Smooth lead deceleration without damping profile acceleration

This reverts commit 9e68801db0.

.gitmodules

@@ -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/commaai/msgq.git

cereal/custom.capnp

@@ -194,6 +194,7 @@ struct LongitudinalPlanSP @0xf35cc4560bbf6ec2 {
   aTarget @5 :Float32;
   events @6 :List(OnroadEventSP.Event);
   e2eAlerts @7 :E2eAlerts;
+  accelController @8 :AccelController;
 
   struct DynamicExperimentalControl {
     state @0 :DynamicExperimentalControlState;
@@ -296,6 +297,47 @@ struct LongitudinalPlanSP @0xf35cc4560bbf6ec2 {
     greenLightAlert @0 :Bool;
     leadDepartAlert @1 :Bool;
   }
+
+  struct AccelController {
+    enabled @0 :Bool;
+    active @1 :Bool;
+    shadowOnly @2 :Bool;
+    profile @3 :Profile;
+    state @4 :State;
+    vTargetBase @5 :Float32;
+    vTargetRaw @6 :Float32;
+    vTargetFiltered @7 :Float32;
+    vTargetShadow @8 :Float32;
+    leadIndex @9 :Int8 = -1;
+    usableGap @10 :Float32;
+    closingSpeed @11 :Float32;
+    requiredDecel @12 :Float32;
+    aMaxProfile @13 :Float32;
+    aMaxEffective @14 :Float32;
+
+    enum Profile {
+      eco @0;
+      normal @1;
+      sport @2;
+    }
+
+    enum State {
+      inactive @0;
+      free @1;
+      restrict @2;
+      hold @3;
+      release @4;
+      stopHold @5;
+    }
+  }
+
+  # Compatibility type for vehicle integrations that map physical drive modes
+  # onto AccelPersonality. New controller telemetry uses AccelController.Profile.
+  enum AccelerationPersonality {
+    eco @0;
+    normal @1;
+    sport @2;
+  }
 }
 
 struct OnroadEventSP @0xda96579883444c35 {
@@ -342,6 +384,7 @@ struct OnroadEventSP @0xda96579883444c35 {
     speedLimitChanged @21;
     speedLimitPending @22;
     e2eChime @23;
+    laneChangeRoadEdge @24;
   }
 }
 
@@ -448,6 +491,8 @@ struct LiveMapDataSP @0xf416ec09499d9d19 {
 
 struct ModelDataV2SP @0xa1680744031fdb2d {
   laneTurnDirection @0 :TurnDirection;
+  leftLaneChangeEdgeBlock @1 :Bool;
+  rightLaneChangeEdgeBlock @2 :Bool;
 
   enum TurnDirection {
     none @0;

common/params_keys.h

@@ -179,12 +179,19 @@ 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"}},
+
     // MADS params
     {"Mads", {PERSISTENT | BACKUP, BOOL, "1"}},
     {"MadsMainCruiseAllowed", {PERSISTENT | BACKUP, BOOL, "1"}},
@@ -228,6 +235,10 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {
     {"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"}},

common/tests/test_params.py

@@ -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("LiveParameters")
 
     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("LiveParameters") is None
     assert self.params.get("LiveParameters", return_default=True) is None
 

selfdrive/car/card.py

@@ -10,7 +10,7 @@ from cereal import car, log, custom
 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
@@ -121,7 +121,13 @@ 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)

selfdrive/controls/lib/desire_helper.py

@@ -56,7 +56,7 @@ 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
@@ -88,8 +88,8 @@ 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)
 

selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py

@@ -217,6 +217,7 @@ class LongitudinalMpc:
   def __init__(self, dt=DT_MDL):
     self.dt = dt
     self.solver = AcadosOcpSolverCython(MODEL_NAME, ACADOS_SOLVER_TYPE, N)
+    self.last_solution_status = 0
     self.reset()
     self.source = LongitudinalPlanSource.cruise
 
@@ -267,10 +268,11 @@ class LongitudinalMpc:
     for i in range(N):
       self.solver.cost_set(i, 'Zl', Zl)
 
-  def set_weights(self, prev_accel_constraint=True, personality=log.LongitudinalPersonality.standard):
+  def set_weights(self, prev_accel_constraint=True, personality=log.LongitudinalPersonality.standard, *, jerk_cost_multiplier=1.0):
     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,
+                    jerk_factor * J_EGO_COST * jerk_cost_multiplier]
     constraint_cost_weights = [LIMIT_COST, LIMIT_COST, LIMIT_COST, DANGER_ZONE_COST]
     self.set_cost_weights(cost_weights, constraint_cost_weights)
 
@@ -313,7 +315,7 @@ class LongitudinalMpc:
     lead_xv = self.extrapolate_lead(x_lead, v_lead, a_lead, a_lead_tau)
     return lead_xv
 
-  def update(self, radarstate, v_cruise, personality=log.LongitudinalPersonality.standard):
+  def update(self, radarstate, v_cruise, personality=log.LongitudinalPersonality.standard, accel_max=None):
     t_follow = get_T_FOLLOW(personality)
     v_ego = self.x0[1]
     self.status = radarstate.leadOne.status or radarstate.leadTwo.status
@@ -345,6 +347,14 @@ class LongitudinalMpc:
 
     self.params[:,0] = ACCEL_MIN
     self.params[:,1] = ACCEL_MAX
+    if accel_max is not None:
+      try:
+        accel_max_trajectory = np.asarray(accel_max, dtype=float)
+      except (OverflowError, TypeError, ValueError):
+        accel_max_trajectory = np.empty(0)
+      if accel_max_trajectory.shape == (N + 1,) and np.all(np.isfinite(accel_max_trajectory)):
+        self.params[:,1] = np.clip(accel_max_trajectory, 0.0, ACCEL_MAX)
+        self.params[0,1] = max(self.params[0,1], float(np.clip(self.x0[2], ACCEL_MIN, ACCEL_MAX)))
     self.params[:,2] = np.min(x_obstacles, axis=1)
     self.params[:,3] = np.copy(self.a_prev)
     self.params[:,4] = t_follow
@@ -364,6 +374,7 @@ class LongitudinalMpc:
     self.solver.constraints_set(0, "ubx", self.x0)
 
     self.solution_status = self.solver.solve()
+    self.last_solution_status = self.solution_status
     self.solve_time = float(self.solver.get_stats('time_tot')[0])
     self.time_qp_solution = float(self.solver.get_stats('time_qp')[0])
     self.time_linearization = float(self.solver.get_stats('time_lin')[0])

selfdrive/controls/lib/longitudinal_planner.py

@@ -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
@@ -129,16 +129,18 @@ 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)
+    base_v_cruise = v_cruise
 
     if force_slow_decel:
       v_cruise = 0.0
 
-    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)
+    is_e2e = LongitudinalPlannerSP.update_accel_controller_mpc(
+      self, sm, base_v_cruise, v_cruise, prev_accel_constraint, reset_state=reset_state,
+      cruise_initialized=v_cruise_initialized, available_accel_max=accel_clip[1] if self.allow_throttle else 0.0,
+      previous_should_stop=self.output_should_stop, force_decel=force_slow_decel,
+    )
 
     self.v_desired_trajectory = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC, self.mpc.v_solution)
     self.a_desired_trajectory = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC, self.mpc.a_solution)
@@ -154,13 +156,14 @@ 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
-    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, vEgoStopping=self.CP.vEgoStopping)
+    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, vEgoStopping=self.CP.vEgoStopping,
+    )
     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:
@@ -168,6 +171,7 @@ class LongitudinalPlanner(LongitudinalPlannerSP):
     else:
       output_a_target = output_a_target_mpc
       self.output_should_stop = output_should_stop_mpc
+    self.output_should_stop = self.accel_controller_should_stop(self.output_should_stop, is_e2e)
 
     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)

selfdrive/selfdrived/selfdrived.py

@@ -321,9 +321,16 @@ 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)

selfdrive/test/longitudinal_maneuvers/plant.py

@@ -1,5 +1,11 @@
 #!/usr/bin/env python3
+from collections import deque
+from collections.abc import Callable
+from dataclasses import dataclass
+import math
 import time
+from typing import Any
+
 import numpy as np
 
 from cereal import log
@@ -11,12 +17,105 @@ from openpilot.selfdrive.controls.lib.longitudinal_planner import LongitudinalPl
 from openpilot.selfdrive.controls.radard import _LEAD_ACCEL_TAU
 
