object()
+    result = ext.initialize_lateral_control(lac, CI=None, dt=0.01)
+
+    assert result is lac
+
+  def test_pid_union_returns_lac_unchanged_with_enforce_torque_off(self):
+    CP = structs.CarParams()
+    CP.lateralTuning.init('pid')
+    ext = make_ext(CP, {'EnforceTorqueControl': False})
+
+    lac = object()
+    result = ext.initialize_lateral_control(lac, CI=None, dt=0.01)
+
+    assert result is lac
+
+  def test_torque_union_still_dispatches_to_torque_v0(self, monkeypatch):
+    calls = []
+
+    class StubTorqueV0:
+      def __init__(self, CP, CP_SP, CI, dt):
+        calls.append((CP, CP_SP, CI, dt))
+
+    monkeypatch.setattr(controlsd_ext, 'LatControlTorqueV0', StubTorqueV0)
+
+    CP = structs.CarParams()
+    CP.lateralTuning.init('torque')
+    ext = make_ext(CP, {'EnforceTorqueControl': False})
+    ext.CP_SP = None
+
+    lac = object()
+    result = ext.initialize_lateral_control(lac, CI=None, dt=0.01)
+
+    assert isinstance(result, StubTorqueV0)
+    assert len(calls) == 1
@@ -0,0 +1,26 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+from opendbc.car import structs
+
+
+class ButtonStateTracker:
+  def __init__(self) -> None:
+    self.pressed: int = 0
+    self.release_toggle: int = 0
+
+  def update(self, CS: structs.CarState) -> None:
+    for b in CS.buttonEvents:
+      bit = 1 << b.type.raw
+      if b.pressed:
+        self.pressed |= bit
+      else:
+        self.pressed &= ~bit
+        self.release_toggle ^= bit
+
+  def publish(self, ss_sp) -> None:
+    ss_sp.buttonsPressed = self.pressed
+    ss_sp.buttonsReleaseToggle = self.release_toggle
@@ -244,4 +244,12 @@ EVENTS_SP: dict[int, dict[str, Alert | AlertCallbackType]] = {
      AlertStatus.normal, AlertSize.none,
      Priority.MID, VisualAlert.none, AudibleAlert.prompt, 3.),
  },
+
+  EventNameSP.laneChangeRoadEdge: {
+    ET.WARNING: Alert(
+      "Lane Change Unavailable: Road Edge",
+      "",
+      AlertStatus.userPrompt, AlertSize.small,
+      Priority.LOW, VisualAlert.none, AudibleAlert.prompt, 0.1),
+  },
 }
@@ -0,0 +1,67 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+from opendbc.car.structs import car
+from openpilot.sunnypilot.selfdrive.selfdrived.button_state_tracker import ButtonStateTracker
+
+ButtonEvent = car.CarState.ButtonEvent
+ButtonType = car.CarState.ButtonEvent.Type
+
+
+class TestButtonStateTracker:
+  def setup_method(self) -> None:
+    self.tracker = ButtonStateTracker()
+
+  def make_cs(self, events: list) -> car.CarState:
+    CS = car.CarState()
+    CS.buttonEvents = events
+    return CS
+
+  def test_initial_state(self) -> None:
+    assert self.tracker.pressed == 0
+    assert self.tracker.release_toggle == 0
+
+  def test_press_sets_bit(self) -> None:
+    self.tracker.update(self.make_cs([ButtonEvent(type=ButtonType.accelCruise, pressed=True)]))
+    assert self.tracker.pressed == (1 << ButtonType.accelCruise)
+    assert self.tracker.release_toggle == 0
+
+  def test_release_clears_and_toggles(self) -> None:
+    self.tracker.update(self.make_cs([ButtonEvent(type=ButtonType.accelCruise, pressed=True)]))
+    self.tracker.update(self.make_cs([ButtonEvent(type=ButtonType.accelCruise, pressed=False)]))
+    assert self.tracker.pressed == 0
+    assert self.tracker.release_toggle == (1 << ButtonType.accelCruise)
+
+  def test_multiple_buttons(self) -> None:
+    self.tracker.update(self.make_cs([
+      ButtonEvent(type=ButtonType.accelCruise, pressed=True),
+      ButtonEvent(type=ButtonType.decelCruise, pressed=True),
+    ]))
+    assert self.tracker.pressed == (1 << ButtonType.accelCruise) | (1 << ButtonType.decelCruise)
