Condensing a little

remove this for now
Merge branch 'master-new' into master-new-model-selector-again
2026-07-25 07:32:06 +08:00 · 2025-01-08 10:59:56 +01:00 · 2025-01-07 16:18:17 -05:00 · 2025-01-07 10:37:43 -05:00 · 2025-01-07 10:37:36 -05:00 · 2025-01-07 09:45:51 +01:00
347 changed files with 2039 additions and 19554 deletions
@@ -67,7 +67,15 @@ jobs:
      timeout-minutes: 1
      run: |
        cd $STRIPPED_DIR
-        ${{ env.RUN }} "release/check-dirty.sh"
+        ${{ env.RUN }} "release/check-dirty.sh && \
+                        MAX_EXAMPLES=5 $PYTEST -m 'not slow' selfdrive/car system/manager"
+    - name: Static analysis
+      timeout-minutes: 1
+      run: |
+        cd $GITHUB_WORKSPACE
+        cp pyproject.toml $STRIPPED_DIR
+        cd $STRIPPED_DIR
+        ${{ env.RUN }} "scripts/lint/lint.sh --skip check_added_large_files"

  build:
    runs-on:
@@ -344,7 +352,7 @@ jobs:
      - name: Build openpilot
        run: ${{ env.RUN }} "scons -j$(nproc)"
      - name: Create Test Report
-        timeout-minutes: ${{ ((steps.frames-cache.outputs.cache-hit == 'true') && 2 || 4) }}
+        timeout-minutes: ${{ ((steps.frames-cache.outputs.cache-hit == 'true') && 1 || 3) }}
        run: >
            ${{ env.RUN }} "PYTHONWARNINGS=ignore &&
                            source selfdrive/test/setup_xvfb.sh &&
@@ -18,18 +18,6 @@ on:
        description: 'Custom name for the model'
        required: false
        type: string
-      file_name:
-        description: 'File name prefix for the model files'
-        required: false
-        type: string
-      is_20hz:
-        description: 'Is this a 20Hz model'
-        required: false
-        type: boolean
-        default: false
-
-
-run-name: Build model [${{ inputs.custom_name || inputs.upstream_branch }}] from ref [${{ inputs.upstream_branch }}]

 jobs:
  build_model:
@@ -49,15 +37,12 @@ jobs:
        with:
          path: ${{env.SCONS_CACHE_DIR}}
          key: scons-${{ runner.os }}-${{ runner.arch }}-${{ github.head_ref || github.ref_name }}-model-${{ github.sha }}
-          # Note: GitHub Actions enforces cache isolation between different build sources (PR builds, workflow dispatches, etc.) 
-          #   for security. Only caches from the default branch are shared across all builds. This is by design and cannot be overridden.
          restore-keys: |
+            scons-${{ runner.os }}-${{ runner.arch }}-${{ github.head_ref || github.ref_name }}-model-${{ github.sha }}
            scons-${{ runner.os }}-${{ runner.arch }}-${{ github.head_ref || github.ref_name }}-model
            scons-${{ runner.os }}-${{ runner.arch }}-${{ github.head_ref || github.ref_name }}
-            scons-${{ runner.os }}-${{ runner.arch }}-${{ env.MASTER_NEW_BRANCH }}-model
-            scons-${{ runner.os }}-${{ runner.arch }}-${{ env.MASTER_BRANCH }}-model
-            scons-${{ runner.os }}-${{ runner.arch }}-${{ env.MASTER_NEW_BRANCH }}
-            scons-${{ runner.os }}-${{ runner.arch }}-${{ env.MASTER_BRANCH }}
+            scons-${{ runner.os }}-${{ runner.arch }}-master-new
+            scons-${{ runner.os }}-${{ runner.arch }}-master
            scons-${{ runner.os }}-${{ runner.arch }}

      - name: Setup build environment
@@ -84,8 +69,6 @@ jobs:
        run: |
          sudo rm -rf ${OUTPUT_DIR}
          mkdir -p ${OUTPUT_DIR}
-          
-          # Copy the model files
          rsync -avm \
            --include='*.dlc' \
            --include='*.thneed' \
@@ -95,36 +78,6 @@ jobs:
            --delete-excluded \
            --chown=comma:comma \
            ./selfdrive/modeld/models/ ${OUTPUT_DIR}/
-          
-          # Rename files if file_name is provided
-          if [ ! -z "${{ inputs.file_name }}" ]; then
-            mv ${OUTPUT_DIR}/supercombo.thneed ${OUTPUT_DIR}/supercombo-${{ inputs.file_name }}.thneed
-            mv ${OUTPUT_DIR}/supercombo_metadata.pkl ${OUTPUT_DIR}/supercombo-${{ inputs.file_name }}_metadata.pkl
-          fi
-          
-          # Calculate SHA256 hashes
-          HASH=$(sha256sum ${OUTPUT_DIR}/supercombo*.thneed | cut -d' ' -f1)
-          METADATA_HASH=$(sha256sum ${OUTPUT_DIR}/supercombo*_metadata.pkl | cut -d' ' -f1)
-          
-          # Create metadata.json
-          cat > ${OUTPUT_DIR}/metadata.json << EOF
-          {
-            "display_name": "${{ inputs.custom_name || inputs.upstream_branch }}",
-            "full_name": "${{ inputs.file_name || 'default' }}",
-            "is_20hz": ${{ inputs.is_20hz || false }},
-            "files": {
-              "drive_model": {
-                "file_name": "$(basename ${OUTPUT_DIR}/supercombo*.thneed)",
-                "sha256": "${HASH}"
-              },
-              "metadata": {
-                "file_name": "$(basename ${OUTPUT_DIR}/supercombo*_metadata.pkl)",
-                "sha256": "${METADATA_HASH}"
-              }
-            },
-            "ref": "${{ inputs.upstream_branch }}",
-            "build_time": "$(date -u +"%Y-%m-%dT%H:%M:%SZ")"
-          }     

      - name: Upload Build Artifacts
        uses: actions/upload-artifact@v4
@@ -51,10 +51,9 @@ jobs:
        with:
          path: ${{env.SCONS_CACHE_DIR}}
          key: scons-${{ runner.os }}-${{ runner.arch }}-${{ github.head_ref || github.ref_name }}-${{ github.sha }}
-          # Note: GitHub Actions enforces cache isolation between different build sources (PR builds, workflow dispatches, etc.) 
-          #   for security. Only caches from the default branch are shared across all builds. This is by design and cannot be overridden.
          restore-keys: |
-            scons-${{ runner.os }}-${{ runner.arch }}-${{ github.head_ref || github.ref_name }}
+            scons-${{ runner.os }}-${{ runner.arch }}-${{ github.head_ref }}
+            scons-${{ runner.os }}-${{ runner.arch }}-${{ github.ref_name }}
            scons-${{ runner.os }}-${{ runner.arch }}-${{ env.MASTER_NEW_BRANCH }}
            scons-${{ runner.os }}-${{ runner.arch }}-${{ env.MASTER_BRANCH }}
            scons-${{ runner.os }}-${{ runner.arch }}
@@ -120,7 +119,6 @@ jobs:
          if [[ "${{ runner.debug }}" == "1" ]]; then
            printenv
          fi
-          PYTHONPATH=$PYTHONPATH:${{ github.workspace }}/ ${{ github.workspace }}/scripts/manage-powersave.py --disable

      - name: Build Panda
        run: |
@@ -189,11 +187,6 @@ jobs:
          name: prebuilt
          path: prebuilt.tar.gz

-      - name: Re-enable powersave
-        if: always()
-        run: |
-          PYTHONPATH=$PYTHONPATH:${{ github.workspace }}/ ${{ github.workspace }}/scripts/manage-powersave.py --enable
-
  publish:
    concurrency:
      group: publish-${{ github.head_ref || github.ref_name }}
@@ -86,7 +86,7 @@ jobs:
        run: >-
          sudo apt-get install -y imagemagick

-          scenes="homescreen settings_device settings_software settings_sunnylink settings_toggles settings_sunnypilot settings_sunnypilot_mads settings_trips settings_developer offroad_alert update_available prime onroad onroad_disengaged onroad_override onroad_sidebar onroad_wide onroad_wide_sidebar onroad_alert_small onroad_alert_mid onroad_alert_full driver_camera body keyboard keyboard_uppercase"
+          scenes="homescreen settings_device settings_software settings_sunnylink settings_toggles settings_sunnypilot settings_sunnypilot_mads settings_developer offroad_alert update_available prime onroad onroad_disengaged onroad_override onroad_sidebar onroad_wide onroad_wide_sidebar onroad_alert_small onroad_alert_mid onroad_alert_full driver_camera body keyboard"
          A=($scenes)

          DIFF=""
@@ -74,9 +74,6 @@ comma*.sh
 selfdrive/modeld/thneed/compile
 selfdrive/modeld/models/*.thneed
 selfdrive/modeld/models/*.pkl
-sunnypilot/modeld/thneed/compile
-sunnypilot/modeld/models/*.thneed
-sunnypilot/modeld/models/*.pkl

 *.bz2
 *.zst
@@ -15,4 +15,4 @@
  url = https://github.com/commaai/teleoprtc
 [submodule "tinygrad"]
  path = tinygrad_repo
-  url = https://github.com/commaai/tinygrad.git
+  url = https://github.com/tinygrad/tinygrad.git
@@ -1,23 +1,16 @@
-Version 0.9.9 (2025-03-30)
-========================
-* Coming soon
-  * Rivian support
-  * F-150 & Mach-E support
-  * Tesla Model 3 support
-
-Version 0.9.8 (2025-01-30)
+Version 0.9.8 (2024-XX-XX)
 ========================
+* New driving model
+  * Trained in brand new ML simulator
+  * Model now gates applying positive accel in Chill mode
 * New driving monitoring model
  * Reduced false positives related to passengers
 * Image processing pipeline moved to the ISP
  * More GPU time for driving models
-  * Power draw reduced 0.5W, which means your device runs cooler
+  * Power draw reduced 0.5W, which means your device runs cooler 
 * Added toggle to enable driver monitoring even when openpilot is not engaged
 * Enable openpilot longitudinal control for Ford Q3 vehicles
 * New Toyota TSS2 longitudinal tune
-* Coming soon
-  * New driving model with gas gating
-  * Training data upload mode

 Version 0.9.7 (2024-06-13)
 ========================
@@ -392,8 +392,6 @@ SConscript(['third_party/SConscript'])

 SConscript(['selfdrive/SConscript'])

-SConscript(['sunnypilot/SConscript'])
-
 if Dir('#tools/cabana/').exists() and GetOption('extras'):
  SConscript(['tools/replay/SConscript'])
  if arch != "larch64":
@@ -64,11 +64,6 @@ struct ModelManagerSP @0xaedffd8f31e7b55d {
    progress @1 :Float32;
    eta @2 :UInt32;
  }
-  
-  enum Runner {
-    snpe @0;
-    tinygrad @1;
-  }

  struct ModelBundle {
    index @0 :UInt32;
@@ -78,7 +73,7 @@ struct ModelManagerSP @0xaedffd8f31e7b55d {
    status @4 :DownloadStatus;
    generation @5 :UInt32;
    environment @6 :Text;
-    runner @7 :Runner;
+    is20hz @7 :Bool;
  }
 }

@@ -198,8 +198,6 @@ struct OnroadEvent @0xc4fa6047f024e718 {
    silentSeatbeltNotLatched @161;
    silentParkBrake @162;
    controlsMismatchLateral @163;
-    hyundaiRadarTracksConfirmed @164;
-    experimentalModeSwitched @165;

    soundsUnavailableDEPRECATED @47;
  }
@@ -484,7 +482,6 @@ struct GpsLocationData {
  speedAccuracy @12 :Float32;

  hasFix @13 :Bool;
-  satelliteCount @14 :Int8;

  enum SensorSource {
    android @0;
@@ -79,10 +79,6 @@ cl_context cl_create_context(cl_device_id device_id) {
  return CL_CHECK_ERR(clCreateContext(NULL, 1, &device_id, NULL, NULL, &err));
 }

-void cl_release_context(cl_context context) {
-  clReleaseContext(context);
-}
-
 cl_program cl_program_from_file(cl_context ctx, cl_device_id device_id, const char* path, const char* args) {
  return cl_program_from_source(ctx, device_id, util::read_file(path), args);
 }
@@ -23,7 +23,6 @@

 cl_device_id cl_get_device_id(cl_device_type device_type);
 cl_context cl_create_context(cl_device_id device_id);
-void cl_release_context(cl_context context);
 cl_program cl_program_from_source(cl_context ctx, cl_device_id device_id, const std::string& src, const char* args = nullptr);
 cl_program cl_program_from_binary(cl_context ctx, cl_device_id device_id, const uint8_t* binary, size_t length, const char* args = nullptr);
 cl_program cl_program_from_file(cl_context ctx, cl_device_id device_id, const char* path, const char* args);
@@ -201,9 +201,7 @@ std::unordered_map<std::string, uint32_t> keys = {
    {"Version", PERSISTENT},

    // --- sunnypilot params --- //
-    {"ApiCache_DriveStats", PERSISTENT},
    {"EnableGithubRunner", PERSISTENT | BACKUP},
-    {"ModelRunnerTypeCache", CLEAR_ON_ONROAD_TRANSITION},