 
+LeadObservation = dict[str, Any]
+LeadObservationFn = Callable[[float, str, LeadObservation], LeadObservation | None]
+ModelActionFn = Callable[[float, float, float], tuple[float, bool]]
+EgoObservationFn = Callable[[float, float, float], tuple[float, float]]
+
+
+@dataclass(frozen=True)
+class ActuatorModel:
+  planner_delay: float
+  transport_delay: float
+  actuator_lag: float
+  command_rate_limit: float
+  stopping_acceleration: float
+  standstill_breakaway_acceleration: float
+  standstill_breakaway_time: float
+
+  def __post_init__(self):
+    nonnegative_fields = {
+      "planner_delay": self.planner_delay,
+      "transport_delay": self.transport_delay,
+      "actuator_lag": self.actuator_lag,
+      "standstill_breakaway_acceleration": self.standstill_breakaway_acceleration,
+      "standstill_breakaway_time": self.standstill_breakaway_time,
+    }
+    if any(not math.isfinite(value) or value < 0.0 for value in nonnegative_fields.values()):
+      raise ValueError(f"ActuatorModel fields must be finite and non-negative: {nonnegative_fields}")
+    if not math.isfinite(self.command_rate_limit) or self.command_rate_limit <= 0.0:
+      raise ValueError("command_rate_limit must be finite and positive")
+    if not math.isfinite(self.stopping_acceleration) or self.stopping_acceleration > 0.0:
+      raise ValueError("stopping_acceleration must be finite and non-positive")
+
+
+# Route-derived conservative Prius TSS2 stress model for the acceleration-controller
+# regression suite. The 1.0 m/s² gate represents prompt takeoffs, not a universal
+# physical threshold: the supplied routes also contain low-command creep departures.
+# This models vehicle response only and does not emulate Toyota's CAN controller.
+PRIUS_TSS2_ROUTE_MODEL = ActuatorModel(
+  planner_delay=0.05,
+  transport_delay=0.0,
+  actuator_lag=0.20,
+  command_rate_limit=4.0,
+  stopping_acceleration=-2.0,
+  standstill_breakaway_acceleration=1.0,
+  standstill_breakaway_time=0.05,
+)
+
+
 class Plant:
   messaging_initialized = False
 
-  def __init__(self, lead_relevancy=False, speed=0.0, distance_lead=2.0,
-               enabled=True, only_lead2=False, only_radar=False, e2e=False, personality=0, force_decel=False):
-    self.rate = 1. / DT_MDL
+  def __init__(
+    self,
+    lead_relevancy=False,
+    speed=0.0,
+    distance_lead=2.0,
+    enabled=True,
+    only_lead2=False,
+    only_radar=False,
+    e2e=False,
+    personality=0,
+    force_decel=False,
+    lead_observation_fn: LeadObservationFn | None = None,
+    model_action_fn: ModelActionFn | None = None,
+    ego_observation_fn: EgoObservationFn | None = None,
+    actuator_delay: float | None = None,
+    actuator_lag: float = 0.0,
+    actuator_model: ActuatorModel | None = None,
+  ):
+    """Closed-loop longitudinal planner plant.
+
+    ``lead_observation_fn(time, lead_name, truth)`` may return a complete or partial
+    observed LeadData mapping, or ``None`` for an absent lead. It is called separately
+    for ``leadOne`` and ``leadTwo``. The supplied truth mapping is a copy, and observed
+    values never affect the physical lead trajectory.
+
+    ``model_action_fn(time, v_ego, a_ego)`` returns
+    ``(desired_acceleration, should_stop)``.
+
+    ``ego_observation_fn(time, true_v_ego, true_a_ego)`` returns the observed
+    ``(v_ego, a_ego)`` published in ``carState``. It can inject measurement noise
+    without changing the physical plant state.
+
+    Passing ``actuator_delay`` both overrides ``CP.longitudinalActuatorDelay`` and
+    adds the corresponding command transport delay to the plant. ``None`` keeps the
+    historical Honda planner delay with instantaneous plant response. ``actuator_lag``
+    is an optional first-order acceleration-response time constant. Both defaults keep
+    historical plant dynamics unchanged.
+
+    ``actuator_model`` opts into a staged vehicle-response model. Its planner delay
+    is used by MPC, while its independent transport delay is used by the command
+    queue before rate limiting, standstill breakaway confirmation, and first-order
+    lag. Leaving it unset preserves the historical actuator path.
+    """
+    if actuator_delay is not None and (not math.isfinite(actuator_delay) or actuator_delay < 0.0):
+      raise ValueError("actuator_delay must be finite and non-negative")
+    if not math.isfinite(actuator_lag) or actuator_lag < 0.0:
+      raise ValueError("actuator_lag must be finite and non-negative")
+
+    self.rate = 1.0 / DT_MDL
 
     if not Plant.messaging_initialized:
       Plant.radar = messaging.pub_sock('radarState')
@@ -28,10 +127,15 @@ class Plant:
 
     self.v_lead_prev = 0.0
 
-    self.distance = 0.
+    self.distance = 0.0
     self.speed = speed
     self.should_stop = False
     self.acceleration = 0.0
+    self.a_target = 0.0
+    self.actuator_command = 0.0
+    self.applied_actuator_command = 0.0
+    self.breakaway_confirmed = False
+    self._breakaway_timer = 0.0
 
     # lead car
     self.lead_relevancy = lead_relevancy
@@ -42,9 +146,18 @@ class Plant:
     self.e2e = e2e
     self.personality = personality
     self.force_decel = force_decel
+    self.lead_observation_fn = lead_observation_fn
+    self.model_action_fn = model_action_fn
+    self.ego_observation_fn = ego_observation_fn
+    self.actuator_model = actuator_model
+    self.actuator_delay = actuator_model.planner_delay if actuator_model is not None else actuator_delay
+    self.transport_delay = actuator_model.transport_delay if actuator_model is not None else actuator_delay
+    self.actuator_lag = actuator_model.actuator_lag if actuator_model is not None else actuator_lag
+    self.publish_realized_a_ego = any((lead_observation_fn is not None, model_action_fn is not None, ego_observation_fn is not None,
+                                      actuator_delay is not None, actuator_lag > 0.0, actuator_model is not None))
 
     self.rk = Ratekeeper(self.rate, print_delay_threshold=100.0)
-    self.ts = 1. / self.rate
+    self.ts = 1.0 / self.rate
     time.sleep(0.1)
     self.sm = messaging.SubMaster(['longitudinalPlan'])
 
@@ -52,14 +165,86 @@ class Plant:
     from opendbc.car.honda.interface import CarInterface
 
     CP = CarInterface.get_non_essential_params(CAR.HONDA_CIVIC)
+    if self.actuator_delay is not None:
+      CP.longitudinalActuatorDelay = self.actuator_delay
     CP_SP = CarInterface.get_non_essential_params_sp(CP, CAR.HONDA_CIVIC)
     self.planner = LongitudinalPlanner(CP, CP_SP, init_v=self.speed)
 
+    if self.actuator_model is not None and self.speed >= 0.01:
+      self.breakaway_confirmed = True
+    delay_steps = 0 if self.transport_delay is None else round(self.transport_delay / self.ts)
+    self._actuator_delay_queue = deque([self.acceleration] * delay_steps)
+
   @property
   def current_time(self):
     return float(self.rk.frame) / self.rate
 
-  def step(self, v_lead=0.0, prob_lead=1.0, v_cruise=50., pitch=0.0, prob_throttle=1.0):
+  @staticmethod
+  def _lead_message(observation: LeadObservation):
+    lead = log.RadarState.LeadData.new_message()
+    for field, value in observation.items():
+      setattr(lead, field, value)
+    return lead
+
+  def _observe_lead(self, lead_name: str, truth: LeadObservation, present_by_default: bool) -> LeadObservation | None:
+    if self.lead_observation_fn is None:
+      return dict(truth) if present_by_default else None
+
+    observed = self.lead_observation_fn(self.current_time, lead_name, dict(truth))
+    if observed is None:
+      return None
+
+    # Partial overrides are convenient for individual sensor glitches, while copying
+    # from truth ensures every field written to cereal is deterministic.
+    complete_observation = dict(truth)
+    complete_observation.update(observed)
+    return complete_observation
+
+  def _update_actuator(self, command: float) -> tuple[float, float]:
+    if self._actuator_delay_queue:
+      self._actuator_delay_queue.append(command)
+      delayed_command = self._actuator_delay_queue.popleft()
+    else:
+      delayed_command = command
+
+    if self.actuator_model is not None:
+      max_command_delta = self.actuator_model.command_rate_limit * self.ts
+      self.applied_actuator_command = float(np.clip(delayed_command,
+                                                   self.applied_actuator_command - max_command_delta,
+                                                   self.applied_actuator_command + max_command_delta))
+
+      if self.speed < 0.01:
+        if self.applied_actuator_command <= 0.0:
+          self.breakaway_confirmed = False
+          self._breakaway_timer = 0.0
+        elif not self.breakaway_confirmed:
+          breakaway_ready = self.applied_actuator_command + 1e-9 >= self.actuator_model.standstill_breakaway_acceleration
+          if breakaway_ready:
+            self._breakaway_timer += self.ts
+          else:
+            self._breakaway_timer = 0.0
+
+          self.breakaway_confirmed = breakaway_ready and self._breakaway_timer + 1e-9 >= self.actuator_model.standstill_breakaway_time
+        if not self.breakaway_confirmed:
+          self.acceleration = 0.0
+          return delayed_command, self.acceleration
+      else:
+        self.breakaway_confirmed = True
+
+      response_command = self.applied_actuator_command
+    else:
+      # Preserve the historical response path exactly when no staged model is used.
+      self.applied_actuator_command = delayed_command
+      response_command = delayed_command
+
+    if self.actuator_lag > 0.0:
+      alpha = 1.0 - math.exp(-self.ts / self.actuator_lag)
+      self.acceleration += alpha * (response_command - self.acceleration)
+    else:
+      self.acceleration = response_command
+    return delayed_command, self.acceleration
+
+  def step(self, v_lead=0.0, prob_lead=1.0, v_cruise=50.0, pitch=0.0, prob_throttle=1.0):
     # ******** publish a fake model going straight and fake calibration ********
     # note that this is worst case for MPC, since model will delay long mpc by one time step
     radar = messaging.new_message('radarState')
@@ -72,39 +257,48 @@ class Plant:
     car_state_sp = messaging.new_message('carStateSP')
     live_map_data_sp = messaging.new_message('liveMapDataSP')
     gps_data = messaging.new_message('gpsLocation')
-    a_lead = (v_lead - self.v_lead_prev)/self.ts
+    a_lead = (v_lead - self.v_lead_prev) / self.ts
     self.v_lead_prev = v_lead
 