+
+    self.tracker.update(self.make_cs([ButtonEvent(type=ButtonType.accelCruise, pressed=False)]))
+    assert self.tracker.pressed == (1 << ButtonType.decelCruise)
+    assert self.tracker.release_toggle == (1 << ButtonType.accelCruise)
+
+  def test_release_toggle_flips(self) -> None:
+    for _ in range(2):
+      self.tracker.update(self.make_cs([ButtonEvent(type=ButtonType.gapAdjustCruise, pressed=True)]))
+      self.tracker.update(self.make_cs([ButtonEvent(type=ButtonType.gapAdjustCruise, pressed=False)]))
+    assert self.tracker.release_toggle == 0
+
+  def test_publish(self) -> None:
+    self.tracker.update(self.make_cs([ButtonEvent(type=ButtonType.accelCruise, pressed=True)]))
+    self.tracker.update(self.make_cs([ButtonEvent(type=ButtonType.decelCruise, pressed=True)]))
+    self.tracker.update(self.make_cs([ButtonEvent(type=ButtonType.accelCruise, pressed=False)]))
+
+    class MockSP:
+      buttonsPressed = 0
+      buttonsReleaseToggle = 0
+
+    sp = MockSP()
+    self.tracker.publish(sp)
+    assert sp.buttonsPressed == self.tracker.pressed
+    assert sp.buttonsReleaseToggle == self.tracker.release_toggle
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
rav4kumar	304f74deea	pid for pri	2026-08-06 12:51:14 -07:00
rav4kumar	56a3f5887d	long: preserve smooth stoppingdeacelrate.	2026-08-06 10:37:04 -07:00
rav4kumar	da7e79e10f	Merge remote-tracking branch 'origin/deep-rl-maybe' into tn	2026-08-05 10:52:53 -07:00
rav4kumar	f6db6c8b33	ref abh params	2026-08-05 10:51:46 -07:00
rav4kumar	87d8db4e44	fix(accel-controller): use current radar lead field	2026-08-05 10:49:01 -07:00
discountchubbs	0a1c4cdc14	bye	2026-08-04 21:28:11 -07:00
discountchubbs	872a76b6f2	shapey	2026-08-04 18:36:16 -07:00
discountchubbs	274a3a6f86	lint	2026-08-04 15:55:49 -07:00
discountchubbs	79a81ca2b8	allow legacy	2026-08-04 15:48:36 -07:00
rav4kumar	314bd78a30	ref	2026-08-04 14:45:10 -07:00
rav4kumar	53cba4d505	arizona mode	2026-08-04 14:45:10 -07:00
rav4kumar	5cd2634643	long: smooth SCC Vision curve pacing	2026-08-04 14:44:47 -07:00
rav4kumar	0ce47381e1	long: improve DEC radar and model braking	2026-08-04 14:44:47 -07:00
rav4kumar	b943211981	toyota: update blind spot monitoring	2026-08-04 14:44:47 -07:00
rav4kumar	427442ee5b	long: add acceleration personality controller	2026-08-04 14:44:47 -07:00
rav4kumar	b58354ee01	feat(dec): rework dynamic experimental controller	2026-08-04 14:09:21 -07:00
rav4kumar	278c3c5409	Add custom params to sunnylink settings	2026-08-04 14:08:14 -07:00
rav4kumar	ec8664fa93	fix mapd scorll	2026-08-04 14:07:11 -07:00
rav4kumar	354d8c2564	feat/relc	2026-08-04 14:07:10 -07:00
rav4kumar	81d7a763b8	mici-sla-ui	2026-08-04 14:06:50 -07:00
rav4kumar	54e79bda3f	point the submodule	2026-08-04 14:06:01 -07:00
rav4kumar	de5ed0d606	toyota sp link and drive mode btn support	2026-08-04 14:06:00 -07:00
rav4kumar	126db660c8	abh, bsm	2026-08-04 14:05:15 -07:00
discountchubbs	918a0962ea	desire me?	2026-08-04 13:34:35 -07:00
discountchubbs	5a2fcde03d	add todo-sp	2026-08-04 13:26:04 -07:00
discountchubbs	07bd1dfb52	prev_feat in cpu to prevent corruption	2026-08-04 13:15:12 -07:00
discountchubbs	6bfbf45c6d	dump and assign to cpu	2026-08-04 12:35:22 -07:00
discountchubbs	289171fef8	fix chunking	2026-08-02 15:38:02 -07:00
Jason Wen	5305655f78	Merge branch 'master' into deep-rl-maybe	2026-08-02 11:04:24 -04:00