    // MADS params
    {"Mads", PERSISTENT | BACKUP},
@@ -223,12 +221,6 @@ std::unordered_map<std::string, uint32_t> keys = {
    {"SunnylinkDongleId", PERSISTENT},
    {"SunnylinkdPid", PERSISTENT},
    {"SunnylinkEnabled", PERSISTENT},
-
-    // sunnypilot car specific params
-    {"HyundaiRadarTracks", PERSISTENT},
-    {"HyundaiRadarTracksConfirmed", PERSISTENT},
-    {"HyundaiRadarTracksPersistent", PERSISTENT},
-    {"HyundaiRadarTracksToggle", PERSISTENT},
 };

 } // namespace
@@ -1,6 +1,9 @@
 import numpy as np
 from numbers import Number

+from openpilot.common.numpy_fast import clip, interp
+
+
 class PIDController:
  def __init__(self, k_p, k_i, k_f=0., k_d=0., pos_limit=1e308, neg_limit=-1e308, rate=100):
    self._k_p = k_p
@@ -25,15 +28,15 @@ class PIDController:

  @property
  def k_p(self):
-    return np.interp(self.speed, self._k_p[0], self._k_p[1])
+    return interp(self.speed, self._k_p[0], self._k_p[1])

  @property
  def k_i(self):
-    return np.interp(self.speed, self._k_i[0], self._k_i[1])
+    return interp(self.speed, self._k_i[0], self._k_i[1])

  @property
  def k_d(self):
-    return np.interp(self.speed, self._k_d[0], self._k_d[1])
+    return interp(self.speed, self._k_d[0], self._k_d[1])

  @property
  def error_integral(self):
@@ -61,10 +64,10 @@ class PIDController:

        # Clip i to prevent exceeding control limits
        control_no_i = self.p + self.d + self.f
-        control_no_i = np.clip(control_no_i, self.neg_limit, self.pos_limit)
-        self.i = np.clip(self.i, self.neg_limit - control_no_i, self.pos_limit - control_no_i)
+        control_no_i = clip(control_no_i, self.neg_limit, self.pos_limit)
+        self.i = clip(self.i, self.neg_limit - control_no_i, self.pos_limit - control_no_i)

    control = self.p + self.i + self.d + self.f

-    self.control = np.clip(control, self.neg_limit, self.pos_limit)
+    self.control = clip(control, self.neg_limit, self.pos_limit)
    return self.control
@@ -26,9 +26,6 @@ public:
    zmq_setsockopt(sock, ZMQ_LINGER, &timeout, sizeof(timeout));
    zmq_connect(sock, Path::swaglog_ipc().c_str());

-    // workaround for https://github.com/dropbox/json11/issues/38
-    setlocale(LC_NUMERIC, "C");
-
    print_level = CLOUDLOG_WARNING;
    if (const char* print_lvl = getenv("LOGPRINT")) {
      if (strcmp(print_lvl, "debug") == 0) {
@@ -0,0 +1,21 @@
+import numpy as np
+
+from openpilot.common.numpy_fast import interp
+
+
+class TestInterp:
+  def test_correctness_controls(self):
+    _A_CRUISE_MIN_BP = np.asarray([0., 5., 10., 20., 40.])
+    _A_CRUISE_MIN_V = np.asarray([-1.0, -.8, -.67, -.5, -.30])
+    v_ego_arr = [-1, -1e-12, 0, 4, 5, 6, 7, 10, 11, 15.2, 20, 21, 39,
+                 39.999999, 40, 41]
+
+    expected = np.interp(v_ego_arr, _A_CRUISE_MIN_BP, _A_CRUISE_MIN_V)
+    actual = interp(v_ego_arr, _A_CRUISE_MIN_BP, _A_CRUISE_MIN_V)
+
+    np.testing.assert_equal(actual, expected)
+
+    for v_ego in v_ego_arr:
+      expected = np.interp(v_ego, _A_CRUISE_MIN_BP, _A_CRUISE_MIN_V)
+      actual = interp(v_ego, _A_CRUISE_MIN_BP, _A_CRUISE_MIN_V)
+      np.testing.assert_equal(actual, expected)
@@ -88,7 +88,7 @@ A supported vehicle is one that just works when you install a comma device. All
 |Hyundai|Elantra 2017-18|Smart Cruise Control (SCC)|Stock|19 mph|32 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-empty.svg)](##)|<details><summary>Parts</summary><sub>- 1 Hyundai B connector<br>- 1 RJ45 cable (7 ft)<br>- 1 comma 3X<br>- 1 comma power v2<br>- 1 harness box<br>- 1 mount<br>- 1 right angle OBD-C cable (1.5 ft)<br><a href="https://comma.ai/shop/comma-3x.html?make=Hyundai&model=Elantra 2017-18">Buy Here</a></sub></details>||
 |Hyundai|Elantra 2019|Smart Cruise Control (SCC)|Stock|19 mph|32 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-empty.svg)](##)|<details><summary>Parts</summary><sub>- 1 Hyundai G connector<br>- 1 RJ45 cable (7 ft)<br>- 1 comma 3X<br>- 1 comma power v2<br>- 1 harness box<br>- 1 mount<br>- 1 right angle OBD-C cable (1.5 ft)<br><a href="https://comma.ai/shop/comma-3x.html?make=Hyundai&model=Elantra 2019">Buy Here</a></sub></details>||
 |Hyundai|Elantra 2021-23|Smart Cruise Control (SCC)|openpilot available[<sup>1</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 Hyundai K connector<br>- 1 RJ45 cable (7 ft)<br>- 1 comma 3X<br>- 1 comma power v2<br>- 1 harness box<br>- 1 mount<br>- 1 right angle OBD-C cable (1.5 ft)<br><a href="https://comma.ai/shop/comma-3x.html?make=Hyundai&model=Elantra 2021-23">Buy Here</a></sub></details>|<a href="https://youtu.be/_EdYQtV52-c" target="_blank"><img height="18px" src="assets/icon-youtube.svg"></img></a>|
-|Hyundai|Elantra GT 2017-20|Smart Cruise Control (SCC)|Stock|0 mph|32 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 Hyundai E connector<br>- 1 RJ45 cable (7 ft)<br>- 1 comma 3X<br>- 1 comma power v2<br>- 1 harness box<br>- 1 mount<br>- 1 right angle OBD-C cable (1.5 ft)<br><a href="https://comma.ai/shop/comma-3x.html?make=Hyundai&model=Elantra GT 2017-20">Buy Here</a></sub></details>||
+|Hyundai|Elantra GT 2017-19|Smart Cruise Control (SCC)|Stock|0 mph|32 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 Hyundai E connector<br>- 1 RJ45 cable (7 ft)<br>- 1 comma 3X<br>- 1 comma power v2<br>- 1 harness box<br>- 1 mount<br>- 1 right angle OBD-C cable (1.5 ft)<br><a href="https://comma.ai/shop/comma-3x.html?make=Hyundai&model=Elantra GT 2017-19">Buy Here</a></sub></details>||
 |Hyundai|Elantra Hybrid 2021-23|Smart Cruise Control (SCC)|openpilot available[<sup>1</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 Hyundai K connector<br>- 1 RJ45 cable (7 ft)<br>- 1 comma 3X<br>- 1 comma power v2<br>- 1 harness box<br>- 1 mount<br>- 1 right angle OBD-C cable (1.5 ft)<br><a href="https://comma.ai/shop/comma-3x.html?make=Hyundai&model=Elantra Hybrid 2021-23">Buy Here</a></sub></details>|<a href="https://youtu.be/_EdYQtV52-c" target="_blank"><img height="18px" src="assets/icon-youtube.svg"></img></a>|
 |Hyundai|Genesis 2015-16|Smart Cruise Control (SCC)|Stock|19 mph|37 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-empty.svg)](##)|<details><summary>Parts</summary><sub>- 1 Hyundai J connector<br>- 1 RJ45 cable (7 ft)<br>- 1 comma 3X<br>- 1 comma power v2<br>- 1 harness box<br>- 1 mount<br>- 1 right angle OBD-C cable (1.5 ft)<br><a href="https://comma.ai/shop/comma-3x.html?make=Hyundai&model=Genesis 2015-16">Buy Here</a></sub></details>||
 |Hyundai|i30 2017-19|Smart Cruise Control (SCC)|Stock|0 mph|32 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 Hyundai E connector<br>- 1 RJ45 cable (7 ft)<br>- 1 comma 3X<br>- 1 comma power v2<br>- 1 harness box<br>- 1 mount<br>- 1 right angle OBD-C cable (1.5 ft)<br><a href="https://comma.ai/shop/comma-3x.html?make=Hyundai&model=i30 2017-19">Buy Here</a></sub></details>||
@@ -4,7 +4,6 @@ set -e
 # Default values
 DEFAULT_REPO_URL="https://github.com/sunnypilot"
 START_AT_BOOT=false
-RESTORE_MODE=false

 # Parse command line arguments
 while [[ $# -gt 0 ]]; do
@@ -21,10 +20,6 @@ while [[ $# -gt 0 ]]; do
            REPO_URL="$2"
            shift 2
            ;;
-        --restore)
-            RESTORE_MODE=true
-            shift
-            ;;
        *)
            if [ -z "$GITHUB_TOKEN" ]; then
                GITHUB_TOKEN="$1"
@@ -36,6 +31,19 @@ while [[ $# -gt 0 ]]; do
    esac
 done

+# Check required arguments
+if [ -z "$GITHUB_TOKEN" ]; then
+    echo "Usage: $0 [--start-at-boot] [--token <github_token>] [--repo <repository_url>]"
+    echo "Required argument: github_token"
+    echo "Optional arguments:"
+    echo "  --start-at-boot    Enable auto-start at boot (default: false)"
+    echo "  --repo            Repository URL (default: ${DEFAULT_REPO_URL})"
+    exit 1
+fi
+
+# Set repository URL if not provided
+REPO_URL="${REPO_URL:-$DEFAULT_REPO_URL}"
+
 # Determine BASE_DIR based on mount point
 if mountpoint -q /data/media; then
    BASE_DIR="/data/media/0/github"
@@ -52,19 +60,20 @@ LOGS_DIR="${BASE_DIR}/logs"
 CACHE_DIR="${BASE_DIR}/cache"
 OPENPILOT_DIR="${BASE_DIR}/openpilot"

-# Basic utility functions (no dependencies)
-remount_rw() {
-    sudo mount -o remount,rw /
+create_directories() {
+    sudo mkdir -p "$RUNNER_DIR" "$BUILDS_DIR" "$LOGS_DIR" "$CACHE_DIR" "$OPENPILOT_DIR"
+    mkdir -p "/data/openpilot"
+    sudo chown -R comma:comma "/data/openpilot"
 }

-remount_ro() {
-    sync || true  # Try to sync but continue even if it fails
-    sudo mount -o remount,ro /  # Always try to remount as read-only
+download_and_setup_runner() {
+    cd "$RUNNER_DIR"
+    curl -o actions-runner-linux-arm64-2.321.0.tar.gz -L https://github.com/actions/runner/releases/download/v2.321.0/actions-runner-linux-arm64-2.321.0.tar.gz
+    tar xzf ./actions-runner-linux-arm64-2.321.0.tar.gz
+    rm ./actions-runner-linux-arm64-2.321.0.tar.gz
+    chmod +x ./config.sh
 }

-# Always ensure we try to remount as read-only on exit
-trap remount_ro EXIT
-
 setup_runner_user() {
    sudo useradd --comment 'GitHub Runner' --create-home --home-dir ${BASE_DIR} ${RUNNER_USER} --shell /bin/bash -G ${USER_GROUPS} || sudo usermod -aG ${USER_GROUPS} ${RUNNER_USER}
    export BASE_DIR
@@ -75,45 +84,18 @@ create_sudoers_entry() {
    sudo grep -qxF "${RUNNER_USER} ALL=(ALL) NOPASSWD: ALL" /etc/sudoers || echo "${RUNNER_USER} ALL=(ALL) NOPASSWD: ALL" | sudo tee -a /etc/sudoers
 }

+configure_runner() {
+    cd "$RUNNER_DIR"
+    sudo -u ${RUNNER_USER} ./config.sh --url "$REPO_URL" --token "$GITHUB_TOKEN" --name $(hostname) --runnergroup "tici-tizi" --labels "tici" --work "$BUILDS_DIR" --unattended
+}
+
 set_directory_permissions() {
    sudo chown -R ${RUNNER_USER}:comma "$BASE_DIR"
    sudo chmod g+rwx "$BASE_DIR"
    sudo chmod g+s "$BASE_DIR"
 }

-setup_directories() {
-    echo "Creating necessary directories..."
-    sudo mkdir -p "$RUNNER_DIR" "$BUILDS_DIR" "$LOGS_DIR" "$CACHE_DIR" "$OPENPILOT_DIR"
-    mkdir -p "/data/openpilot"
-    sudo chown -R comma:comma "/data/openpilot"
-}
-
-# System configuration functions (depends on basic utility functions)
-setup_system_configs() {
-    echo "Setting up system configurations..."
-    setup_runner_user
-    create_sudoers_entry
-    set_directory_permissions
-}
-
-# Runner setup functions
-install_runner() {
-    echo "Downloading and setting up runner..."
-    cd "$RUNNER_DIR"
-    curl -o actions-runner-linux-arm64-2.321.0.tar.gz -L https://github.com/actions/runner/releases/download/v2.321.0/actions-runner-linux-arm64-2.321.0.tar.gz
-    tar xzf ./actions-runner-linux-arm64-2.321.0.tar.gz
-    rm ./actions-runner-linux-arm64-2.321.0.tar.gz
-    chmod +x ./config.sh
-}
-
-configure_runner() {
-    echo "Configuring runner..."
-    cd "$RUNNER_DIR"
-    sudo -u ${RUNNER_USER} ./config.sh --url "$REPO_URL" --token "$GITHUB_TOKEN" --name $(hostname) --runnergroup "tici-tizi" --labels "tici" --work "$BUILDS_DIR" --unattended
-}
-
-create_service_template() {
-    echo "Creating service template..."
+modify_service_template() {
    cat <<EOL > "$RUNNER_DIR/bin/actions.runner.service.template"
 [Unit]
 Description={{Description}}
@@ -138,116 +120,28 @@ WantedBy=multi-user.target
 EOL
 }