     if self.lead_relevancy:
-      d_rel = np.maximum(0., self.distance_lead - self.distance)
+      d_rel = np.maximum(0.0, self.distance_lead - self.distance)
       v_rel = v_lead - self.speed
       if self.only_radar:
         status = True
-      elif prob_lead > .5:
+      elif prob_lead > 0.5:
         status = True
       else:
         status = False
     else:
-      d_rel = 200.
-      v_rel = 0.
+      d_rel = 200.0
+      v_rel = 0.0
       prob_lead = 0.0
       status = False
 
-    lead = log.RadarState.LeadData.new_message()
-    lead.dRel = float(d_rel)
-    lead.yRel = 0.0
-    lead.vRel = float(v_rel)
-    lead.aRel = float(a_lead - self.acceleration)
-    lead.vLead = float(v_lead)
-    lead.vLeadK = float(v_lead)
-    lead.aLeadK = float(a_lead)
-    # TODO use real radard logic for this
-    lead.aLeadTau = float(_LEAD_ACCEL_TAU)
-    lead.status = status
-    lead.modelProb = float(prob_lead)
-    if not self.only_lead2:
-      radar.radarState.leadOne = lead
-    radar.radarState.leadTwo = lead
+    truth_lead: LeadObservation = {
+      "dRel": float(d_rel),
+      "yRel": 0.0,
+      "vRel": float(v_rel),
+      "aRel": float(a_lead - self.acceleration),
+      "vLead": float(v_lead),
+      "dPath": 0.0,
+      "vLat": 0.0,
+      "vLeadK": float(v_lead),
+      "aLeadK": float(a_lead),
+      "fcw": False,
+      "status": bool(status),
+      # TODO use real radard logic for this
+      "aLeadTau": float(_LEAD_ACCEL_TAU),
+      "modelProb": float(prob_lead),
+      "radar": bool(self.only_radar),
+      "radarTrackId": -1,
+    }
+    lead_one_observation = self._observe_lead("leadOne", truth_lead, not self.only_lead2)
+    lead_two_observation = self._observe_lead("leadTwo", truth_lead, True)
+    if lead_one_observation is not None:
+      radar.radarState.leadOne = self._lead_message(lead_one_observation)
+    if lead_two_observation is not None:
+      radar.radarState.leadTwo = self._lead_message(lead_two_observation)
 
     # Simulate model predicting slightly faster speed
     # this is to ensure lead policy is effective when model
@@ -112,10 +306,15 @@ class Plant:
     position = log.XYZTData.new_message()
     position.x = [float(x) for x in (self.speed + 0.5) * np.array(ModelConstants.T_IDXS)]
     model.modelV2.position = position
-    model.modelV2.action.desiredAcceleration = float(self.acceleration + 0.1)
+    if self.model_action_fn is None:
+      model_acceleration, model_should_stop = self.acceleration + 0.1, False
+    else:
+      model_acceleration, model_should_stop = self.model_action_fn(self.current_time, self.speed, self.acceleration)
+    model.modelV2.action.desiredAcceleration = float(model_acceleration)
+    model.modelV2.action.shouldStop = bool(model_should_stop)
     velocity = log.XYZTData.new_message()
     velocity.x = [float(x) for x in (self.speed + 0.5) * np.ones_like(ModelConstants.T_IDXS)]
-    velocity.x[0] = float(self.speed) # always start at current speed
+    velocity.x[0] = float(self.speed)  # always start at current speed
     model.modelV2.velocity = velocity
     acceleration = log.XYZTData.new_message()
     acceleration.x = [float(x) for x in np.zeros_like(ModelConstants.T_IDXS)]
@@ -126,33 +325,45 @@ class Plant:
     ss.selfdriveState.experimentalMode = self.e2e
     ss.selfdriveState.personality = self.personality
     control.controlsState.forceDecel = self.force_decel
-    car_state.carState.vEgo = float(self.speed)
+    true_v_ego = self.speed
+    true_a_ego = self.acceleration
+    published_v_ego = true_v_ego
+    published_a_ego = true_a_ego if self.publish_realized_a_ego else 0.0
+    if self.ego_observation_fn is not None:
+      published_v_ego, published_a_ego = self.ego_observation_fn(self.current_time, true_v_ego, true_a_ego)
+    car_state.carState.vEgo = float(published_v_ego)
+    car_state.carState.aEgo = float(published_a_ego)
     car_state.carState.standstill = bool(self.speed < 0.01)
     car_state.carState.vCruise = float(v_cruise * 3.6)
-    car_control.carControl.orientationNED = [0., float(pitch), 0.]
+    car_control.carControl.orientationNED = [0.0, float(pitch), 0.0]
 
     # ******** get controlsState messages for plotting ***
-    sm = {'radarState': radar.radarState,
-          'carState': car_state.carState,
-          'carControl': car_control.carControl,
-          'controlsState': control.controlsState,
-          'selfdriveState': ss.selfdriveState,
-          'liveParameters': lp.liveParameters,
-          'modelV2': model.modelV2,
-          'carStateSP': car_state_sp.carStateSP,
-          'liveMapDataSP': live_map_data_sp.liveMapDataSP,
-          'gpsLocation': gps_data.gpsLocation}
+    sm = {
+      'radarState': radar.radarState,
+      'carState': car_state.carState,
+      'carControl': car_control.carControl,
+      'controlsState': control.controlsState,
+      'selfdriveState': ss.selfdriveState,
+      'liveParameters': lp.liveParameters,
+      'modelV2': model.modelV2,
+      'carStateSP': car_state_sp.carStateSP,
+      'liveMapDataSP': live_map_data_sp.liveMapDataSP,
+      'gpsLocation': gps_data.gpsLocation,
+    }
     self.planner.update(sm)
-    self.acceleration = self.planner.output_a_target
+    self.a_target = self.planner.output_a_target
+    self.actuator_command = self.a_target
     if self.planner.output_should_stop:
-      self.acceleration = min(-0.5, self.acceleration)
+      stopping_acceleration = -0.5 if self.actuator_model is None else self.actuator_model.stopping_acceleration
+      self.actuator_command = min(stopping_acceleration, self.actuator_command)
+    delayed_actuator_command, _ = self._update_actuator(self.actuator_command)
     self.speed = self.speed + self.acceleration * self.ts
     self.should_stop = self.planner.output_should_stop
     fcw = self.planner.fcw
     self.distance_lead = self.distance_lead + v_lead * self.ts
 
     # ******** run the car ********
-    #print(self.distance, speed)
+    # print(self.distance, speed)
     if self.speed <= 0:
       self.speed = 0
       self.acceleration = 0
@@ -160,30 +371,64 @@ class Plant:
 
     # *** radar model ***
     if self.lead_relevancy:
-      d_rel = np.maximum(0., self.distance_lead - self.distance)
+      d_rel = np.maximum(0.0, self.distance_lead - self.distance)
       v_rel = v_lead - self.speed
     else:
-      d_rel = 200.
-      v_rel = 0.
+      d_rel = 200.0
+      v_rel = 0.0
 
     # print at 5hz
     # if (self.rk.frame % (self.rate // 5)) == 0:
     #   print("%2.2f sec   %6.2f m  %6.2f m/s  %6.2f m/s2   lead_rel: %6.2f m  %6.2f m/s"
     #         % (self.current_time, self.distance, self.speed, self.acceleration, d_rel, v_rel))
 
-
     # ******** update prevs ********
     self.rk.monitor_time()
 