Amy Jeanes	1a07e47228	Tesla: MADS Screen Activation (#1808 ) * Tesla: MADS Screen Button Settings Adds a Tesla vehicle setting (vehicle bus required) to control how many fingers activate the MADS screen button, or disable it entirely. Rebased onto current master: - Migrated the setting metadata from the deprecated params_metadata.json (removed in #1862) to the yaml SDUI system: added TeslaMadsScreenButton to settings_ui_src/pages/vehicle.yaml and recompiled settings_ui.json. Vehicle-bus gating uses the tesla_has_vehicle_bus capability visibility. - Bumped opendbc_repo to the latest head of sunnypilot/opendbc#459. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01JiSH2KAAueUmDe29xvpuAd * bump * Tesla: address MADS Screen Activation review feedback Default TeslaMadsScreenButton to Off for fresh installs and add a param migration that seeds existing Tesla installs with 3-Finger, preserving the previous always-on behaviour. Brand resolves from CarPlatformBundle, falling back to CarParamsPersistent so auto-fingerprinted Teslas are covered too. Rename the setting to "MADS Screen Activation", hyphenate the finger-count labels, and reword the description to use <br> (descriptions render as HTML) with a note that a higher finger count may reduce accidental activations. Applied both on-device and in sunnylink. Also fix test_tesla_with_vehicle_bus_uses_param, which broke once get_mads_limited_brands started reading TeslaMadsScreenButton from the same blanket params mock, and add coverage for the screen-button-Off path. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01X5icDp7zZ49gpyC2mpCfF1 * bump opendbc --------- Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> Co-authored-by: Jason Wen <haibin.wen3@gmail.com>	2026-08-01 23:40:52 -04:00
Jason Wen	50b860c928	Reapply "plannerd: check all services for validity (#38341 )" This reverts commit `0265ae5f76`.	2026-08-01 23:27:36 -04:00
Jason Wen	978ec800fe	plannerd & selfdriveStateSP: poll `modelV2`, relay button state via bitmask (#1893 ) * selfdrived: continuous button state and release counters in selfdriveStateSP * tests and more	2026-08-01 23:05:03 -04:00
discountchubbs	d733058dee	that comment was wrong, its still per model, just compiled at with the input/output shapes	2026-08-01 16:13:28 -07:00
discountchubbs	ce488dff50	fix pkl loader test	2026-08-01 16:09:04 -07:00
discountchubbs	2e87e08ba6	back	2026-08-01 15:17:26 -07:00
discountchubbs	785a6baa3d	Merge remote-tracking branch 'origin/deep-rl-maybe' into deep-rl-maybe	2026-08-01 15:17:05 -07:00
discountchubbs	c22f58dae3	lint be crazy now, man i've been away a while	2026-08-01 15:15:54 -07:00
James Vecellio-Grant	3c9dd2e590	Merge branch 'master' into deep-rl-maybe	2026-08-02 00:09:53 +02:00
discountchubbs	d086f83aed	no	2026-08-01 15:07:47 -07:00
discountchubbs	eef36d8b54	too much fluff	2026-08-01 15:05:31 -07:00
discountchubbs	62e0462662	this is annoying me	2026-08-01 14:58:01 -07:00
discountchubbs	ee9ee3a6fc	np.random.seed is deprecated, use Generator or default rng instead @nayan	2026-08-01 14:48:11 -07:00
discountchubbs	c4a3854dc5	fix macos cabana	2026-07-28 11:24:46 -07:00
nayan	ad516e619b	simplify everything.	2026-07-28 13:21:44 -04:00
nayan	8c3cd2575f	fuckit. dynamic everything.	2026-07-28 13:21:44 -04:00
nayan	3ec226474a	fix paths	2026-07-25 20:22:04 -04:00
nayan	273eb9f997	whatever	2026-07-25 18:30:29 -04:00
nayan	e143888eb1	realize. that i don't know shit	2026-07-25 18:08:17 -04:00
nayan	8092f13438	read/open - what's the difference	2026-07-25 17:43:59 -04:00
nayan	a87788b65c	Merge remote-tracking branch 'origin/deep-rl-maybe' into deep-rl-maybe	2026-07-25 17:29:35 -04:00
nayan	4cbe97fd20	hmmmm	2026-07-25 17:29:18 -04:00
Nayan	98254867a9	fuck. i AM blind. or dumb. or both.	2026-07-25 16:49:07 -04:00
Nayan	bee1cdd45d	i might be blind	2026-07-25 16:47:01 -04:00
nayan	91e40b80d8	wtf. ghostwriter	2026-07-25 16:34:01 -04:00