-install_service() {
-    echo "Installing systemd service..."
-    cd "$RUNNER_DIR"
-    sudo ./svc.sh install $RUNNER_USER
+# Make filesystem writable
+sudo mount -o remount,rw /

-    if [ "$START_AT_BOOT" = false ]; then
-        local service_name
-        if [ -f "${RUNNER_DIR}/.service" ]; then
-            service_name=$(cat "${RUNNER_DIR}/.service")
-        else
-            service_name="actions.runner.sunnypilot.$(uname -n)"
-        fi
-        sudo systemctl disable "${service_name}"
-    fi
-}
+# Ensure filesystem is remounted as read-only on script exit
+trap "sudo mount -o remount,ro /" EXIT

-check_restore_prerequisites() {
-    local needs_restore=false
-    local can_restore=false
-    local service_name=""
+# Execute installation steps
+setup_runner_user
+create_sudoers_entry
+create_directories
+download_and_setup_runner
+modify_service_template
+configure_runner
+set_directory_permissions

-    # Check if base runner directory exists
-    if [ ! -d "${RUNNER_DIR}" ]; then
-        echo "ERROR: Runner directory ${RUNNER_DIR} does not exist"
-        echo "This directory is required for restore operations"
-        exit 1
-    fi
+# Install and start service using built-in installer
+cd "$RUNNER_DIR"
+sudo ./svc.sh install $RUNNER_USER

-    # First check if we have the required files for restoration
-    if [ -f "${RUNNER_DIR}/.credentials" ] && [ -f "${RUNNER_DIR}/.service" ]; then
-        can_restore=true
-        service_name=$(cat "${RUNNER_DIR}/.service")
-        echo "Found required runner configuration files"
-    else
-        echo "Missing required runner configuration files"
-        echo "Required: .credentials and .service files in ${RUNNER_DIR}"
-        exit 1
-    fi
+# Handle auto-start configuration
+if [ "$START_AT_BOOT" = false ]; then
+    sudo systemctl disable actions.runner.sunnypilot.$(uname -n)
+fi

-    # Then check if restoration is needed (if either service or user is missing)
-    if ! systemctl list-unit-files "${service_name}" &>/dev/null; then
-        echo "Service ${service_name} not found in systemd"
-        needs_restore=true
-    fi
-
-    if ! id "${RUNNER_USER}" &>/dev/null; then
-        echo "User ${RUNNER_USER} does not exist"
-        needs_restore=true
-    fi
-
-    # Only proceed if we can restore AND need to restore
-    if [ "$can_restore" = true ] && [ "$needs_restore" = true ]; then
-        echo "Restoration is needed and possible"
-        return 0
-    else
-        if [ "$needs_restore" = false ]; then
-            echo "System is already properly configured (user and service exist)"
-        fi
-        exit 0
-    fi
-}
-
-perform_restore() {
-    echo "Starting runner restoration..."
-    setup_directories
-    remount_rw
-    setup_system_configs
-    install_service
-    remount_ro
-    echo "Runner restoration completed successfully"
-}
-
-perform_install() {
-    echo "Starting fresh installation..."
-    setup_directories
-    install_runner
-    create_service_template
-    remount_rw
-    setup_system_configs
-    configure_runner
-    install_service
-    remount_ro
-    echo "Installation completed successfully"
-}
-
-main() {
-    if [ "$RESTORE_MODE" = true ]; then
-        echo "Running in restore mode - will only restore system configurations..."
-        check_restore_prerequisites
-        perform_restore
-    else
-        # Check required arguments for normal installation
-        if [ -z "$GITHUB_TOKEN" ]; then
-            echo "Usage: $0 [--start-at-boot] [--token <github_token>] [--repo <repository_url>] [--restore]"
-            echo "Required argument (except for --restore): github_token"
-            echo "Optional arguments:"
-            echo "  --start-at-boot    Enable auto-start at boot (default: false)"
-            echo "  --repo            Repository URL (default: ${DEFAULT_REPO_URL})"
-            echo "  --restore         Restore existing runner configuration"
-            exit 1
-        fi
-
-        # Set repository URL if not provided
-        REPO_URL="${REPO_URL:-$DEFAULT_REPO_URL}"
-        perform_install
-    fi
-
-    echo "Starting runner service..."
-    cd "$RUNNER_DIR"
-    sudo ./svc.sh start
-}
-
-main
+sudo ./svc.sh start
@@ -6,11 +6,41 @@ from pathlib import Path
 HERE = os.path.abspath(os.path.dirname(__file__))
 ROOT = HERE + "/.."

+# blacklisting is for two purposes:
+# - minimizing release download size
+# - keeping the diff readable
 blacklist = [
-  ".git/",
+  "panda/drivers/",
+  "panda/examples/",
+  "panda/tests/safety/",
+
+  "opendbc_repo/dbc/.*.dbc$",
+  "opendbc_repo/dbc/generator/",
+
+  "cereal/.*test.*",
+  "^common/tests/",
+
+  # particularly large text files
+  "uv.lock",
+  "third_party/catch2",
+  "selfdrive/car/tests/test_models.*",
+
+  "^tools/",
+  "^tinygrad_repo/",

  "matlab.*.md",

+  ".git/",
+  ".github/",
+  ".devcontainer/",
+  "Darwin/",
+  ".vscode",
+
+  # common things
+  "LICENSE",
+  "Dockerfile",
+  ".pre-commit",
+
  # no LFS or submodules in release
  ".lfsconfig",
  ".gitattributes",
@@ -18,10 +48,153 @@ blacklist = [
  ".gitmodules",
 ]

-# gets you through the blacklist
-whitelist: list[str] = [
+# Sunnypilot blacklist
+sunnypilot_blacklist = [
+  "system/loggerd/sunnylink_uploader.py",  # Temporarily, until we are ready to roll it out widely
+  ".idea/",
+  ".run/",
+  ".*__pycache__/.*",
+  ".*\\.pyc",
+  "teleoprtc/*",
+  "third_party/snpe/x86_64/*",
+  "body/board/canloader.py",
+  "body/board/flash_base.sh",
+  "body/board/flash_knee.sh",
+  "body/board/recover.sh",
+  ".*/test/",
+  ".*/tests/",
+  ".*tinygrad_repo/tinygrad/renderer/",
+  "README.md",
+  ".*internal/",
+  "docs/.*",
+  ".sconsign.dblite",
+  "release/ci/scons_cache/",
+  ".gitlab-ci.yml",
+  ".clang-tidy",
+  ".dockerignore",
+  ".editorconfig",
+  ".python-version",
+  "SECURITY.md",
+  "codecov.yml",
+  "conftest.py",
+  "poetry.lock",
+  ".venv/",
 ]

+# Merge the blacklists
+blacklist += sunnypilot_blacklist
+
+# gets you through the blacklist
+whitelist = [
+  "tools/lib/",
+  "tools/bodyteleop/",
+  "tools/joystick/",
+  "tools/longitudinal_maneuvers/",
+
+  "tinygrad_repo/examples/openpilot/compile3.py",
+  "tinygrad_repo/extra/onnx.py",
+  "tinygrad_repo/extra/onnx_ops.py",
+  "tinygrad_repo/extra/thneed.py",
+  "tinygrad_repo/extra/utils.py",
+  "tinygrad_repo/tinygrad/codegen/kernel.py",
+  "tinygrad_repo/tinygrad/codegen/linearizer.py",
+  "tinygrad_repo/tinygrad/features/image.py",
+  "tinygrad_repo/tinygrad/features/search.py",
+  "tinygrad_repo/tinygrad/nn/*",
+  "tinygrad_repo/tinygrad/renderer/cstyle.py",
+  "tinygrad_repo/tinygrad/renderer/opencl.py",
+  "tinygrad_repo/tinygrad/runtime/lib.py",
+  "tinygrad_repo/tinygrad/runtime/ops_cpu.py",
+  "tinygrad_repo/tinygrad/runtime/ops_disk.py",
+  "tinygrad_repo/tinygrad/runtime/ops_gpu.py",
+  "tinygrad_repo/tinygrad/shape/*",
+  "tinygrad_repo/tinygrad/.*.py",
+
+  # TODO: do this automatically
+  "opendbc_repo/dbc/comma_body.dbc",
+  "opendbc_repo/dbc/chrysler_ram_hd_generated.dbc",
+  "opendbc_repo/dbc/chrysler_ram_dt_generated.dbc",
+  "opendbc_repo/dbc/chrysler_pacifica_2017_hybrid_generated.dbc",
+  "opendbc_repo/dbc/chrysler_pacifica_2017_hybrid_private_fusion.dbc",
+  "opendbc_repo/dbc/gm_global_a_powertrain_generated.dbc",
+  "opendbc_repo/dbc/gm_global_a_object.dbc",
+  "opendbc_repo/dbc/gm_global_a_chassis.dbc",
+  "opendbc_repo/dbc/FORD_CADS.dbc",
+  "opendbc_repo/dbc/ford_fusion_2018_adas.dbc",
+  "opendbc_repo/dbc/ford_lincoln_base_pt.dbc",
+  "opendbc_repo/dbc/honda_accord_2018_can_generated.dbc",
+  "opendbc_repo/dbc/acura_ilx_2016_can_generated.dbc",
+  "opendbc_repo/dbc/acura_rdx_2018_can_generated.dbc",
+  "opendbc_repo/dbc/acura_rdx_2020_can_generated.dbc",
+  "opendbc_repo/dbc/honda_civic_touring_2016_can_generated.dbc",
+  "opendbc_repo/dbc/honda_civic_hatchback_ex_2017_can_generated.dbc",
+  "opendbc_repo/dbc/honda_crv_touring_2016_can_generated.dbc",
+  "opendbc_repo/dbc/honda_crv_ex_2017_can_generated.dbc",
+  "opendbc_repo/dbc/honda_crv_ex_2017_body_generated.dbc",
+  "opendbc_repo/dbc/honda_crv_executive_2016_can_generated.dbc",
+  "opendbc_repo/dbc/honda_fit_ex_2018_can_generated.dbc",
+  "opendbc_repo/dbc/honda_odyssey_exl_2018_generated.dbc",
+  "opendbc_repo/dbc/honda_odyssey_extreme_edition_2018_china_can_generated.dbc",
+  "opendbc_repo/dbc/honda_insight_ex_2019_can_generated.dbc",
+  "opendbc_repo/dbc/acura_ilx_2016_nidec.dbc",
+  "opendbc_repo/dbc/honda_civic_ex_2022_can_generated.dbc",
+  "opendbc_repo/dbc/hyundai_canfd.dbc",
+  "opendbc_repo/dbc/hyundai_kia_generic.dbc",
+  "opendbc_repo/dbc/hyundai_kia_mando_front_radar_generated.dbc",
+  "opendbc_repo/dbc/mazda_2017.dbc",
+  "opendbc_repo/dbc/nissan_x_trail_2017_generated.dbc",
+  "opendbc_repo/dbc/nissan_leaf_2018_generated.dbc",
+  "opendbc_repo/dbc/subaru_global_2017_generated.dbc",
+  "opendbc_repo/dbc/subaru_global_2020_hybrid_generated.dbc",
+  "opendbc_repo/dbc/subaru_outback_2015_generated.dbc",
+  "opendbc_repo/dbc/subaru_outback_2019_generated.dbc",
+  "opendbc_repo/dbc/subaru_forester_2017_generated.dbc",
+  "opendbc_repo/dbc/toyota_tnga_k_pt_generated.dbc",
+  "opendbc_repo/dbc/toyota_new_mc_pt_generated.dbc",
+  "opendbc_repo/dbc/toyota_nodsu_pt_generated.dbc",
+  "opendbc_repo/dbc/toyota_adas.dbc",
+  "opendbc_repo/dbc/toyota_tss2_adas.dbc",
+  "opendbc_repo/dbc/vw_golf_mk4.dbc",
+  "opendbc_repo/dbc/vw_mqb_2010.dbc",
+  "opendbc_repo/dbc/tesla_can.dbc",
+  "opendbc_repo/dbc/tesla_radar_bosch_generated.dbc",
+  "opendbc_repo/dbc/tesla_radar_continental_generated.dbc",
+  "opendbc_repo/dbc/tesla_powertrain.dbc",
+]
+
+# Sunnypilot whitelist
+sunnypilot_whitelist = [
+  "^README.md",
+  ".*selfdrive/test/fuzzy_generation.py",
+  ".*selfdrive/test/helpers.py",
+  ".*selfdrive/test/__init__.py",
+  ".*selfdrive/test/setup_device_ci.sh",
+  ".*selfdrive/test/test_time_to_onroad.py",
+  ".*selfdrive/test/test_onroad.py",
+  ".*system/manager/test/test_manager.py",
+  ".*system/manager/test/__init__.py",
+  ".*system/qcomgpsd/tests/test_qcomgpsd.py",
+  ".*system/updated/casync/tests/test_casync.py",
+  ".*system/updated/tests/test_git.py",
+  ".*system/updated/tests/test_base.py",
+  ".*selfdrive/ui/tests/test_translations.py",
+  ".*selfdrive/car/tests/__init__.py",
+  ".*selfdrive/car/tests/test_car_interfaces.py",
+  ".*selfdrive/navd/tests/test_navd.py",
+  ".*selfdrive/navd/tests/test_map_renderer.py",
+  ".*selfdrive/boardd/tests/test_boardd_loopback.py",
+  ".*INTEGRATION.md",
+  ".*HOW-TOS.md",
+  ".*CARS.md",
+  ".*LIMITATIONS.md",
+  ".*CONTRIBUTING.md",
+  ".*sunnyhaibin0850_qrcode_paypal.me.png",
+  "opendbc/.*.dbc",
+]
+
+# Merge the whitelists
+whitelist += sunnypilot_whitelist
+