+    accel_controller_result = getattr(self.planner, "accel_controller_result", None)
     return {
       "distance": self.distance,
       "speed": self.speed,
       "acceleration": self.acceleration,
+      "realized_acceleration": self.acceleration,
+      "a_target": self.a_target,
+      "planner_acceleration": self.a_target,
+      "actuator_command": self.actuator_command,
+      "stop_clamped_actuator_command": self.actuator_command,
+      "delayed_actuator_command": delayed_actuator_command,
+      "applied_actuator_command": self.applied_actuator_command,
+      "vehicle_actuator_command": self.applied_actuator_command,
+      "true_v_ego": true_v_ego,
+      "true_a_ego": true_a_ego,
+      "published_a_ego": published_a_ego,
+      "published_v_ego": published_v_ego,
+      "observed_a_ego": published_a_ego,
+      "observed_v_ego": published_v_ego,
+      "planner_delay": self.actuator_delay,
+      "transport_delay": self.transport_delay,
+      "breakaway_confirmed": self.breakaway_confirmed,
+      "breakaway_time": self._breakaway_timer,
       "should_stop": self.should_stop,
       "distance_lead": self.distance_lead,
       "fcw": fcw,
+      "mpc_source": self.planner.mpc.source,
+      "dec_mode": self.planner.dec.mode(),
+      "pace_cap": getattr(accel_controller_result, "target_speed", None),
+      "base_target": getattr(accel_controller_result, "base_speed", None),
+      "raw_energy_cap": getattr(accel_controller_result, "raw_energy_cap", None),
+      "live_filtered_cap": getattr(accel_controller_result, "live_filtered_cap", None),
+      "shadow_filtered_cap": getattr(accel_controller_result, "shadow_filtered_cap", None),
+      "accel_controller_selected_lead": getattr(accel_controller_result, "selected_lead", None),
+      "model_action": {
+        "desiredAcceleration": float(model_acceleration),
+        "shouldStop": bool(model_should_stop),
+      },
+      "truth_lead": dict(truth_lead),
+      "lead_one_observation": None if lead_one_observation is None else dict(lead_one_observation),
+      "lead_two_observation": None if lead_two_observation is None else dict(lead_two_observation),
     }
 
+
 # simple engage in standalone mode
 def plant_thread():
   plant = Plant()

selfdrive/test/longitudinal_maneuvers/test_plant.py

@@ -0,0 +1,80 @@
+import math
+
+import pytest
+
+from openpilot.common.realtime import DT_MDL
+from openpilot.selfdrive.test.longitudinal_maneuvers.plant import Plant
+
+
+def test_full_lead_observation_is_independent_from_truth():
+  callback_inputs = []
+
+  def observe_lead(current_time, lead_name, truth):
+    callback_inputs.append((current_time, lead_name, truth))
+    if lead_name == "leadOne":
+      return {
+        "dRel": 12.5,
+        "vRel": -4.0,
+        "vLead": 6.0,
+        "vLeadK": 5.5,
+        "aLeadK": -1.25,
+        "aLeadTau": 0.7,
+        "status": True,
+        "modelProb": 0.9,
+        "radarTrackId": 42,
+      }
+    return None
+
+  plant = Plant(lead_relevancy=True, speed=10.0, distance_lead=50.0, lead_observation_fn=observe_lead)
+  result = plant.step(v_lead=8.0)
+
+  assert [entry[1] for entry in callback_inputs] == ["leadOne", "leadTwo"]
+  assert callback_inputs[0][2]["dRel"] == pytest.approx(50.0)
+  assert result["truth_lead"]["dRel"] == pytest.approx(50.0)
+  assert result["lead_one_observation"]["dRel"] == pytest.approx(12.5)
+  assert result["lead_one_observation"]["radarTrackId"] == 42
+  assert result["lead_two_observation"] is None
+  assert result["distance_lead"] == pytest.approx(50.0 + 8.0 * DT_MDL)
+
+
+def test_model_action_realized_acceleration_and_source_logging():
+  def model_action(current_time, v_ego, a_ego):
+    return -1.25, True
+
+  plant = Plant(speed=10.0, e2e=True, force_decel=True, model_action_fn=model_action, actuator_lag=0.5)
+  first = plant.step()
+  second = plant.step()
+
+  assert first["model_action"] == {"desiredAcceleration": -1.25, "shouldStop": True}
+  assert first["published_a_ego"] == pytest.approx(0.0)
+  assert second["published_a_ego"] == pytest.approx(first["realized_acceleration"])
+  assert first["acceleration"] == first["realized_acceleration"]
+  assert abs(first["realized_acceleration"]) < abs(first["actuator_command"])
+  assert first["mpc_source"] is not None
+  assert first["dec_mode"] in ("acc", "blended")
+  assert "pace_cap" in first
+  assert "raw_energy_cap" in first
+  assert "live_filtered_cap" in first
+  assert first["lead_one_observation"] is not None
+  assert first["truth_lead"] == first["lead_one_observation"]
+
+
+def test_configurable_transport_delay_and_first_order_lag():
+  plant = Plant(speed=10.0, actuator_delay=2 * DT_MDL, actuator_lag=0.2)
+
+  assert plant.planner.CP.longitudinalActuatorDelay == pytest.approx(2 * DT_MDL)
+  delayed_commands = [plant._update_actuator(-1.0) for _ in range(3)]
+  assert [command for command, _ in delayed_commands[:2]] == [0.0, 0.0]
+
+  expected_acceleration = -(1.0 - math.exp(-DT_MDL / 0.2))
+  assert delayed_commands[2][0] == -1.0
+  assert delayed_commands[2][1] == pytest.approx(expected_acceleration)
+
+
+@pytest.mark.parametrize(
+  ("delay", "lag"),
+  [(-0.1, 0.0), (float("nan"), 0.0), (float("inf"), 0.0), (None, -0.1), (None, float("nan")), (None, float("inf"))],
+)
+def test_invalid_actuator_dynamics(delay, lag):
+  with pytest.raises(ValueError):
+    Plant(actuator_delay=delay, actuator_lag=lag)

selfdrive/ui/layouts/settings/toggles.py

@@ -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)

selfdrive/ui/mici/layouts/main.py

@@ -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):

selfdrive/ui/mici/layouts/settings/toggles.py

@@ -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()

selfdrive/ui/mici/widgets/button.py

@@ -382,13 +382,18 @@ 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):

selfdrive/ui/sunnypilot/layouts/settings/steering_sub_layouts/lane_change_settings.py

@@ -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

selfdrive/ui/sunnypilot/mici/layouts/main.py

@@ -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()

selfdrive/ui/sunnypilot/mici/layouts/onroad.py

@@ -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)

selfdrive/ui/sunnypilot/mici/layouts/onroad_info_panel.py

@@ -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")

selfdrive/ui/sunnypilot/mici/onroad/augmented_road_view.py

@@ -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)

selfdrive/ui/sunnypilot/mici/widgets/scroll_panel_sp.py

@@ -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()

selfdrive/ui/sunnypilot/mici/widgets/scroller_sp.py

@@ -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)

selfdrive/ui/ui.py

@@ -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()
 
 

sunnypilot/modeld_v2/modeld.py

@@ -40,6 +40,7 @@ from openpilot.sunnypilot.modeld_v2.camera_offset_helper import CameraOffsetHelp
 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 = "selfdrive.modeld.modeld_tinygrad"
 
@@ -329,6 +330,7 @@ def main(demo=False):
   prev_action = log.ModelDataV2.Action()
 
   DH = DesireHelper()
+  RELC = RoadEdgeLaneChangeController(DH)
   meta_constants = load_meta_constants()
 
   while True:
@@ -433,7 +435,10 @@ 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

sunnypilot/selfdrive/car/interfaces.py

@@ -129,6 +129,7 @@ def initialize_params(params) -> list[dict[str, Any]]:
   keys.extend([
     "ToyotaEnforceStockLongitudinal",
     "ToyotaStopAndGoHack",
+    "ToyotaEnhancedBsm",
   ])
 
   return [{k: params.get(k, return_default=True)} for k in keys]

sunnypilot/selfdrive/controls/lib/accel_personality/__init__.py

@@ -0,0 +1,8 @@
+from openpilot.sunnypilot.selfdrive.controls.lib.accel_personality.accel_controller import (
+  AccelController,
+  AccelControllerResult,
+  AccelControllerState,
+)
+from openpilot.sunnypilot.selfdrive.controls.lib.accel_personality.constants import AccelProfile
+
+__all__ = ["AccelController", "AccelControllerResult", "AccelControllerState", "AccelProfile"]