nayan	110568a9d1	it's a supercombo	2026-07-25 16:28:47 -04:00
nayan	bb1b9a27d8	Merge remote-tracking branch 'origin/master' into deep-rl # Conflicts: # openpilot/sunnypilot/modeld_v2/compile_modeld.py # openpilot/sunnypilot/modeld_v2/modeld.py # openpilot/sunnypilot/modeld_v2/tests/test_combined_pkl_loader.py # openpilot/sunnypilot/modeld_v2/tests/test_warp.py # openpilot/sunnypilot/modeld_v2/warp.py # openpilot/sunnypilot/models/manager.py # openpilot/sunnypilot/models/runners/helpers.py # openpilot/sunnypilot/models/runners/model_runner.py # openpilot/sunnypilot/models/runners/tinygrad/model_types.py # openpilot/sunnypilot/models/runners/tinygrad/tinygrad_runner.py # sunnypilot/models/helpers.py	2026-07-25 16:12:52 -04:00
nayan	76b21c72cf	i don't know what i'm doing	2026-07-25 16:08:15 -04:00
Jason Wen	3a05c03079	ci: no more docker (#1886 )	2026-07-25 15:34:36 -04:00
Eitan	fd22de1c9a	SCC-M: fix operator precedence in quadratic roots (#1816 ) * controls/scc: fix operator precedence in map controller quadratic roots * not async --------- Co-authored-by: Jason Wen <haibin.wen3@gmail.com>	2026-07-25 09:37:18 -04:00
Christopher Haucke	ee3583df33	[tizi/tici] ui: Camera offset controls (#1813 ) * Camera offset controls * final --------- Co-authored-by: Jason Wen <haibin.wen3@gmail.com>	2026-07-25 09:15:05 -04:00
discountchubbs	70424bd661	oopsie	2026-06-12 04:08:47 -07:00
discountchubbs	d215eab1d4	deeeeep	2026-06-12 03:49:03 -07:00
discountchubbs	91316c8cb5	simplify	2026-06-12 03:38:33 -07:00
discountchubbs	fa284be7e6	bye metadata	2026-06-12 03:12:13 -07:00
discountchubbs	6e7d9e5e52	done done done	2026-06-07 11:37:30 -07:00
James Vecellio-Grant	a4a7c2335d	Merge branch 'compile-modeld-defluff' into deep-rl	2026-06-07 19:54:47 +02:00
James Vecellio-Grant	ae573c7c3f	Update compile_modeld.py	2026-06-07 10:52:39 -07:00
discountchubbs	7d7b6ee306	i could	2026-06-07 10:32:55 -07:00
discountchubbs	6a4c59c3e0	needed	2026-06-07 10:22:07 -07:00
discountchubbs	fb5cb7a1cc	i could lie say	2026-06-07 10:06:02 -07:00
discountchubbs	049dfd2eaa	Update compile_modeld.py	2026-06-07 09:47:40 -07:00
discountchubbs	be20848487	Update compile_modeld.py	2026-06-07 04:17:14 -07:00
discountchubbs	cdd232b606	Merge branch 'deep-rl' of github.com:sunnypilot/sunnypilot into deep-rl	2026-06-07 04:06:16 -07:00
discountchubbs	b21c70b1ba	Update compile_modeld.py	2026-06-07 04:05:54 -07:00
James Vecellio-Grant	67e5bd3c1e	Merge branch 'compile-modeld-defluff' into deep-rl	2026-06-07 12:58:59 +02:00
discountchubbs	74692d0b5f	summary	2026-06-07 03:56:09 -07:00
discountchubbs	dd35c27981	Update compile_modeld.py	2026-06-07 03:44:27 -07:00
discountchubbs	159140e64e	Update compile_modeld.py	2026-06-07 03:41:09 -07:00
James Vecellio-Grant	f1ab6c8dfb	Update compile_modeld.py	2026-06-07 12:21:49 +02:00
James Vecellio-Grant	e1fe30fd3e	Update compile_modeld.py	2026-06-07 12:19:36 +02:00
James Vecellio-Grant	fba521dcff	Update fetcher.py	2026-06-07 12:06:02 +02:00
discountchubbs	a8ef55bfaa	gpu stuffs	2026-06-07 02:14:25 -07:00
discountchubbs	a232f54e2d	CREAM AND SUGAR	2026-06-07 01:45:42 -07:00
discountchubbs	2697008aa7	redundant	2026-06-06 10:09:07 -07:00
discountchubbs	ad5abd242a	modeld_v2: refactor compile_modeld	2026-06-06 09:58:48 -07:00
discountchubbs	6c1e0f370b	god use full attribute names please	2026-06-06 09:25:50 -07:00
discountchubbs	1083f5bf21	dumb	2026-06-06 09:16:36 -07:00
discountchubbs	dc5116c718	numpy	2026-06-06 09:15:51 -07:00
discountchubbs	8611e08dc6	fix string	2026-06-06 09:03:36 -07:00
discountchubbs	dc0f73c63b	modeld_v2: safe model validation	2026-06-06 08:54:36 -07:00