 if __name__ == "__main__":
  for f in Path(ROOT).rglob("**/*"):
@@ -1,27 +0,0 @@
-#!/usr/bin/env python3
-import argparse
-import multiprocessing
-from openpilot.system.hardware import HARDWARE
-
-
-def main():
-  parser = argparse.ArgumentParser(description='Control power saving mode')
-  parser.add_argument('--enable', action='store_true', help='Enable power saving mode')
-  parser.add_argument('--disable', action='store_true', help='Disable power saving mode')
-  args = parser.parse_args()
-
-  if args.enable and args.disable:
-    parser.error("Cannot specify both --enable and --disable")
-  elif not (args.enable or args.disable):
-    parser.error("Must specify either --enable or --disable")
-
-  print(f"Number of CPU cores available before: [{multiprocessing.cpu_count()}]")
-  HARDWARE.set_power_save(args.enable)
-
-  state = "enabled" if args.enable else "disabled"
-  print(f"Power save mode set to: [{state}]")
-  print(f"Number of CPU cores available now: [{multiprocessing.cpu_count()}]")
-
-
-if __name__ == "__main__":
-  main()
@@ -122,7 +122,7 @@ class CarSpecificEvents:

    elif self.CP.carName == 'volkswagen':
      events = self.create_common_events(CS, CS_prev, extra_gears=[GearShifter.eco, GearShifter.sport, GearShifter.manumatic],
-                                         pcm_enable=self.CP.pcmCruise,
+                                         pcm_enable=not self.CP.openpilotLongitudinalControl,
                                         enable_buttons=(ButtonType.setCruise, ButtonType.resumeCruise))

      # Low speed steer alert hysteresis logic
@@ -149,7 +149,7 @@ class CarSpecificEvents:
      # Main button also can trigger an engagement on these cars
      self.cruise_buttons.append(any(ev.type in HYUNDAI_ENABLE_BUTTONS for ev in CS.buttonEvents))
      events = self.create_common_events(CS, CS_prev, extra_gears=(GearShifter.sport, GearShifter.manumatic),
-                                         pcm_enable=self.CP.pcmCruise, allow_enable=any(self.cruise_buttons), allow_button_cancel=False)
+                                         pcm_enable=self.CP.pcmCruise, allow_enable=any(self.cruise_buttons))

      # low speed steer alert hysteresis logic (only for cars with steer cut off above 10 m/s)
      if CS.vEgo < (self.CP.minSteerSpeed + 2.) and self.CP.minSteerSpeed > 10.:
@@ -165,7 +165,7 @@ class CarSpecificEvents:
    return events

  def create_common_events(self, CS: structs.CarState, CS_prev: car.CarState, extra_gears=None, pcm_enable=True,
-                           allow_enable=True, allow_button_cancel=True, enable_buttons=(ButtonType.accelCruise, ButtonType.decelCruise)):
+                           allow_enable=True, enable_buttons=(ButtonType.accelCruise, ButtonType.decelCruise)):
    events = Events()

    if CS.doorOpen:
@@ -191,7 +191,7 @@ class CarSpecificEvents:
      events.add(EventName.speedTooHigh)
    if CS.cruiseState.nonAdaptive:
      events.add(EventName.wrongCruiseMode)
-    if CS.brakeHoldActive and self.CP.openpilotLongitudinalControl and self.CP.carName != 'toyota':
+    if CS.brakeHoldActive and self.CP.openpilotLongitudinalControl:
      events.add(EventName.brakeHold)
    if CS.parkingBrake:
      events.add(EventName.parkBrake)
@@ -216,8 +216,7 @@ class CarSpecificEvents:
      if not self.CP.pcmCruise and (b.type in enable_buttons and not b.pressed):
        events.add(EventName.buttonEnable)
      # Disable on rising and falling edge of cancel for both stock and OP long
-      # TODO: only check the cancel button with openpilot longitudinal on all brands to match panda safety
-      if b.type == ButtonType.cancel and (allow_button_cancel or not self.CP.pcmCruise):
+      if b.type == ButtonType.cancel:
        events.add(EventName.buttonCancel)

    # Handle permanent and temporary steering faults
@@ -23,7 +23,6 @@ from openpilot.selfdrive.car.cruise import VCruiseHelper
 from openpilot.selfdrive.car.car_specific import MockCarState

 from openpilot.sunnypilot.mads.mads import MadsParams
-from openpilot.sunnypilot.selfdrive.car.interfaces import setup_car_interface_sp, initialize_car_interface_sp

 REPLAY = "REPLAY" in os.environ

@@ -75,7 +74,6 @@ class Car:
    self.can_rcv_cum_timeout_counter = 0

    self.CC_prev = car.CarControl.new_message()
-    self.CS_prev = car.CarState.new_message()
    self.initialized_prev = False

    self.last_actuators_output = structs.CarControl.Actuators()
@@ -102,7 +100,6 @@ class Car:
          cached_params = _cached_params

      self.CI = get_car(*self.can_callbacks, obd_callback(self.params), experimental_long_allowed, num_pandas, cached_params)
-      setup_car_interface_sp(self.CI.CP, self.params)
      self.RI = get_radar_interface(self.CI.CP)
      self.CP = self.CI.CP

@@ -189,12 +186,8 @@ class Car:
    if can_rcv_valid and REPLAY:
      self.can_log_mono_time = messaging.log_from_bytes(can_strs[0]).logMonoTime

-    self.v_cruise_helper.update_v_cruise(CS, self.sm['carControl'].enabled, self.is_metric)
-    if self.sm['carControl'].enabled and not self.CC_prev.enabled:
-      # Use CarState w/ buttons from the step selfdrived enables on
-      self.v_cruise_helper.initialize_v_cruise(self.CS_prev, self.experimental_mode)
-
    # TODO: mirror the carState.cruiseState struct?
+    self.v_cruise_helper.update_v_cruise(CS, self.sm['carControl'].enabled, self.is_metric)
    CS.vCruise = float(self.v_cruise_helper.v_cruise_kph)
    CS.vCruiseCluster = float(self.v_cruise_helper.v_cruise_cluster_kph)

@@ -237,7 +230,6 @@ class Car:
      # Initialize CarInterface, once controls are ready
      # TODO: this can make us miss at least a few cycles when doing an ECU knockout
      self.CI.init(self.CP, *self.can_callbacks)
-      initialize_car_interface_sp(self.CP, self.params, *self.can_callbacks)
      # signal pandad to switch to car safety mode
      self.params.put_bool_nonblocking("ControlsReady", True)

@@ -252,6 +244,9 @@ class Car:
  def step(self):
    CS, RD = self.state_update()

+    if self.sm['carControl'].enabled and not self.CC_prev.enabled:
+      self.v_cruise_helper.initialize_v_cruise(CS, self.experimental_mode)
+
    self.state_publish(CS, RD)

    initialized = (not any(e.name == EventName.selfdriveInitializing for e in self.sm['onroadEvents']) and
@@ -260,7 +255,6 @@ class Car:
      self.controls_update(CS, self.sm['carControl'])

    self.initialized_prev = initialized
-    self.CS_prev = CS

  def params_thread(self, evt):
    while not evt.is_set():
@@ -1,8 +1,8 @@
 import math
-import numpy as np

 from cereal import car
 from openpilot.common.conversions import Conversions as CV
+from openpilot.common.numpy_fast import clip


 # WARNING: this value was determined based on the model's training distribution,
@@ -106,7 +106,7 @@ class VCruiseHelper:
    if CS.gasPressed and button_type in (ButtonType.decelCruise, ButtonType.setCruise):
      self.v_cruise_kph = max(self.v_cruise_kph, CS.vEgo * CV.MS_TO_KPH)

-    self.v_cruise_kph = np.clip(round(self.v_cruise_kph, 1), V_CRUISE_MIN, V_CRUISE_MAX)
+    self.v_cruise_kph = clip(round(self.v_cruise_kph, 1), V_CRUISE_MIN, V_CRUISE_MAX)

  def update_button_timers(self, CS, enabled):
    # increment timer for buttons still pressed
@@ -130,6 +130,6 @@ class VCruiseHelper:
    if any(b.type in (ButtonType.accelCruise, ButtonType.resumeCruise) for b in CS.buttonEvents) and self.v_cruise_initialized:
      self.v_cruise_kph = self.v_cruise_kph_last
    else:
-      self.v_cruise_kph = int(round(np.clip(CS.vEgo * CV.MS_TO_KPH, initial, V_CRUISE_MAX)))
+      self.v_cruise_kph = int(round(clip(CS.vEgo * CV.MS_TO_KPH, initial, V_CRUISE_MAX)))

    self.v_cruise_cluster_kph = self.v_cruise_kph
@@ -119,16 +119,15 @@ class Controls:

    # accel PID loop
    pid_accel_limits = self.CI.get_pid_accel_limits(self.CP, CS.vEgo, CS.vCruise * CV.KPH_TO_MS)
-    actuators.accel = float(self.LoC.update(CC.longActive, CS, long_plan.aTarget, long_plan.shouldStop, pid_accel_limits))
+    actuators.accel = self.LoC.update(CC.longActive, CS, long_plan.aTarget, long_plan.shouldStop, pid_accel_limits)

    # Steering PID loop and lateral MPC
    self.desired_curvature = clip_curvature(CS.vEgo, self.desired_curvature, model_v2.action.desiredCurvature)
-    actuators.curvature = float(self.desired_curvature)
-    steer, steeringAngleDeg, lac_log = self.LaC.update(CC.latActive, CS, self.VM, lp,
+    actuators.curvature = self.desired_curvature
+    actuators.steer, actuators.steeringAngleDeg, lac_log = self.LaC.update(CC.latActive, CS, self.VM, lp,
                                                                            self.steer_limited, self.desired_curvature,
                                                                            self.calibrated_pose) # TODO what if not available
-    actuators.steer = float(steer)
-    actuators.steeringAngleDeg = float(steeringAngleDeg)
+
    # Ensure no NaNs/Infs
    for p in ACTUATOR_FIELDS:
      attr = getattr(actuators, p)
@@ -193,7 +192,7 @@ class Controls:

    cs.longitudinalPlanMonoTime = self.sm.logMonoTime['longitudinalPlan']
    cs.lateralPlanMonoTime = self.sm.logMonoTime['modelV2']
-    cs.desiredCurvature = float(self.desired_curvature)
+    cs.desiredCurvature = self.desired_curvature
    cs.longControlState = self.LoC.long_control_state
    cs.upAccelCmd = float(self.LoC.pid.p)
    cs.uiAccelCmd = float(self.LoC.pid.i)
@@ -1,5 +1,5 @@
-import numpy as np
 from cereal import log
+from openpilot.common.numpy_fast import clip
 from openpilot.common.realtime import DT_CTRL

 MIN_SPEED = 1.0
@@ -13,10 +13,10 @@ MAX_LATERAL_JERK = 5.0
 MAX_VEL_ERR = 5.0

 def clip_curvature(v_ego, prev_curvature, new_curvature):
-  new_curvature = np.clip(new_curvature, -MAX_CURVATURE, MAX_CURVATURE)
+  new_curvature = clip(new_curvature, -MAX_CURVATURE, MAX_CURVATURE)
  v_ego = max(MIN_SPEED, v_ego)
  max_curvature_rate = MAX_LATERAL_JERK / (v_ego**2) # inexact calculation, check https://github.com/commaai/openpilot/pull/24755
-  safe_desired_curvature = np.clip(new_curvature,
+  safe_desired_curvature = clip(new_curvature,
                                prev_curvature - max_curvature_rate * DT_CTRL,
                                prev_curvature + max_curvature_rate * DT_CTRL)

@@ -26,6 +26,6 @@ def clip_curvature(v_ego, prev_curvature, new_curvature):
 def get_speed_error(modelV2: log.ModelDataV2, v_ego: float) -> float:
  # ToDo: Try relative error, and absolute speed
  if len(modelV2.temporalPose.trans):
-    vel_err = np.clip(modelV2.temporalPose.trans[0] - v_ego, -MAX_VEL_ERR, MAX_VEL_ERR)
+    vel_err = clip(modelV2.temporalPose.trans[0] - v_ego, -MAX_VEL_ERR, MAX_VEL_ERR)
    return float(vel_err)
  return 0.0
@@ -1,6 +1,6 @@
-import numpy as np
 from abc import abstractmethod, ABC