sunnypilot/selfdrive/controls/lib/accel_personality/accel_controller.py

@@ -0,0 +1,683 @@
+#!/usr/bin/env python3
+from collections import deque
+from dataclasses import dataclass, field
+from enum import IntEnum
+import math
+
+import numpy as np
+
+from cereal import log
+from opendbc.car.interfaces import ACCEL_MIN, ACCEL_MAX
+from openpilot.common.realtime import DT_MDL
+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_personality.constants import (
+  ACCEL_LIMIT_HORIZON_JERK, ACCEL_PROFILE_MAX_BP, ACCEL_PROFILE_MAX_V, BRAKING_ACCEL_LIMIT_THRESHOLD, CAP_FILTER_FRAMES,
+  LAUNCH_END_SPEED, LAUNCH_TARGET_HEADROOM, LAUNCH_TARGET_SLEW, LEAD_LOSS_HOLD_TIME, LEAD_MATCH_ACCEL_SLEW,
+  LEAD_MATCH_GAP_GAIN, LEAD_MATCH_SPEED_HEADROOM, LEAD_MATCH_TAPER_GAIN, MATCHED_PACE_DECEL_RATE, MAX_LEAD_ACCEL_TAU,
+  MIN_LEAD_SPEED, PACE_RELIEF_DEADBAND, PACE_TARGET_ARM_MARGIN, PACE_TARGET_RESERVE,
+  PACE_RESTRICT_DEADBAND, PROFILE_CONFIGS, RADAR_STALE_TIMEOUT, STOP_GAP_RESERVE, STOP_GAP_RESERVE_DECEL_BP,
+  STOP_GAP_RESERVE_LEAD_SPEED,
+  STOP_HOLD_CREEP_DISTANCE, STOP_HOLD_CREEP_SPEED, STOP_HOLD_EGO_SPEED, STOP_HOLD_EXIT_FRAMES, STOP_HOLD_EXIT_SPEED,
+  STOP_HOLD_MAX_LEAD_DISTANCE, STOPPED_LEAD_SPEED, VEGO_NOISE_TOLERANCE, AccelProfile,
+)
+
+
+class AccelControllerState(IntEnum):
+  inactive = 0
+  free = 1
+  restrict = 2
+  hold = 3
+  release = 4
+  stopHold = 5
+
+
+@dataclass(frozen=True)
+class EnergyEnvelope:
+  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_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
+
+
+@dataclass(frozen=True)
+class AccelControllerResult:
+  target_speed: float
+  enabled: bool
+  active: bool
+  shadow_active: bool
+  launching: bool
+  departure_launching: bool
+  profile: AccelProfile
+  profile_accel_max: float
+  positive_accel_max: float
+  effective_accel_max: float
+  mpc_accel_max: tuple[float, ...] | None
+  state: AccelControllerState
+  shadow_state: AccelControllerState
+  base_speed: float
+  raw_energy_cap: float
+  live_filtered_cap: float
+  shadow_filtered_cap: float
+  selected_lead: int
+  selected_lead_speed: float
+  usable_gap: float
+  closing_speed: float
+  required_decel: float
+
+
+@dataclass
+class _ControllerPath:
+  cap_samples: deque[float] = field(default_factory=lambda: deque([math.inf] * CAP_FILTER_FRAMES, maxlen=CAP_FILTER_FRAMES))
+  lead_speed_samples: deque[float] = field(default_factory=lambda: deque([math.inf] * CAP_FILTER_FRAMES, maxlen=CAP_FILTER_FRAMES))
+  lead_accel_samples: deque[float] = field(default_factory=lambda: deque([0.0] * CAP_FILTER_FRAMES, maxlen=CAP_FILTER_FRAMES))
+  departure_samples: tuple[deque[float], deque[float]] = field(
+    default_factory=lambda: (deque(maxlen=CAP_FILTER_FRAMES), deque(maxlen=CAP_FILTER_FRAMES)),
+  )
+  departure_references: list[float | None] = field(default_factory=lambda: [None, None])
+  pace: float | None = None
+  state: AccelControllerState = AccelControllerState.inactive
+  departure_frames: int = 0
+  active_frames: int = 0
+  lead_loss_frames: int = 0
+  lead_switch_guard_frames: int = 0
+  selected_lead: int = -1
+  selected_lead_track_id: int = -1
+  stale_frames: int = 0
+  launching: bool = False
+  departure_launch: bool = False
+  matched_lead: bool = False
+  braking_limited: bool = False
+  braking_handoff: bool = False
+  pace_reserve_armed: bool = False
+  matched_accel_limit: float | None = None
+
+  @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 robust_departure_separation(self, lead_index: int) -> float:
+    samples = self.departure_samples[lead_index]
+    return float(np.median(samples)) if samples else -math.inf
+
+  def reset(self) -> None:
+    self.cap_samples = deque([math.inf] * CAP_FILTER_FRAMES, maxlen=CAP_FILTER_FRAMES)
+    self.lead_speed_samples = deque([math.inf] * CAP_FILTER_FRAMES, maxlen=CAP_FILTER_FRAMES)
+    self.lead_accel_samples = deque([0.0] * CAP_FILTER_FRAMES, maxlen=CAP_FILTER_FRAMES)
+    self.departure_samples = (deque(maxlen=CAP_FILTER_FRAMES), deque(maxlen=CAP_FILTER_FRAMES))
+    self.departure_references = [None, None]
+    self.pace = None
+    self.state = AccelControllerState.inactive
+    self.departure_frames = 0
+    self.active_frames = 0
+    self.lead_loss_frames = 0
+    self.lead_switch_guard_frames = 0
+    self.selected_lead = -1
+    self.selected_lead_track_id = -1
+    self.stale_frames = 0
+    self.launching = False
+    self.departure_launch = False
+    self.matched_lead = False
+    self.braking_limited = False
+    self.braking_handoff = False
+    self.pace_reserve_armed = False
+    self.matched_accel_limit = None
+
+
+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.CP = CP
+    self.dt = dt
+    self.lead_loss_hold_frames = max(CAP_FILTER_FRAMES, math.ceil(LEAD_LOSS_HOLD_TIME / dt))
+    self.radar_stale_frames = max(1, math.ceil(RADAR_STALE_TIMEOUT / dt))
+    self.live = _ControllerPath()
+    self.shadow = _ControllerPath()
+    self._held_envelope: EnergyEnvelope | None = None
+
+  @staticmethod
+  def _profile(profile: int | AccelProfile) -> AccelProfile:
+    try:
+      return AccelProfile(profile)
+    except (TypeError, ValueError):
+      return AccelProfile.normal
+
+  @classmethod
+  def get_profile_accel_max(cls, profile: int | AccelProfile, v_ego: float) -> float:
+    if not math.isfinite(v_ego):
+      return math.nan
+    selected_profile = cls._profile(profile)
+    return float(np.interp(max(v_ego, 0.0), ACCEL_PROFILE_MAX_BP, ACCEL_PROFILE_MAX_V[selected_profile]))
+
+  def _delay(self) -> float:
+    try:
+      return float(self.CP.longitudinalActuatorDelay) + DT_MDL
+    except (AttributeError, OverflowError, TypeError, ValueError):
+      return math.nan
+
+  @staticmethod
+  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)
+
+  @staticmethod
+  def _lead_values(lead) -> tuple[float, float, float, float] | None:
+    try:
+      if not lead.status:
+        return None
+      d_rel, v_lead = float(lead.dRel), float(lead.vLeadK)
+    except (AttributeError, OverflowError, TypeError, ValueError):
+      return None
+    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
+
+    try:
+      a_lead = float(lead.aLeadK)
+    except (AttributeError, OverflowError, TypeError, ValueError):
+      a_lead = 0.0
+    if not math.isfinite(a_lead):
+      a_lead = 0.0
+
+    try:
+      a_lead_tau = float(lead.aLeadTau)
+    except (AttributeError, OverflowError, TypeError, ValueError):
+      a_lead_tau = _LEAD_ACCEL_TAU
+    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
+
+  @staticmethod
+  def _lead_track_id(lead) -> int:
+    try:
+      return max(int(lead.radarTrackId), -1)
+    except (AttributeError, OverflowError, TypeError, ValueError):
+      return -1
+
+  def calculate_energy_envelope(self, radar_state, v_ego: float, a_ego: float, profile: int | AccelProfile,
+                                follow_personality=log.LongitudinalPersonality.standard) -> EnergyEnvelope:
+    delay = self._delay()
+    if not all(math.isfinite(value) for value in (v_ego, a_ego, delay)) or v_ego < 0.0 or delay < 0.0:
+      return EnergyEnvelope()
+
+    try:
+      leads = (radar_state.leadOne, radar_state.leadTwo)
+      lead_status = any(bool(lead.status) for lead in leads)
+    except (AttributeError, TypeError, ValueError):
+      return EnergyEnvelope()
+
+    try:
+      t_follow = get_T_FOLLOW(follow_personality)
+    except (NotImplementedError, TypeError, ValueError):
+      t_follow = get_T_FOLLOW(log.LongitudinalPersonality.standard)
+    if not math.isfinite(t_follow) or t_follow < 0.0:
+      return EnergyEnvelope(lead_status=lead_status)
+
+    x_ego, v_ego_delay = self._project_ego(v_ego, a_ego, delay)
+    comfort_decel = PROFILE_CONFIGS[self._profile(profile)].comfort_decel
+    candidates: list[EnergyEnvelope] = []
+    departure_candidates: list[tuple[float, int]] = []
+    departure_speeds = [math.inf, math.inf]
+    departure_separations = [-math.inf, -math.inf]
+    departure_caps = [math.inf, math.inf]
+
+    for lead_index, lead in enumerate(leads):
+      values = self._lead_values(lead)
+      if values is None:
+        continue
+      try:
+        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))
+      except (FloatingPointError, OverflowError, TypeError, ValueError):
+        continue
+
+      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:
+        continue
+      if not math.isfinite(separation):
+        continue
+
+      candidates.append(EnergyEnvelope(
+        cap=cap, selected_lead=lead_index, selected_lead_track_id=self._lead_track_id(lead),
+        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_separations[lead_index] = separation
+      departure_caps[lead_index] = departure_cap
+
+    if not candidates:
+      return EnergyEnvelope(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 EnergyEnvelope(
+      cap=selected.cap, selected_lead=selected.selected_lead, selected_lead_track_id=selected.selected_lead_track_id,
+      selected_lead_speed=selected.selected_lead_speed,
+      selected_lead_accel=selected.selected_lead_accel,
+      departure_lead_index=departure_lead_index, departure_lead_speed=departure_lead_speed,
+      departure_cap=departure_caps[departure_lead_index], departure_lead_separations=tuple(departure_separations),
+      usable_gap=selected.usable_gap, closing_speed=selected.closing_speed, required_decel=selected.required_decel,
+      has_nearly_stopped_lead=departure_lead_speed < STOPPED_LEAD_SPEED, lead_status=lead_status,
+    )
+
+  @staticmethod
+  def _move(value: float, target: float, rate: float, dt: float) -> float:
+    return float(np.clip(target, value - rate * dt, value + rate * dt))
+
+  @staticmethod
+  def _lead_source(source) -> bool:
+    return source in (LongitudinalPlanSource.lead0, LongitudinalPlanSource.lead1)
+
+  @staticmethod
+  def _update_samples(path: _ControllerPath, envelope: EnergyEnvelope) -> bool:
+    had_filtered_lead = math.isfinite(path.filtered_cap)