+from openpilot.common.numpy_fast import clip
 from openpilot.common.realtime import DT_CTRL

 MIN_LATERAL_CONTROL_SPEED = 0.3  # m/s
@@ -28,5 +28,5 @@ class LatControl(ABC):
      self.sat_count += self.sat_count_rate
    else:
      self.sat_count -= self.sat_count_rate
-    self.sat_count = np.clip(self.sat_count, 0.0, self.sat_limit)
+    self.sat_count = clip(self.sat_count, 0.0, self.sat_limit)
    return self.sat_count > (self.sat_limit - 1e-3)
@@ -23,7 +23,7 @@ class LatControlAngle(LatControl):
      angle_steers_des += params.angleOffsetDeg

    angle_control_saturated = abs(angle_steers_des - CS.steeringAngleDeg) > STEER_ANGLE_SATURATION_THRESHOLD
-    angle_log.saturated = bool(self._check_saturation(angle_control_saturated, CS, False))
+    angle_log.saturated = self._check_saturation(angle_control_saturated, CS, False)
    angle_log.steeringAngleDeg = float(CS.steeringAngleDeg)
    angle_log.steeringAngleDesiredDeg = angle_steers_des
    return 0, float(angle_steers_des), angle_log
@@ -39,10 +39,10 @@ class LatControlPID(LatControl):
      output_steer = self.pid.update(error, override=CS.steeringPressed,
                                     feedforward=steer_feedforward, speed=CS.vEgo)
      pid_log.active = True
-      pid_log.p = float(self.pid.p)
-      pid_log.i = float(self.pid.i)
-      pid_log.f = float(self.pid.f)
-      pid_log.output = float(output_steer)
-      pid_log.saturated = bool(self._check_saturation(self.steer_max - abs(output_steer) < 1e-3, CS, steer_limited))
+      pid_log.p = self.pid.p
+      pid_log.i = self.pid.i
+      pid_log.f = self.pid.f
+      pid_log.output = output_steer
+      pid_log.saturated = self._check_saturation(self.steer_max - abs(output_steer) < 1e-3, CS, steer_limited)

    return output_steer, angle_steers_des, pid_log
@@ -1,8 +1,8 @@
 import math
-import numpy as np

 from cereal import log
 from opendbc.car.interfaces import LatControlInputs
+from openpilot.common.numpy_fast import interp
 from openpilot.selfdrive.controls.lib.latcontrol import LatControl
 from openpilot.common.pid import PIDController
 from openpilot.selfdrive.controls.lib.vehicle_model import ACCELERATION_DUE_TO_GRAVITY
@@ -51,7 +51,7 @@ class LatControlTorque(LatControl):
      else:
        assert calibrated_pose is not None
        actual_curvature_pose = calibrated_pose.angular_velocity.yaw / CS.vEgo
-        actual_curvature = np.interp(CS.vEgo, [2.0, 5.0], [actual_curvature_vm, actual_curvature_pose])
+        actual_curvature = interp(CS.vEgo, [2.0, 5.0], [actual_curvature_vm, actual_curvature_pose])
        curvature_deadzone = 0.0
      desired_lateral_accel = desired_curvature * CS.vEgo ** 2

@@ -60,7 +60,7 @@ class LatControlTorque(LatControl):
      actual_lateral_accel = actual_curvature * CS.vEgo ** 2
      lateral_accel_deadzone = curvature_deadzone * CS.vEgo ** 2

-      low_speed_factor = np.interp(CS.vEgo, LOW_SPEED_X, LOW_SPEED_Y)**2
+      low_speed_factor = interp(CS.vEgo, LOW_SPEED_X, LOW_SPEED_Y)**2
      setpoint = desired_lateral_accel + low_speed_factor * desired_curvature
      measurement = actual_lateral_accel + low_speed_factor * actual_curvature
      gravity_adjusted_lateral_accel = desired_lateral_accel - roll_compensation
@@ -68,7 +68,7 @@ class LatControlTorque(LatControl):
                                                            setpoint, lateral_accel_deadzone, friction_compensation=False, gravity_adjusted=False)
      torque_from_measurement = self.torque_from_lateral_accel(LatControlInputs(measurement, roll_compensation, CS.vEgo, CS.aEgo), self.torque_params,
                                                               measurement, lateral_accel_deadzone, friction_compensation=False, gravity_adjusted=False)
-      pid_log.error = float(torque_from_setpoint - torque_from_measurement)
+      pid_log.error = torque_from_setpoint - torque_from_measurement
      ff = self.torque_from_lateral_accel(LatControlInputs(gravity_adjusted_lateral_accel, roll_compensation, CS.vEgo, CS.aEgo), self.torque_params,
                                          desired_lateral_accel - actual_lateral_accel, lateral_accel_deadzone, friction_compensation=True,
                                          gravity_adjusted=True)
@@ -80,14 +80,14 @@ class LatControlTorque(LatControl):
                                      freeze_integrator=freeze_integrator)

      pid_log.active = True
-      pid_log.p = float(self.pid.p)
-      pid_log.i = float(self.pid.i)
-      pid_log.d = float(self.pid.d)
-      pid_log.f = float(self.pid.f)
-      pid_log.output = float(-output_torque)
-      pid_log.actualLateralAccel = float(actual_lateral_accel)
-      pid_log.desiredLateralAccel = float(desired_lateral_accel)
-      pid_log.saturated = bool(self._check_saturation(self.steer_max - abs(output_torque) < 1e-3, CS, steer_limited))
+      pid_log.p = self.pid.p
+      pid_log.i = self.pid.i
+      pid_log.d = self.pid.d
+      pid_log.f = self.pid.f
+      pid_log.output = -output_torque
+      pid_log.actualLateralAccel = actual_lateral_accel
+      pid_log.desiredLateralAccel = desired_lateral_accel
+      pid_log.saturated = self._check_saturation(self.steer_max - abs(output_torque) < 1e-3, CS, steer_limited)

    # TODO left is positive in this convention
    return -output_torque, 0.0, pid_log
@@ -1,5 +1,5 @@
-import numpy as np
 from cereal import car
+from openpilot.common.numpy_fast import clip
 from openpilot.common.realtime import DT_CTRL
 from openpilot.selfdrive.controls.lib.drive_helpers import CONTROL_N
 from openpilot.common.pid import PIDController
@@ -84,5 +84,5 @@ class LongControl:
      output_accel = self.pid.update(error, speed=CS.vEgo,
                                     feedforward=a_target)

-    self.last_output_accel = np.clip(output_accel, accel_limits[0], accel_limits[1])
+    self.last_output_accel = clip(output_accel, accel_limits[0], accel_limits[1])
    return self.last_output_accel
@@ -4,6 +4,7 @@ import time
 import numpy as np
 from cereal import log
 from opendbc.car.interfaces import ACCEL_MIN
+from openpilot.common.numpy_fast import clip
 from openpilot.common.realtime import DT_MDL
 from openpilot.common.swaglog import cloudlog
 # WARNING: imports outside of constants will not trigger a rebuild
@@ -319,9 +320,9 @@ class LongitudinalMpc:
    # MPC will not converge if immediate crash is expected
    # Clip lead distance to what is still possible to brake for
    min_x_lead = ((v_ego + v_lead)/2) * (v_ego - v_lead) / (-ACCEL_MIN * 2)
-    x_lead = np.clip(x_lead, min_x_lead, 1e8)
-    v_lead = np.clip(v_lead, 0.0, 1e8)
-    a_lead = np.clip(a_lead, -10., 5.)
+    x_lead = clip(x_lead, min_x_lead, 1e8)
+    v_lead = clip(v_lead, 0.0, 1e8)
+    a_lead = clip(a_lead, -10., 5.)
    lead_xv = self.extrapolate_lead(x_lead, v_lead, a_lead, a_lead_tau)
    return lead_xv

@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 import math
 import numpy as np
+from openpilot.common.numpy_fast import clip, interp

 import cereal.messaging as messaging
 from opendbc.car.interfaces import ACCEL_MIN, ACCEL_MAX
@@ -29,7 +30,7 @@ _A_TOTAL_MAX_BP = [20., 40.]


 def get_max_accel(v_ego):
-  return np.interp(v_ego, A_CRUISE_MAX_BP, A_CRUISE_MAX_VALS)
+  return interp(v_ego, A_CRUISE_MAX_BP, A_CRUISE_MAX_VALS)

 def get_coast_accel(pitch):
  return np.sin(pitch) * -5.65 - 0.3  # fitted from data using xx/projects/allow_throttle/compute_coast_accel.py
@@ -42,7 +43,7 @@ def limit_accel_in_turns(v_ego, angle_steers, a_target, CP):
  """
  # FIXME: This function to calculate lateral accel is incorrect and should use the VehicleModel
  # The lookup table for turns should also be updated if we do this
-  a_total_max = np.interp(v_ego, _A_TOTAL_MAX_BP, _A_TOTAL_MAX_V)
+  a_total_max = interp(v_ego, _A_TOTAL_MAX_BP, _A_TOTAL_MAX_V)
  a_y = v_ego ** 2 * angle_steers * CV.DEG_TO_RAD / (CP.steerRatio * CP.wheelbase)
  a_x_allowed = math.sqrt(max(a_total_max ** 2 - a_y ** 2, 0.))

@@ -54,9 +55,9 @@ def get_accel_from_plan(speeds, accels, action_t=DT_MDL, vEgoStopping=0.05):
    v_now = speeds[0]
    a_now = accels[0]

-    v_target = np.interp(action_t, CONTROL_N_T_IDX, speeds)
+    v_target = interp(action_t, CONTROL_N_T_IDX, speeds)
    a_target = 2 * (v_target - v_now) / (action_t) - a_now
-    v_target_1sec = np.interp(action_t + 1.0, CONTROL_N_T_IDX, speeds)
+    v_target_1sec = interp(action_t + 1.0, CONTROL_N_T_IDX, speeds)
  else:
    v_target = 0.0
    v_target_1sec = 0.0
@@ -138,7 +139,7 @@ class LongitudinalPlanner:
    if reset_state:
      self.v_desired_filter.x = v_ego
      # Clip aEgo to cruise limits to prevent large accelerations when becoming active
-      self.a_desired = np.clip(sm['carState'].aEgo, accel_limits[0], accel_limits[1])
+      self.a_desired = clip(sm['carState'].aEgo, accel_limits[0], accel_limits[1])

    # Prevent divergence, smooth in current v_ego
    self.v_desired_filter.x = max(0.0, self.v_desired_filter.update(v_ego))
@@ -150,7 +151,7 @@ class LongitudinalPlanner:

    if not self.allow_throttle:
      clipped_accel_coast = max(accel_coast, accel_limits_turns[0])
-      clipped_accel_coast_interp = np.interp(v_ego, [MIN_ALLOW_THROTTLE_SPEED, MIN_ALLOW_THROTTLE_SPEED*2], [accel_limits_turns[1], clipped_accel_coast])
+      clipped_accel_coast_interp = interp(v_ego, [MIN_ALLOW_THROTTLE_SPEED, MIN_ALLOW_THROTTLE_SPEED*2], [accel_limits_turns[1], clipped_accel_coast])
      accel_limits_turns[1] = min(accel_limits_turns[1], clipped_accel_coast_interp)

    if force_slow_decel:
@@ -175,7 +176,7 @@ class LongitudinalPlanner:

    # Interpolate 0.05 seconds and save as starting point for next iteration
    a_prev = self.a_desired
-    self.a_desired = float(np.interp(self.dt, CONTROL_N_T_IDX, self.a_desired_trajectory))
+    self.a_desired = float(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

  def publish(self, sm, pm):
@@ -199,9 +200,9 @@ class LongitudinalPlanner:
    action_t =  self.CP.longitudinalActuatorDelay + DT_MDL
    a_target, should_stop = get_accel_from_plan(longitudinalPlan.speeds, longitudinalPlan.accels,
                                                action_t=action_t, vEgoStopping=self.CP.vEgoStopping)
-    longitudinalPlan.aTarget = float(a_target)
-    longitudinalPlan.shouldStop = bool(should_stop)
+    longitudinalPlan.aTarget = a_target
+    longitudinalPlan.shouldStop = should_stop
    longitudinalPlan.allowBrake = True
-    longitudinalPlan.allowThrottle = bool(self.allow_throttle)
+    longitudinalPlan.allowThrottle = self.allow_throttle

    pm.send('longitudinalPlan', plan_send)
@@ -1,11 +1,11 @@
 #!/usr/bin/env python3
 import math
-import numpy as np
 from collections import deque
 from typing import Any

 import capnp
 from cereal import messaging, log, car
+from openpilot.common.numpy_fast import interp
 from openpilot.common.params import Params
 from openpilot.common.realtime import DT_MDL, Priority, config_realtime_process
 from openpilot.common.swaglog import cloudlog
@@ -44,7 +44,7 @@ class KalmanParams:
          0.28144091, 0.27958406, 0.27783249, 0.27617149, 0.27458948, 0.27307714,
          0.27162685, 0.27023228, 0.26888809, 0.26758976, 0.26633338, 0.26511557,
          0.26393339, 0.26278425]
-    self.K = [[np.interp(dt, dts, K0)], [np.interp(dt, dts, K1)]]
+    self.K = [[interp(dt, dts, K0)], [interp(dt, dts, K1)]]


 class Track:
@@ -2,8 +2,8 @@
 import sys
 import argparse
 from subprocess import check_output, CalledProcessError
-from opendbc.car.uds import UdsClient, MessageTimeoutError, SESSION_TYPE, DTC_GROUP_TYPE
 from panda import Panda
+from panda.python.uds import UdsClient, MessageTimeoutError, SESSION_TYPE, DTC_GROUP_TYPE

 parser = argparse.ArgumentParser(description="clear DTC status")
 parser.add_argument("addr", type=lambda x: int(x,0), nargs="?", default=0x7DF) # default is functional (broadcast) address
@@ -1,9 +1,9 @@
 #!/usr/bin/env python3
 import argparse

-from opendbc.car import uds
 from openpilot.tools.lib.live_logreader import live_logreader
 from openpilot.tools.lib.logreader import LogReader, ReadMode
+from panda.python import uds


 def main(route: str | None, addrs: list[int]):
@@ -16,8 +16,8 @@ import argparse
 from typing import NamedTuple
 from subprocess import check_output, CalledProcessError

-from opendbc.car.uds import UdsClient, SESSION_TYPE, DATA_IDENTIFIER_TYPE
 from panda.python import Panda
+from panda.python.uds import UdsClient, SESSION_TYPE, DATA_IDENTIFIER_TYPE

 class ConfigValues(NamedTuple):
  default_config: bytes
@@ -1,10 +1,10 @@
 #!/usr/bin/env python3
 import argparse
-import numpy as np
 import capnp
 from collections import defaultdict

 from cereal.messaging import SubMaster
+from openpilot.common.numpy_fast import mean

 def cputime_total(ct):
  return ct.user + ct.nice + ct.system + ct.idle + ct.iowait + ct.irq + ct.softirq
@@ -49,7 +49,7 @@ if __name__ == "__main__":

    if sm.updated['deviceState']:
      t = sm['deviceState']
-      last_temp = np.mean(t.cpuTempC)
+      last_temp = mean(t.cpuTempC)
      last_mem = t.memoryUsagePercent

    if sm.updated['procLog']:
@@ -72,7 +72,7 @@ if __name__ == "__main__":
      total_times = total_times_new[:]
      busy_times = busy_times_new[:]

-      print(f"CPU {100.0 * np.mean(cores):.2f}% - RAM: {last_mem:.2f}% - Temp {last_temp:.2f}C")
+      print(f"CPU {100.0 * mean(cores):.2f}% - RAM: {last_mem:.2f}% - Temp {last_temp:.2f}C")

      if args.cpu and prev_proclog is not None and prev_proclog_t is not None:
        procs: dict[str, float] = defaultdict(float)
@@ -2,8 +2,9 @@
 import sys
 import argparse
 from subprocess import check_output, CalledProcessError
-from opendbc.car.uds import UdsClient, SESSION_TYPE, DTC_REPORT_TYPE, DTC_STATUS_MASK_TYPE, get_dtc_num_as_str, get_dtc_status_names
 from panda import Panda
+from panda.python.uds import UdsClient, SESSION_TYPE, DTC_REPORT_TYPE, DTC_STATUS_MASK_TYPE
+from panda.python.uds import get_dtc_num_as_str, get_dtc_status_names

 parser = argparse.ArgumentParser(description="read DTC status")
 parser.add_argument("addr", type=lambda x: int(x,0))
@@ -3,19 +3,14 @@ import time

 from cereal import car, log, messaging
 from openpilot.common.params import Params
-from openpilot.system.manager.process_config import managed_processes, is_snpe_model
+from openpilot.system.manager.process_config import managed_processes
 from openpilot.system.hardware import HARDWARE

 if __name__ == "__main__":
  CP = car.CarParams(notCar=True, wheelbase=1, steerRatio=10)
-  params = Params()
-  params.put("CarParams", CP.to_bytes())
-  if use_snpe_modeld := is_snpe_model(False, params, CP):
-    print("Using SNPE modeld")
-  HARDWARE.set_power_save(False)
+  Params().put("CarParams", CP.to_bytes())

-  procs = ['camerad', 'ui', 'calibrationd', 'plannerd', 'dmonitoringmodeld', 'dmonitoringd']
-  procs += ["modeld_snpe" if use_snpe_modeld else "modeld"]
+  procs = ['camerad', 'ui', 'modeld', 'calibrationd', 'plannerd', 'dmonitoringmodeld', 'dmonitoringd']
  for p in procs:
    managed_processes[p].start()

@@ -3,9 +3,9 @@
 import argparse
 import struct
 from enum import IntEnum
-from opendbc.car.uds import UdsClient, MessageTimeoutError, NegativeResponseError, SESSION_TYPE,\
-  DATA_IDENTIFIER_TYPE, ACCESS_TYPE
 from panda import Panda
+from panda.python.uds import UdsClient, MessageTimeoutError, NegativeResponseError, SESSION_TYPE,\
+  DATA_IDENTIFIER_TYPE, ACCESS_TYPE
 from datetime import date

 # TODO: extend UDS library to allow custom/vendor-defined data identifiers without ignoring type checks
@@ -8,6 +8,7 @@ import cereal.messaging as messaging
 from cereal import car, log
 from openpilot.common.params import Params
 from openpilot.common.realtime import config_realtime_process, DT_MDL
+from openpilot.common.numpy_fast import clip
 from openpilot.selfdrive.locationd.models.car_kf import CarKalman, ObservationKind, States
 from openpilot.selfdrive.locationd.models.constants import GENERATED_DIR
 from openpilot.selfdrive.locationd.helpers import PoseCalibrator, Pose
@@ -65,7 +66,7 @@ class ParamsLearner:
        # This is done to bound the road roll estimate when localizer values are invalid
        roll = 0.0
        roll_std = np.radians(10.0)
-      self.roll = np.clip(roll, self.roll - ROLL_MAX_DELTA, self.roll + ROLL_MAX_DELTA)
+      self.roll = clip(roll, self.roll - ROLL_MAX_DELTA, self.roll + ROLL_MAX_DELTA)

      yaw_rate_valid = msg.angularVelocityDevice.valid and self.calibrator.calib_valid
      yaw_rate_valid = yaw_rate_valid and 0 < self.yaw_rate_std < 10  # rad/s
@@ -202,11 +203,11 @@ def main():
        learner = ParamsLearner(CP, CP.steerRatio, 1.0, 0.0)
        x = learner.kf.x

-      angle_offset_average = np.clip(math.degrees(x[States.ANGLE_OFFSET].item()),
+      angle_offset_average = clip(math.degrees(x[States.ANGLE_OFFSET].item()),
                                  angle_offset_average - MAX_ANGLE_OFFSET_DELTA, angle_offset_average + MAX_ANGLE_OFFSET_DELTA)
-      angle_offset = np.clip(math.degrees(x[States.ANGLE_OFFSET].item() + x[States.ANGLE_OFFSET_FAST].item()),
+      angle_offset = clip(math.degrees(x[States.ANGLE_OFFSET].item() + x[States.ANGLE_OFFSET_FAST].item()),
                          angle_offset - MAX_ANGLE_OFFSET_DELTA, angle_offset + MAX_ANGLE_OFFSET_DELTA)
-      roll = np.clip(float(x[States.ROAD_ROLL].item()), roll - ROLL_MAX_DELTA, roll + ROLL_MAX_DELTA)
+      roll = clip(float(x[States.ROAD_ROLL].item()), roll - ROLL_MAX_DELTA, roll + ROLL_MAX_DELTA)
      roll_std = float(P[States.ROAD_ROLL].item())
      if learner.active and learner.speed > LOW_ACTIVE_SPEED:
        # Account for the opposite signs of the yaw rates
@@ -225,9 +226,9 @@ def main():
      liveParameters.sensorValid = sensors_valid
      liveParameters.steerRatio = float(x[States.STEER_RATIO].item())
      liveParameters.stiffnessFactor = float(x[States.STIFFNESS].item())
-      liveParameters.roll = float(roll)
-      liveParameters.angleOffsetAverageDeg = float(angle_offset_average)
-      liveParameters.angleOffsetDeg = float(angle_offset)
+      liveParameters.roll = roll
+      liveParameters.angleOffsetAverageDeg = angle_offset_average
+      liveParameters.angleOffsetDeg = angle_offset
      liveParameters.valid = all((
        avg_offset_valid,
        total_offset_valid,
@@ -1,21 +1,15 @@
 #!/usr/bin/env python3
-import os
 from openpilot.system.hardware import TICI

+from openpilot.selfdrive.modeld.runners.model_runner import ONNXRunner, TinygradRunner
+from openpilot.sunnypilot.models.helpers import is_active_model_20hz
+
 #
-if TICI:
-  from tinygrad.tensor import Tensor
-  from tinygrad.dtype import dtypes
-  from openpilot.selfdrive.modeld.runners.tinygrad_helpers import qcom_tensor_from_opencl_address
-  os.environ['QCOM'] = '1'
-else:
-  from openpilot.selfdrive.modeld.runners.ort_helpers import make_onnx_cpu_runner
+import os
 import time
-import pickle
 import numpy as np
 import cereal.messaging as messaging
 from cereal import car, log
-from pathlib import Path
 from setproctitle import setproctitle
 from cereal.messaging import PubMaster, SubMaster
 from msgq.visionipc import VisionIpcClient, VisionStreamType, VisionBuf
@@ -27,19 +21,16 @@ from openpilot.common.realtime import config_realtime_process
 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.hardware import PC
 from openpilot.selfdrive.controls.lib.desire_helper import DesireHelper
 from openpilot.selfdrive.modeld.parse_model_outputs import Parser
 from openpilot.selfdrive.modeld.fill_model_msg import fill_model_msg, fill_pose_msg, PublishState
 from openpilot.selfdrive.modeld.constants import ModelConstants
 from openpilot.selfdrive.modeld.models.commonmodel_pyx import DrivingModelFrame, CLContext

-
 PROCESS_NAME = "selfdrive.modeld.modeld"
-SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
-
-MODEL_PATH = Path(__file__).parent / 'models/supercombo.onnx'
-MODEL_PKL_PATH = Path(__file__).parent / 'models/supercombo_tinygrad.pkl'
-METADATA_PATH = Path(__file__).parent / 'models/supercombo_metadata.pkl'
+USE_ONNX = bool(os.getenv('USE_ONNX', PC))
+IS_20HZ_MODEL_DEFAULT = False

 class FrameMeta:
  frame_id: int = 0
@@ -57,81 +48,86 @@ class ModelState:
  prev_desire: np.ndarray  # for tracking the rising edge of the pulse

  def __init__(self, context: CLContext):
-    self.frames = {'input_imgs': DrivingModelFrame(context), 'big_input_imgs': DrivingModelFrame(context)}
+    self.is_20hz = IS_20HZ_MODEL_DEFAULT
+    if (active_20hz := is_active_model_20hz(None)) is not None:
+      self.is_20hz = active_20hz
+
+    self.frames = {'input_imgs': DrivingModelFrame(context, self.is_20hz), 'big_input_imgs': DrivingModelFrame(context, self.is_20hz)}
    self.prev_desire = np.zeros(ModelConstants.DESIRE_LEN, dtype=np.float32)
+    self.full_features_20Hz = np.zeros((ModelConstants.FULL_HISTORY_BUFFER_LEN, ModelConstants.FEATURE_LEN), dtype=np.float32)
+    self.desire_20Hz =  np.zeros((ModelConstants.FULL_HISTORY_BUFFER_LEN + 1, ModelConstants.DESIRE_LEN), dtype=np.float32)
+    # Initialize model runner
+    self.model_runner = ONNXRunner(self.frames) if (not TICI) and USE_ONNX else TinygradRunner(self.frames)

    # img buffers are managed in openCL transform code
-    self.numpy_inputs = {
-      'desire': np.zeros((1, (ModelConstants.FULL_HISTORY_BUFFER_LEN+1), ModelConstants.DESIRE_LEN), dtype=np.float32),
-      'traffic_convention': np.zeros((1, ModelConstants.TRAFFIC_CONVENTION_LEN), dtype=np.float32),
-      'lateral_control_params': np.zeros((1, ModelConstants.LATERAL_CONTROL_PARAMS_LEN), dtype=np.float32),
-      'prev_desired_curv': np.zeros((1, (ModelConstants.FULL_HISTORY_BUFFER_LEN+1), ModelConstants.PREV_DESIRED_CURV_LEN), dtype=np.float32),
-      'features_buffer': np.zeros((1, ModelConstants.FULL_HISTORY_BUFFER_LEN,  ModelConstants.FEATURE_LEN), dtype=np.float32),
-    }
+    self.numpy_inputs = {}

-    with open(METADATA_PATH, 'rb') as f:
-      model_metadata = pickle.load(f)
-    self.input_shapes =  model_metadata['input_shapes']
+    for key, shape in self.model_runner.input_shapes.items():
+      if key not in self.frames:  # Managed by opencl
+        self.numpy_inputs[key] = np.zeros(shape, dtype=np.float32)

-    self.output_slices = model_metadata['output_slices']
-    net_output_size = model_metadata['output_shapes']['outputs'][1]
-    self.output = np.zeros(net_output_size, dtype=np.float32)
    self.parser = Parser()