+    has_lead = envelope.selected_lead >= 0
+    path.cap_samples.append(envelope.cap if has_lead else math.inf)
+    path.lead_speed_samples.append(envelope.selected_lead_speed if has_lead else math.inf)
+    path.lead_accel_samples.append(envelope.selected_lead_accel if has_lead else 0.0)
+    path.lead_loss_frames = 0 if has_lead else path.lead_loss_frames + 1
+    for lead_index, separation in enumerate(envelope.departure_lead_separations):
+      if math.isfinite(separation):
+        path.departure_samples[lead_index].append(separation)
+    return not had_filtered_lead and math.isfinite(path.filtered_cap)
+
+  @staticmethod
+  def _seed_departure_tracking(path: _ControllerPath, envelope: EnergyEnvelope) -> None:
+    path.departure_samples = (deque(maxlen=CAP_FILTER_FRAMES), deque(maxlen=CAP_FILTER_FRAMES))
+    path.departure_references = [None, None]
+    for lead_index, separation in enumerate(envelope.departure_lead_separations):
+      if math.isfinite(separation):
+        path.departure_samples[lead_index].append(separation)
+        path.departure_references[lead_index] = separation
+    path.departure_frames = 0
+
+  @staticmethod
+  def _creep_departure(path: _ControllerPath, envelope: EnergyEnvelope) -> bool:
+    lead_index = envelope.departure_lead_index
+    if lead_index < 0 or envelope.departure_lead_speed <= STOP_HOLD_CREEP_SPEED:
+      return False
+    reference = path.departure_references[lead_index]
+    separation = path.robust_departure_separation(lead_index)
+    return reference is not None and separation - reference >= STOP_HOLD_CREEP_DISTANCE
+
+  def _enter_stop_hold(self, path: _ControllerPath, envelope: EnergyEnvelope) -> None:
+    if path.state != AccelControllerState.stopHold:
+      self._seed_departure_tracking(path, envelope)
+    path.pace = 0.0
+    path.state = AccelControllerState.stopHold
+    path.departure_frames = 0
+    path.launching = False
+    path.departure_launch = False
+    path.matched_lead = False
+    path.pace_reserve_armed = False
+    path.matched_accel_limit = None
+
+  def _update_path(self, path: _ControllerPath, envelope: EnergyEnvelope, base_speed: float, v_ego: float,
+                   profile: AccelProfile, profile_accel_max: float, previous_should_stop: bool,
+                   previous_mpc_source, planner_speed: float, planner_accel: float) -> float:
+    confirmed_lead = self._update_samples(path, envelope)
+    path.active_frames += 1
+    has_lead = envelope.selected_lead >= 0
+    filtered_cap = path.filtered_cap
+    slot_changed = has_lead and path.selected_lead >= 0 and envelope.selected_lead != path.selected_lead
+    track_changed = (has_lead and path.selected_lead >= 0 and envelope.selected_lead == path.selected_lead
+                     and envelope.selected_lead_track_id != path.selected_lead_track_id
+                     and (path.selected_lead_track_id >= 0 or envelope.selected_lead_track_id >= 0))
+    false_relief = (has_lead and math.isfinite(filtered_cap)
+                    and envelope.cap >= filtered_cap + PACE_RELIEF_DEADBAND)
+    if (slot_changed or track_changed) and false_relief and path.lead_switch_guard_frames == 0 and planner_accel <= BRAKING_ACCEL_LIMIT_THRESHOLD:
+      path.lead_switch_guard_frames = self.lead_loss_hold_frames
+    elif path.lead_switch_guard_frames > 0:
+      path.lead_switch_guard_frames -= 1
+    if slot_changed or track_changed:
+      path.matched_lead = False
+      path.matched_accel_limit = None
+    if has_lead:
+      path.selected_lead = envelope.selected_lead
+      path.selected_lead_track_id = envelope.selected_lead_track_id
+    elif path.lead_loss_frames >= self.lead_loss_hold_frames:
+      path.lead_switch_guard_frames = 0
+      path.selected_lead = -1
+      path.selected_lead_track_id = -1
+    departure_separation = (envelope.departure_lead_separations[envelope.departure_lead_index]
+                            if envelope.departure_lead_index >= 0 else math.inf)
+    stopped_lead_hold = (has_lead and envelope.has_nearly_stopped_lead
+                         and (envelope.departure_cap < 0.50
+                              or (path.braking_limited and departure_separation <= STOP_HOLD_MAX_LEAD_DISTANCE)))
+    invalid_lead = envelope.lead_status and not has_lead
+    prior_lead_context = self._lead_source(previous_mpc_source) or math.isfinite(filtered_cap) or path.braking_limited
+    previous_stop = (previous_should_stop and prior_lead_context
+                     and (not has_lead or envelope.departure_lead_speed < STOP_HOLD_EXIT_SPEED))
+    stop_evidence = (stopped_lead_hold or envelope.cap < 0.50 or filtered_cap < 0.50
+                     or (previous_stop and not path.launching) or invalid_lead)
+    confirmed_creep_departure = (path.launching and path.departure_launch and has_lead
+                                 and (envelope.departure_lead_speed > STOP_HOLD_CREEP_SPEED
+                                      or self._creep_departure(path, envelope)))
+    if (path.active_frames >= self.lead_loss_hold_frames and math.isfinite(filtered_cap)
+        and has_lead and planner_accel <= BRAKING_ACCEL_LIMIT_THRESHOLD):
+      path.braking_limited = True
+    elif not has_lead and path.lead_loss_frames >= self.lead_loss_hold_frames:
+      path.braking_limited = False
+
+    if path.pace is None:
+      e2e_handoff = previous_mpc_source == LongitudinalPlanSource.e2e
+      seed_from_ego = has_lead and planner_accel > BRAKING_ACCEL_LIMIT_THRESHOLD and not e2e_handoff
+      path.pace = min(base_speed, v_ego) if seed_from_ego else base_speed
+      path.braking_handoff = e2e_handoff and planner_accel < 0.0
+      path.state = AccelControllerState.free
+      if v_ego < STOP_HOLD_EGO_SPEED and not stop_evidence:
+        path.pace = min(base_speed, v_ego + LAUNCH_TARGET_HEADROOM)
+        path.state = AccelControllerState.release
+        path.launching = True
+        path.departure_launch = False
+    elif path.braking_handoff and planner_accel >= 0.0:
+      path.braking_handoff = False
+
+    path.pace = min(path.pace, base_speed)
+    if (v_ego < STOP_HOLD_EGO_SPEED and stop_evidence and not confirmed_creep_departure
+        and path.state != AccelControllerState.stopHold):
+      self._enter_stop_hold(path, envelope)
+      return path.pace
+
+    if path.state == AccelControllerState.stopHold:
+      for lead_index in range(len(path.departure_references)):
+        separation = path.robust_departure_separation(lead_index)
+        if math.isfinite(separation) and path.departure_references[lead_index] is None:
+          path.departure_references[lead_index] = separation
+      raw_departure = ((has_lead and envelope.departure_lead_speed > STOP_HOLD_CREEP_SPEED
+                        and envelope.departure_cap > STOP_HOLD_CREEP_SPEED)
+                       or (not envelope.lead_status and path.lead_loss_frames >= self.lead_loss_hold_frames))
+      departed = self._creep_departure(path, envelope) or raw_departure
+      path.departure_frames = path.departure_frames + 1 if departed else 0
+      path.pace = 0.0
+      if path.departure_frames < STOP_HOLD_EXIT_FRAMES:
+        return path.pace
+      path.pace = base_speed
+      path.state = AccelControllerState.release
+      path.departure_frames = 0
+      path.launching = True
+      path.departure_launch = has_lead
+      return path.pace
+
+    if path.launching:
+      invalid_lead = envelope.lead_status and not has_lead
+      renewed_stop = (has_lead and not confirmed_creep_departure
+                      and (envelope.cap < STOP_HOLD_EXIT_SPEED
+                           or (envelope.has_nearly_stopped_lead and envelope.departure_cap < STOP_HOLD_EXIT_SPEED)))
+      guarded_departure_loss = path.departure_launch and not envelope.lead_status and path.lead_loss_frames < self.lead_loss_hold_frames
+      if invalid_lead:
+        path.launching = False
+        path.departure_launch = False
+        if v_ego < STOP_HOLD_EGO_SPEED:
+          self._enter_stop_hold(path, envelope)
+          return path.pace
+        path.state = AccelControllerState.hold
+        return path.pace
+      if guarded_departure_loss:
+        path.state = AccelControllerState.hold
+        return path.pace
+      if path.departure_launch and not has_lead:
+        path.departure_launch = False
+      if renewed_stop:
+        path.launching = False
+        path.departure_launch = False
+        if v_ego < STOP_HOLD_EGO_SPEED:
+          self._enter_stop_hold(path, envelope)
+          return path.pace
+      if path.departure_launch:
+        path.pace = base_speed
+      else:
+        launch_target = min(base_speed, v_ego + LAUNCH_TARGET_HEADROOM)
+        path.pace = min(base_speed, max(path.pace, launch_target) + LAUNCH_TARGET_SLEW * self.dt)
+      if v_ego >= LAUNCH_END_SPEED:
+        path.launching = False
+        path.departure_launch = False
+
+    comfort_decel = PROFILE_CONFIGS[profile].comfort_decel
+    if (has_lead and not path.launching and path.state == AccelControllerState.restrict
+        and envelope.closing_speed <= 0.0
+        and v_ego >= path.filtered_lead_speed - VEGO_NOISE_TOLERANCE):
+      path.matched_lead = True
+    elif not has_lead and path.lead_loss_frames >= self.lead_loss_hold_frames:
+      path.matched_lead = False
+
+    if path.matched_lead:
+      if not has_lead:
+        if self._lead_source(previous_mpc_source) and planner_speed < path.pace:
+          path.pace = max(planner_speed, path.pace - MATCHED_PACE_DECEL_RATE * self.dt)
+        path.state = AccelControllerState.hold
+        return path.pace
+      if math.isfinite(path.filtered_lead_speed):
+        recovery_speed = min(base_speed, path.filtered_lead_speed + min(LEAD_MATCH_SPEED_HEADROOM, LEAD_MATCH_GAP_GAIN * envelope.usable_gap))
+        desired_accel_limit = min(profile_accel_max, LEAD_MATCH_TAPER_GAIN * max(recovery_speed - v_ego, 0.0))
+      else:
+        desired_accel_limit = 0.0
+      if path.filtered_lead_accel < BRAKING_ACCEL_LIMIT_THRESHOLD:
+        desired_accel_limit = profile_accel_max
+      if path.matched_accel_limit is None:
+        path.matched_accel_limit = profile_accel_max
+      if path.lead_switch_guard_frames > 0:
+        desired_accel_limit = min(desired_accel_limit, path.matched_accel_limit)
+      path.matched_accel_limit = min(profile_accel_max,
+                                     self._move(path.matched_accel_limit, desired_accel_limit, LEAD_MATCH_ACCEL_SLEW, self.dt))
+      matched_ceiling = min(base_speed, filtered_cap)
+      if matched_ceiling <= path.pace - PACE_RESTRICT_DEADBAND:
+        path.pace = max(matched_ceiling, path.pace - MATCHED_PACE_DECEL_RATE * self.dt)
+        path.state = AccelControllerState.restrict
+      elif path.lead_switch_guard_frames == 0 and matched_ceiling >= path.pace + PACE_RELIEF_DEADBAND:
+        path.pace = min(matched_ceiling, path.pace + profile_accel_max * self.dt)
+        path.state = AccelControllerState.free if path.pace >= base_speed - PACE_RESTRICT_DEADBAND else AccelControllerState.release
+      else:
+        path.state = AccelControllerState.free if path.pace >= base_speed - PACE_RESTRICT_DEADBAND else AccelControllerState.hold
+      return path.pace
+    path.matched_accel_limit = None
+
+    ceiling = min(base_speed, filtered_cap)
+    if (confirmed_lead and path.active_frames == CAP_FILTER_FRAMES // 2 + 1 and not path.launching
+        and planner_speed < path.pace):
+      path.pace = max(planner_speed, path.pace - comfort_decel * self.dt)
+
+    if self._lead_source(previous_mpc_source) and not has_lead and planner_speed < path.pace:
+      path.pace = max(planner_speed, path.pace - MATCHED_PACE_DECEL_RATE * self.dt)
+      path.state = AccelControllerState.hold
+      return path.pace
+
+    if ceiling <= path.pace - PACE_RESTRICT_DEADBAND or (path.state == AccelControllerState.restrict and ceiling < path.pace):
+      path.pace = max(ceiling, path.pace - comfort_decel * self.dt)
+      path.state = AccelControllerState.restrict
+      return path.pace
+
+    filter_warmup = has_lead and not math.isfinite(filtered_cap)
+    guarded_lead_loss = not has_lead and path.lead_loss_frames < self.lead_loss_hold_frames
+    if (filter_warmup or guarded_lead_loss) and path.pace < base_speed - PACE_RESTRICT_DEADBAND:
+      path.state = AccelControllerState.hold
+      return path.pace
+
+    confirmed_clear_road = not math.isfinite(filtered_cap) and not guarded_lead_loss
+    relief = not has_lead or envelope.closing_speed <= 0.0
+    if relief and (ceiling >= path.pace + PACE_RELIEF_DEADBAND or (confirmed_clear_road and ceiling > path.pace)):
+      if path.lead_switch_guard_frames == 0:
+        path.pace = ceiling
+      path.state = AccelControllerState.free if path.pace >= base_speed - PACE_RESTRICT_DEADBAND else AccelControllerState.release
+    else:
+      path.state = AccelControllerState.free if path.pace >= base_speed - PACE_RESTRICT_DEADBAND else AccelControllerState.hold
+    return path.pace
+
+  @staticmethod
+  def _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 _update_freshness(self, path: _ControllerPath, radar_fresh: bool) -> bool:
+    if radar_fresh:
+      path.stale_frames = 0
+      return True
+    path.stale_frames += 1
+    if path.stale_frames >= self.radar_stale_frames:
+      path.reset()
+    return False
+
+  @staticmethod
+  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.maximum(limit, a0 - ACCEL_LIMIT_HORIZON_JERK * T_IDXS)
+    ceiling = np.clip(ceiling, 0.0, ACCEL_MAX)
+    ceiling[0] = max(ceiling[0], a0)
+    return tuple(float(value) for value in ceiling)
+
+  def reset(self) -> None:
+    self.live.reset()
+    self.shadow.reset()
+    self._held_envelope = None
+
+  def update(self, radar_state, *, base_speed: float, v_ego: float, a_ego: float, profile: int | AccelProfile, follow_personality,
+             enabled: bool, 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) -> AccelControllerResult:
+    selected_profile = self._profile(profile)
+    sanitized_v_ego = max(v_ego, 0.0) if math.isfinite(v_ego) and v_ego >= -VEGO_NOISE_TOLERANCE else v_ego
+    profile_accel_max = self.get_profile_accel_max(selected_profile, sanitized_v_ego)
+    try:
+      stock_accel_max = float(stock_accel_max)
+    except (OverflowError, TypeError, ValueError):
+      stock_accel_max = math.nan
+    positive_accel_max = (max(0.0, min(profile_accel_max, stock_accel_max, ACCEL_MAX))
+                          if math.isfinite(profile_accel_max) and math.isfinite(stock_accel_max) else math.nan)
+    planner_speed = sanitized_v_ego if planner_speed is None else planner_speed
+    valid_context = self._valid_context(base_speed, sanitized_v_ego, a_ego, planner_speed, planner_accel, stock_accel_max, self._delay(),
+                                        engaged, cruise_initialized)
+    if valid_context and radar_fresh:
+      envelope = self.calculate_energy_envelope(radar_state, sanitized_v_ego, a_ego, selected_profile, follow_personality)
+      self._held_envelope = envelope
+    elif valid_context and self._held_envelope is not None:
+      envelope = self._held_envelope
+    else:
+      envelope = EnergyEnvelope(lead_status=self._radar_has_lead(radar_state))
+      if not valid_context:
+        self._held_envelope = None
+
+    shadow_fresh = self._update_freshness(self.shadow, radar_fresh) if valid_context else False
+    if valid_context and radar_fresh:
+      self._update_path(self.shadow, envelope, base_speed, sanitized_v_ego, selected_profile, profile_accel_max, previous_should_stop,
+                        previous_mpc_source, planner_speed, planner_accel)
+      shadow_active = True
+    elif valid_context and not shadow_fresh and self.shadow.pace is not None:
+      shadow_active = True
+    else:
+      self.shadow.reset()
+      shadow_active = False
+
+    live_context = valid_context and bool(enabled) and bool(acc_selected)
+    live_fresh = self._update_freshness(self.live, radar_fresh) if live_context else False
+    if live_context and radar_fresh:
+      pace_target = self._update_path(self.live, envelope, base_speed, sanitized_v_ego, selected_profile, profile_accel_max,
+                                      previous_should_stop,
+                                      previous_mpc_source, planner_speed, planner_accel)
+      live_active = True
+    elif live_context and not live_fresh and self.live.pace is not None:
+      pace_target = self.live.pace
+      live_active = True
+    else:
+      self.live.reset()
+      pace_target = base_speed
+      live_active = False
+
+    if not radar_fresh and not shadow_active and not live_active:
+      self._held_envelope = None
+      envelope = EnergyEnvelope(lead_status=self._radar_has_lead(radar_state))
+
+    stop_hold_active = live_active and self.live.state == AccelControllerState.stopHold
+    matched_limit_active = (live_active and self.live.matched_lead and self.live.matched_accel_limit is not None
+                            and not self.live.braking_handoff)
+    lead_accel_request = (live_active and envelope.selected_lead >= 0
+                          and envelope.closing_speed <= 0.0 and planner_accel >= 0.0)
+    profile_limit_active = live_active and not stop_hold_active and (self.live.launching or not envelope.lead_status or lead_accel_request)
+    if matched_limit_active:
+      effective_accel_max = min(positive_accel_max, self.live.matched_accel_limit)
+    elif profile_limit_active:
+      effective_accel_max = positive_accel_max
+    else:
+      effective_accel_max = math.inf
+    if matched_limit_active or profile_limit_active:
+      mpc_accel_max = self._build_accel_ceiling(effective_accel_max, planner_accel)
+    else:
+      mpc_accel_max = None
+    guarded_lead_loss = (not envelope.lead_status and self.live.selected_lead >= 0
+                         and self.live.lead_loss_frames < self.lead_loss_hold_frames)
+    lead_context = envelope.lead_status or math.isfinite(self.live.filtered_cap) or guarded_lead_loss
+    reserve_eligible = (live_active and lead_context and not stop_hold_active and self.live.lead_switch_guard_frames == 0
+                        and not self.live.launching and not self.live.braking_handoff)
+    if not lead_context:
+      self.live.pace_reserve_armed = False
+    elif (reserve_eligible and not self.live.pace_reserve_armed and math.isfinite(self.live.filtered_cap)
+          and self.live.filtered_cap <= pace_target + PACE_TARGET_ARM_MARGIN):
+      self.live.pace_reserve_armed = True
+
+    target_speed = 0.0 if stop_hold_active else pace_target
+    if reserve_eligible and self.live.pace_reserve_armed:
+      target_speed = max(0.0, target_speed - PACE_TARGET_RESERVE)
+
+    return AccelControllerResult(
+      target_speed=target_speed,
+      enabled=bool(enabled), active=live_active, shadow_active=shadow_active, launching=live_active and self.live.launching,
+      departure_launching=live_active and self.live.launching and self.live.departure_launch,
+      profile=selected_profile, profile_accel_max=profile_accel_max if live_active else math.inf,
+      positive_accel_max=positive_accel_max if live_active else math.inf, effective_accel_max=effective_accel_max,
+      mpc_accel_max=mpc_accel_max, state=self.live.state,
+      shadow_state=self.shadow.state, base_speed=base_speed, raw_energy_cap=envelope.cap,
+      live_filtered_cap=self.live.filtered_cap if live_active else math.inf,
+      shadow_filtered_cap=self.shadow.filtered_cap if shadow_active else math.inf, selected_lead=envelope.selected_lead,
+      selected_lead_speed=envelope.selected_lead_speed, usable_gap=envelope.usable_gap,
+      closing_speed=envelope.closing_speed, required_decel=envelope.required_decel,
+    )
+
+  @staticmethod
+  def _radar_has_lead(radar_state) -> bool:
+    try:
+      return bool(radar_state.leadOne.status or radar_state.leadTwo.status)
+    except (AttributeError, TypeError, ValueError):
+      return True