-    if TICI:
-      self.tensor_inputs = {k: Tensor(v, device='NPY').realize() for k,v in self.numpy_inputs.items()}
-      with open(MODEL_PKL_PATH, "rb") as f:
-        self.model_run = pickle.load(f)
-    else:
-      self.onnx_cpu_runner = make_onnx_cpu_runner(MODEL_PATH)
+    net_output_size = self.model_runner.model_metadata['output_shapes']['outputs'][1]
+    self.output = np.zeros(net_output_size, dtype=np.float32)

-  def slice_outputs(self, model_outputs: np.ndarray) -> dict[str, np.ndarray]:
-    parsed_model_outputs = {k: model_outputs[np.newaxis, v] for k,v in self.output_slices.items()}
-    if SEND_RAW_PRED:
-      parsed_model_outputs['raw_pred'] = model_outputs.copy()
-    return parsed_model_outputs
+    num_elements = self.numpy_inputs['features_buffer'].shape[1]
+    step_size = int(-100 / num_elements)
+    self.full_features_20Hz_idxs = np.arange(step_size, step_size * (num_elements + 1), step_size)[::-1]
+    self.desire_reshape_dims = (self.numpy_inputs['desire'].shape[0], self.numpy_inputs['desire'].shape[1], -1, self.numpy_inputs['desire'].shape[2])

  def run(self, buf: VisionBuf, wbuf: VisionBuf, transform: np.ndarray, transform_wide: np.ndarray,
-                inputs: dict[str, np.ndarray], prepare_only: bool) -> dict[str, np.ndarray] | None:
+          inputs: dict[str, np.ndarray], prepare_only: bool) -> dict[str, np.ndarray] | None:
    # Model decides when action is completed, so desire input is just a pulse triggered on rising edge
    inputs['desire'][0] = 0
    new_desire = np.where(inputs['desire'] - self.prev_desire > .99, inputs['desire'], 0)
    self.prev_desire[:] = inputs['desire']

-    self.numpy_inputs['desire'][0,:-1] = self.numpy_inputs['desire'][0,1:]
-    self.numpy_inputs['desire'][0,-1] = new_desire
+    if self.is_20hz:
+      self.desire_20Hz[:-1] = self.desire_20Hz[1:]
+      self.desire_20Hz[-1] = new_desire
+      self.numpy_inputs['desire'][:] = self.desire_20Hz.reshape(self.desire_reshape_dims).max(axis=2)
+    else:
+      self.numpy_inputs['desire'][0,:-1] = self.numpy_inputs['desire'][0,1:]
+      self.numpy_inputs['desire'][0,-1] = new_desire
+
+    for key in self.numpy_inputs:
+      if key in inputs and key not in ['desire']:
+        self.numpy_inputs[key][:] = inputs[key]

-    self.numpy_inputs['traffic_convention'][:] = inputs['traffic_convention']
-    self.numpy_inputs['lateral_control_params'][:] = inputs['lateral_control_params']
    imgs_cl = {'input_imgs': self.frames['input_imgs'].prepare(buf, transform.flatten()),
               'big_input_imgs': self.frames['big_input_imgs'].prepare(wbuf, transform_wide.flatten())}

-    if TICI:
-      # The imgs tensors are backed by opencl memory, only need init once
-      for key in imgs_cl:
-        if key not in self.tensor_inputs:
-          self.tensor_inputs[key] = qcom_tensor_from_opencl_address(imgs_cl[key].mem_address, self.input_shapes[key], dtype=dtypes.uint8)
-    else:
-      for key in imgs_cl:
-        self.numpy_inputs[key] = self.frames[key].buffer_from_cl(imgs_cl[key]).reshape(self.input_shapes[key]).astype(dtype=np.float32)
+    # Prepare inputs using the model runner
+    self.model_runner.prepare_inputs(imgs_cl, self.numpy_inputs)

    if prepare_only:
      return None

-    if TICI:
-      self.output = self.model_run(**self.tensor_inputs).numpy().flatten()
+    # Run model inference
+    self.output = self.model_runner.run_model()
+    outputs = self.parser.parse_outputs(self.model_runner.slice_outputs(self.output))
+
+    if self.is_20hz:
+      self.full_features_20Hz[:-1] = self.full_features_20Hz[1:]
+      self.full_features_20Hz[-1] = outputs['hidden_state'][0, :]
+      self.numpy_inputs['features_buffer'][:] = self.full_features_20Hz[self.full_features_20Hz_idxs]
    else:
-      self.output = self.onnx_cpu_runner.run(None, self.numpy_inputs)[0].flatten()
+      self.numpy_inputs['features_buffer'][0,:-1] = self.numpy_inputs['features_buffer'][0,1:]
+      self.numpy_inputs['features_buffer'][0,-1] = outputs['hidden_state'][0, :]

-    outputs = self.parser.parse_outputs(self.slice_outputs(self.output))
+    if "desired_curvature" in outputs:
+      input_name_prev = None

-    self.numpy_inputs['features_buffer'][0,:-1] = self.numpy_inputs['features_buffer'][0,1:]
-    self.numpy_inputs['features_buffer'][0,-1] = outputs['hidden_state'][0, :]
+      if "prev_desired_curvs" in self.numpy_inputs.keys():
+        input_name_prev = 'prev_desired_curvs'
+      elif "prev_desired_curv" in self.numpy_inputs.keys():
+        input_name_prev = 'prev_desired_curv'

-
-    # TODO model only uses last value now
-    self.numpy_inputs['prev_desired_curv'][0,:-1] = self.numpy_inputs['prev_desired_curv'][0,1:]
-    self.numpy_inputs['prev_desired_curv'][0,-1,:] = outputs['desired_curvature'][0, :]
+      if input_name_prev is not None:
+        len = outputs['desired_curvature'][0].size
+        self.numpy_inputs['prev_desired_curv'][0,:-len] = self.numpy_inputs['prev_desired_curv'][0,len:]
+        self.numpy_inputs['prev_desired_curv'][0,-len,:] = outputs['desired_curvature'][0, :]
    return outputs


@@ -242,7 +238,6 @@ def main(demo=False):
    is_rhd = sm["driverMonitoringState"].isRHD
    frame_id = sm["roadCameraState"].frameId
    v_ego = max(sm["carState"].vEgo, 0.)
-    lateral_control_params = np.array([v_ego, steer_delay], dtype=np.float32)
    if sm.updated["liveCalibration"] and sm.seen['roadCameraState'] and sm.seen['deviceState']:
      device_from_calib_euler = np.array(sm["liveCalibration"].rpyCalib, dtype=np.float32)
      dc = DEVICE_CAMERAS[(str(sm['deviceState'].deviceType), str(sm['roadCameraState'].sensor))]
@@ -273,8 +268,10 @@ def main(demo=False):
    inputs:dict[str, np.ndarray] = {
      'desire': vec_desire,
      'traffic_convention': traffic_convention,
-      'lateral_control_params': lateral_control_params,
-      }
+    }
+
+    if "lateral_control_params" in model.numpy_inputs.keys():
+      inputs['lateral_control_params'] = np.array([v_ego, steer_delay], dtype=np.float32)

    mt1 = time.perf_counter()
    model_output = model.run(buf_main, buf_extra, model_transform_main, model_transform_extra, inputs, prepare_only)
@@ -5,11 +5,11 @@

 #include "common/clutil.h"

-DrivingModelFrame::DrivingModelFrame(cl_device_id device_id, cl_context context) : ModelFrame(device_id, context) {
+DrivingModelFrame::DrivingModelFrame(cl_device_id device_id, cl_context context, bool is_20hz) : ModelFrame(device_id, context), is_20hz(is_20hz) {
  input_frames = std::make_unique<uint8_t[]>(buf_size);
  input_frames_cl = CL_CHECK_ERR(clCreateBuffer(context, CL_MEM_READ_WRITE, buf_size, NULL, &err));
-  img_buffer_20hz_cl = CL_CHECK_ERR(clCreateBuffer(context, CL_MEM_READ_WRITE, 2*frame_size_bytes, NULL, &err));
-  region.origin = 1 * frame_size_bytes;
+  img_buffer_20hz_cl = CL_CHECK_ERR(clCreateBuffer(context, CL_MEM_READ_WRITE, buf_len*frame_size_bytes, NULL, &err));
+  region.origin = (buf_len - 1) * frame_size_bytes;
  region.size = frame_size_bytes;
  last_img_cl = CL_CHECK_ERR(clCreateSubBuffer(img_buffer_20hz_cl, CL_MEM_READ_WRITE, CL_BUFFER_CREATE_TYPE_REGION, &region, &err));

@@ -20,7 +20,7 @@ DrivingModelFrame::DrivingModelFrame(cl_device_id device_id, cl_context context)
 cl_mem* DrivingModelFrame::prepare(cl_mem yuv_cl, int frame_width, int frame_height, int frame_stride, int frame_uv_offset, const mat3& projection) {
  run_transform(yuv_cl, MODEL_WIDTH, MODEL_HEIGHT, frame_width, frame_height, frame_stride, frame_uv_offset, projection);

-  for (int i = 0; i < 1; i++) {
+  for (int i = 0; i < (buf_len - 1); i++) {
    CL_CHECK(clEnqueueCopyBuffer(q, img_buffer_20hz_cl, img_buffer_20hz_cl, (i+1)*frame_size_bytes, i*frame_size_bytes, frame_size_bytes, 0, nullptr, nullptr));
  }
  loadyuv_queue(&loadyuv, q, y_cl, u_cl, v_cl, last_img_cl);
@@ -64,7 +64,7 @@ protected:

 class DrivingModelFrame : public ModelFrame {
 public:
-  DrivingModelFrame(cl_device_id device_id, cl_context context);
+  DrivingModelFrame(cl_device_id device_id, cl_context context, bool is_20hz = false);
  ~DrivingModelFrame();
  cl_mem* prepare(cl_mem yuv_cl, int frame_width, int frame_height, int frame_stride, int frame_uv_offset, const mat3& projection);

@@ -74,6 +74,9 @@ public:
  const int buf_size = MODEL_FRAME_SIZE * 2;
  const size_t frame_size_bytes = MODEL_FRAME_SIZE * sizeof(uint8_t);

+  const bool is_20hz;
+  const int buf_len = is_20hz ? 5 : 2;
+
 private:
  LoadYUVState loadyuv;
  cl_mem img_buffer_20hz_cl, last_img_cl, input_frames_cl;
@@ -10,7 +10,6 @@ cdef extern from "common/clutil.h":
  cdef unsigned long CL_DEVICE_TYPE_DEFAULT
  cl_device_id cl_get_device_id(unsigned long)
  cl_context cl_create_context(cl_device_id)
-  void cl_release_context(cl_context)

 cdef extern from "selfdrive/modeld/models/commonmodel.h":
  cppclass ModelFrame:
@@ -20,7 +19,7 @@ cdef extern from "selfdrive/modeld/models/commonmodel.h":

  cppclass DrivingModelFrame:
    int buf_size
-    DrivingModelFrame(cl_device_id, cl_context)
+    DrivingModelFrame(cl_device_id, cl_context, bint)

  cppclass MonitoringModelFrame:
    int buf_size
@@ -8,7 +8,7 @@ from libc.stdint cimport uintptr_t

 from msgq.visionipc.visionipc cimport cl_mem
 from msgq.visionipc.visionipc_pyx cimport VisionBuf, CLContext as BaseCLContext
-from .commonmodel cimport CL_DEVICE_TYPE_DEFAULT, cl_get_device_id, cl_create_context, cl_release_context
+from .commonmodel cimport CL_DEVICE_TYPE_DEFAULT, cl_get_device_id, cl_create_context
 from .commonmodel cimport mat3, ModelFrame as cppModelFrame, DrivingModelFrame as cppDrivingModelFrame, MonitoringModelFrame as cppMonitoringModelFrame


@@ -17,10 +17,6 @@ cdef class CLContext(BaseCLContext):
    self.device_id = cl_get_device_id(CL_DEVICE_TYPE_DEFAULT)
    self.context = cl_create_context(self.device_id)

-  def __dealloc__(self):
-    if self.context:
-      cl_release_context(self.context)
-
 cdef class CLMem:
  @staticmethod
  cdef create(void * cmem):
@@ -59,8 +55,8 @@ cdef class ModelFrame:
 cdef class DrivingModelFrame(ModelFrame):
  cdef cppDrivingModelFrame * _frame

-  def __cinit__(self, CLContext context):
-    self._frame = new cppDrivingModelFrame(context.device_id, context.context)
+  def __cinit__(self, CLContext context, bint is_20hz=False):
+    self._frame = new cppDrivingModelFrame(context.device_id, context.context, is_20hz)
    self.frame = <cppModelFrame*>(self._frame)
    self.buf_size = self._frame.buf_size

@@ -85,6 +85,7 @@ class Parser:
    outs[name + '_stds'] = pred_std_final.reshape(final_shape)

  def parse_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
+    """ Parse the model outputs into a dictionary of numpy arrays. The input_keys are used to determine how the output should be parsed. """
    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))
@@ -0,0 +1,120 @@
+import os
+from openpilot.system.hardware import TICI
+
+from openpilot.sunnypilot.modeld.run_helpers import get_custom_model_paths
+#
+from tinygrad.tensor import Tensor, dtypes
+from openpilot.selfdrive.modeld.runners.tinygrad_helpers import qcom_tensor_from_opencl_address
+from openpilot.selfdrive.modeld.runners.ort_helpers import make_onnx_cpu_runner, ORT_TYPES_TO_NP_TYPES
+import pickle
+import numpy as np
+from pathlib import Path
+from abc import ABC, abstractmethod
+from openpilot.selfdrive.modeld.models.commonmodel_pyx import DrivingModelFrame, CLMem
+from openpilot.system.hardware import PC
+
+if TICI:
+  os.environ['QCOM'] = '1'
+
+SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
+MODEL_PATH = Path(__file__).parent / '../models/supercombo.onnx'
+MODEL_PKL_PATH = Path(__file__).parent / '../models/supercombo_tinygrad.pkl'
+METADATA_PATH = Path(__file__).parent / '../models/supercombo_metadata.pkl'
+USE_ONNX = os.getenv('USE_ONNX', PC)
+
+
+class ModelRunner(ABC):
+  """Abstract base class for model runners that defines the interface for running ML models."""
+
+  def __init__(self):
+    """Initialize the model runner with paths to model and metadata files."""
+    self.model_paths = ({"model": MODEL_PATH, "metadata": METADATA_PATH} if USE_ONNX else
+                        get_custom_model_paths() or {"model": MODEL_PKL_PATH, "metadata": METADATA_PATH})
+
+    with open(self.model_paths["metadata"], 'rb') as f:
+      self.model_metadata = pickle.load(f)
+
+    self.input_shapes = self.model_metadata['input_shapes']
+    self.output_slices = self.model_metadata['output_slices']
+    self.inputs: dict = {}
+
+  @abstractmethod
+  def prepare_inputs(self, imgs_cl: dict[str, CLMem], numpy_inputs: dict[str, np.ndarray]) -> dict:
+    """Prepare inputs for model inference."""
+
+  @abstractmethod
+  def run_model(self):
+    """Run model inference with prepared inputs."""
+
+  def slice_outputs(self, model_outputs: np.ndarray) -> dict:
+    """Slice model outputs according to metadata configuration."""
+    parsed_outputs = {k: model_outputs[np.newaxis, v] for k, v in self.output_slices.items()}
+    if SEND_RAW_PRED:
+      parsed_outputs['raw_pred'] = model_outputs.copy()
+    return parsed_outputs
+
+
+class TinygradRunner(ModelRunner):
+  """Tinygrad implementation of model runner for TICI hardware."""
+
+  def __init__(self, frames: dict[str, DrivingModelFrame] | None = None):
+    super().__init__()
+    if not str(self.model_paths["model"]).endswith("_tinygrad.pkl"):
+      raise ValueError(f"Tinygrad model must be a _tinygrad.pkl file, we got {self.model_paths['model']}")
+
+    # Load Tinygrad model
+    with open(self.model_paths["model"], "rb") as f:
+      self.model_run = pickle.load(f)
+
+    self.input_to_dtype = {}
+    self.input_to_device = {}
+
+    for idx, name in enumerate(self.model_run.captured.expected_names):
+      self.input_to_dtype[name] = self.model_run.captured.expected_st_vars_dtype_device[idx][2]  # 2 is the dtype
+      self.input_to_device[name] = self.model_run.captured.expected_st_vars_dtype_device[idx][3]  # 3 is the device
+
+    assert TICI or frames is not None, "TinygradRunner requires frames for non-TICI hardware"
+    self.frames = frames
+    self.is_memory_model = None  # Use None to indicate that it hasn't been determined yet
+
+  def prepare_inputs(self, imgs_cl: dict[str, CLMem], numpy_inputs: dict[str, np.ndarray]) -> dict:
+    # Initialize image tensors if not already done
+    for key in imgs_cl:
+      if TICI and key not in self.inputs:
+        self.inputs[key] = qcom_tensor_from_opencl_address(imgs_cl[key].mem_address, self.input_shapes[key], dtype=dtypes.uint8)
+      elif not TICI:
+        shape = self.frames[key].buffer_from_cl(imgs_cl[key]).reshape(self.input_shapes[key])
+        self.inputs[key] = Tensor(shape, device=self.input_to_device[key], dtype=self.input_to_dtype[key]).realize()
+
+    # Update numpy inputs
+    for key, value in numpy_inputs.items():
+      if key not in imgs_cl:
+        self.inputs[key] = Tensor(value, device=self.input_to_device[key], dtype=self.input_to_dtype[key]).realize()
+
+    return self.inputs
+
+  def run_model(self):
+    return self.model_run(**self.inputs).numpy().flatten()
+
+
+class ONNXRunner(ModelRunner):
+  """ONNX implementation of model runner for non-TICI hardware."""
+
+  def __init__(self, frames: dict[str, DrivingModelFrame]):
+    super().__init__()
+    self.runner = make_onnx_cpu_runner(self.model_paths["model"])
+    self.frames = frames
+
+    self.input_to_nptype = {
+      model_input.name: ORT_TYPES_TO_NP_TYPES[model_input.type]
+      for model_input in self.runner.get_inputs()
+    }
+
+  def prepare_inputs(self, imgs_cl: dict[str, CLMem], numpy_inputs: dict[str, np.ndarray]) -> dict:
+    self.inputs = numpy_inputs.copy()
+    for key in imgs_cl:
+      self.inputs[key] = self.frames[key].buffer_from_cl(imgs_cl[key]).reshape(self.input_shapes[key]).astype(dtype=np.float32)
+    return self.inputs
+
+  def run_model(self):
+    return self.runner.run(None, self.inputs)[0].flatten()
@@ -1,9 +1,9 @@
 from math import atan2
-import numpy as np

 from cereal import car, log
 import cereal.messaging as messaging
 from openpilot.selfdrive.selfdrived.events import Events
+from openpilot.common.numpy_fast import interp
 from openpilot.common.realtime import DT_DMON
 from openpilot.common.filter_simple import FirstOrderFilter
 from openpilot.common.stat_live import RunningStatFilter
@@ -205,10 +205,10 @@ class DriverMonitoring:
    bp = model_data.meta.disengagePredictions.brakeDisengageProbs[0] # brake disengage prob in next 2s
    k1 = max(-0.00156*((car_speed-16)**2)+0.6, 0.2)
    bp_normal = max(min(bp / k1, 0.5),0)
-    self.pose.cfactor_pitch = np.interp(bp_normal, [0, 0.5],
+    self.pose.cfactor_pitch = interp(bp_normal, [0, 0.5],
                                           [self.settings._POSE_PITCH_THRESHOLD_SLACK,
                                            self.settings._POSE_PITCH_THRESHOLD_STRICT]) / self.settings._POSE_PITCH_THRESHOLD
-    self.pose.cfactor_yaw = np.interp(bp_normal, [0, 0.5],
+    self.pose.cfactor_yaw = interp(bp_normal, [0, 0.5],
                                           [self.settings._POSE_YAW_THRESHOLD_SLACK,
                                            self.settings._POSE_YAW_THRESHOLD_STRICT]) / self.settings._POSE_YAW_THRESHOLD

@@ -1061,13 +1061,6 @@ EVENTS: dict[int, dict[str, Alert | AlertCallbackType]] = {
    ET.NO_ENTRY: NoEntryAlert("Controls Mismatch: Lateral"),
  },

-  EventName.hyundaiRadarTracksConfirmed: {
-    ET.PERMANENT: NormalPermanentAlert("Radar tracks available. Restart the car to initialize")
-  },
-
-  EventName.experimentalModeSwitched: {
-    ET.WARNING: NormalPermanentAlert("Experimental Mode Switched", duration=1.5)
-  }

 }

@@ -24,8 +24,6 @@ from openpilot.selfdrive.controls.lib.latcontrol import MIN_LATERAL_CONTROL_SPEE
 from openpilot.system.version import get_build_metadata

 from openpilot.sunnypilot.mads.mads import ModularAssistiveDrivingSystem
-from openpilot.sunnypilot.selfdrive.car.car_specific import CarSpecificEventsSP
-from openpilot.sunnypilot.selfdrive.car.experimental_switcher import ExperimentalSwitcher

 REPLAY = "REPLAY" in os.environ
 SIMULATION = "SIMULATION" in os.environ
@@ -45,7 +43,7 @@ SafetyModel = car.CarParams.SafetyModel
 IGNORED_SAFETY_MODES = (SafetyModel.silent, SafetyModel.noOutput)


-class SelfdriveD(ExperimentalSwitcher):
+class SelfdriveD:
  def __init__(self, CP=None):
    self.params = Params()

@@ -139,10 +137,6 @@ class SelfdriveD(ExperimentalSwitcher):
    sock_services = list(self.pm.sock.keys()) + ['selfdriveStateSP']
    self.pm = messaging.PubMaster(sock_services)

-    self.car_events_sp = CarSpecificEventsSP(self.CP, self.params)
-
-    ExperimentalSwitcher.__init__(self, self.params)
-
  def update_events(self, CS):
    """Compute onroadEvents from carState"""

@@ -183,9 +177,6 @@ class SelfdriveD(ExperimentalSwitcher):
      car_events = self.car_events.update(CS, self.CS_prev, self.sm['carControl']).to_msg()
      self.events.add_from_msg(car_events)

-      car_events_sp = self.car_events_sp.update().to_msg()
-      self.events.add_from_msg(car_events_sp)
-
      if self.CP.notCar:
        # wait for everything to init first
        if self.sm.frame > int(5. / DT_CTRL) and self.initialized:
@@ -370,18 +361,12 @@ class SelfdriveD(ExperimentalSwitcher):
      if self.sm['modelV2'].frameDropPerc > 20:
        self.events.add(EventName.modeldLagging)

-    # toggle experimental mode once on distance button hold
-    if self.CP.openpilotLongitudinalControl:
-      ExperimentalSwitcher.update(self, CS, self.events, self.experimental_mode)
-
    # decrement personality on distance button press
    if self.CP.openpilotLongitudinalControl:
      if any(not be.pressed and be.type == ButtonType.gapAdjustCruise for be in CS.buttonEvents):
-        if not self.experimental_mode_switched:
-          self.personality = (self.personality - 1) % 3
-          self.params.put_nonblocking('LongitudinalPersonality', str(self.personality))
-          self.events.add(EventName.personalityChanged)
-        self.experimental_mode_switched = False
+        self.personality = (self.personality - 1) % 3
+        self.params.put_nonblocking('LongitudinalPersonality', str(self.personality))
+        self.events.add(EventName.personalityChanged)

  def data_sample(self):
    car_state = messaging.recv_one(self.car_state_sock)
@@ -461,7 +446,6 @@ class SelfdriveD(ExperimentalSwitcher):
    ss.alertStatus = self.AM.current_alert.alert_status
    ss.alertType = self.AM.current_alert.alert_type
    ss.alertSound = self.AM.current_alert.audible_alert
-    ss.alertHudVisual = self.AM.current_alert.visual_alert

    self.pm.send('selfdriveState', ss_msg)

@@ -511,7 +495,6 @@ class SelfdriveD(ExperimentalSwitcher):
      self.personality = self.read_personality_param()

      self.mads.read_params()
-      self.car_events_sp.read_params()
      time.sleep(0.1)

  def run(self):
@@ -121,7 +121,7 @@ class Plant:
    ss.selfdriveState.personality = self.personality
    control.controlsState.forceDecel = self.force_decel
    car_state.carState.vEgo = float(self.speed)
-    car_state.carState.standstill = bool(self.speed < 0.01)
+    car_state.carState.standstill = self.speed < 0.01
    car_state.carState.vCruise = float(v_cruise * 3.6)
    car_control.carControl.orientationNED = [0., float(pitch), 0.]

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--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
DevTekVE	240044c907	Condensing a little	2025-01-08 10:59:56 +01:00
Jason Wen	1b256f6204	remove this for now	2025-01-07 16:18:17 -05:00
Jason Wen	4597350df7	Merge branch 'master-new' into master-new-model-selector-again	2025-01-07 10:37:43 -05:00
Jason Wen	b8a5448148	do something	2025-01-07 10:37:36 -05:00
DevTekVE	6b0b74def1	Add support for detecting 20Hz models during operation This commit introduces functionality to detect if the active model operates at 20Hz. A new helper function `is_active_model_20hz` was added, alongside updates to model initialization and configuration handling. Additionally, a new `is20hz` field was added to the model bundle for improved introspection and performance adaptation.	2025-01-07 09:45:51 +01:00
DevTekVE	7cad216a01	Add support for dynamic frame buffer length in DrivingModelFrame Introduce an `is_20hz` flag to dynamically adjust buffer length and improve flexibility for different frame rates. This ensures better handling of 20Hz configurations by modifying initialization and processing logic. Updated related method signatures and implementation to accommodate the new behavior.	2025-01-07 09:32:50 +01:00
DevTekVE	f45a8f8334	Clean	2025-01-07 09:04:46 +01:00
DevTekVE	a17853e52b	Add support for custom model loading via paths and metadata. Introduced helper functions for retrieving and loading custom models in `run_helpers.py`. Updated model initialization logic in `model_runner.py` to dynamically choose between default and custom models based on environment variables. This enhances flexibility for using alternative model configurations.	2025-01-07 09:04:03 +01:00
DevTekVE	c80021ac48	Refactor model output parsing and cleanup unused code. Added `input_keys` parameter to enhance output parsing logic in `parse_outputs`. Removed commented-out code and unnecessary variable in `modeld.py` for better clarity and maintainability. Simplified import statements in `modeld.py` to reduce redundancy.	2025-01-07 08:13:55 +01:00
DevTekVE	2ce950ac25	i think this might work?	2025-01-07 07:59:13 +01:00