sunnypilot/selfdrive/controls/lib/accel_personality/constants.py

@@ -0,0 +1,63 @@
+from dataclasses import dataclass
+from enum import IntEnum
+
+
+class AccelProfile(IntEnum):
+  eco = 0
+  normal = 1
+  sport = 2
+
+
+@dataclass(frozen=True)
+class ProfileConfig:
+  comfort_decel: float
+
+
+PROFILE_CONFIGS = {
+  AccelProfile.eco: ProfileConfig(comfort_decel=0.25),
+  AccelProfile.normal: ProfileConfig(comfort_decel=0.30),
+  AccelProfile.sport: ProfileConfig(comfort_decel=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
+PACE_RESTRICT_DEADBAND = 0.15
+PACE_RELIEF_DEADBAND = 0.35
+PACE_TARGET_ARM_MARGIN = 1.0
+PACE_TARGET_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_TAPER_GAIN = 1.00
+LEAD_MATCH_ACCEL_SLEW = 0.25
+MATCHED_PACE_DECEL_RATE = 0.50
+BRAKING_ACCEL_LIMIT_THRESHOLD = -0.11
+MPC_DECEL_JERK_COST_MULTIPLIER = 1.05
+MPC_DECEL_JERK_MAX_REQUIRED_DECEL = 0.80
+MPC_DECEL_JERK_MAX_TARGET_REDUCTION = 9.0
+
+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
+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

sunnypilot/selfdrive/controls/lib/accel_personality/tests/test_accel_controller.py

@@ -0,0 +1,687 @@
+import math
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+
+from cereal import log
+from opendbc.car.interfaces import ACCEL_MAX, ACCEL_MIN
+from openpilot.common.realtime import DT_MDL
+from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import (
+  STOP_DISTANCE, T_IDXS, LongitudinalMpc, LongitudinalPlanSource, get_T_FOLLOW,
+)
+from openpilot.sunnypilot.selfdrive.controls.lib.accel_personality import AccelController, AccelControllerState, AccelProfile
+from openpilot.sunnypilot.selfdrive.controls.lib.accel_personality.constants import (
+  ACCEL_LIMIT_HORIZON_JERK, ACCEL_PROFILE_MAX_BP, ACCEL_PROFILE_MAX_V, CAP_FILTER_FRAMES, LAUNCH_END_SPEED,
+  LAUNCH_TARGET_HEADROOM, LAUNCH_TARGET_SLEW, LEAD_MATCH_ACCEL_SLEW, MATCHED_PACE_DECEL_RATE, PROFILE_CONFIGS,
+  PACE_TARGET_RESERVE, RADAR_STALE_TIMEOUT, STOP_GAP_RESERVE, STOP_HOLD_EXIT_FRAMES,
+)
+
+
+def make_lead(*, status=False, d_rel=0.0, v_lead_k=0.0, a_lead_k=0.0, a_lead_tau=1.5, radar_track_id=-1):
+  return SimpleNamespace(status=status, dRel=d_rel, vLeadK=v_lead_k, aLeadK=a_lead_k, aLeadTau=a_lead_tau,
+                         radarTrackId=radar_track_id)
+
+
+def make_radar(lead_one=None, lead_two=None):
+  return SimpleNamespace(leadOne=lead_one or make_lead(), leadTwo=lead_two or make_lead())
+
+
+def make_controller(delay=0.10):
+  return AccelController(SimpleNamespace(longitudinalActuatorDelay=delay))
+
+
+def update(controller, radar_state=None, **overrides):
+  args = {
+    "base_speed": 25.0,
+    "v_ego": 10.0,
+    "a_ego": 0.0,
+    "profile": AccelProfile.normal,
+    "follow_personality": log.LongitudinalPersonality.standard,
+    "enabled": True,
+    "acc_selected": True,
+    "engaged": True,
+    "cruise_initialized": True,
+    "stock_accel_max": ACCEL_MAX,
+    "previous_should_stop": False,
+  }
+  args.update(overrides)
+  return controller.update(radar_state or make_radar(), **args)
+
+
+def restrictive_radar():
+  return make_radar(make_lead(status=True, d_rel=20.0, v_lead_k=8.0, a_lead_k=-0.5))
+
+
+def enter_stop_hold(controller, *, base_speed=8.0, v_ego=0.1):
+  stopped = make_radar(make_lead(status=True, d_rel=6.0, v_lead_k=0.0))
+  return update(controller, stopped, base_speed=base_speed, v_ego=v_ego, previous_should_stop=True)
+
+
+class TestProfiles:
+  def test_lookup_table_is_explicit_and_tunable(self):
+    assert ACCEL_PROFILE_MAX_BP == [0.0, 3.0, 10.0, 25.0, 40.0]
+    assert 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],
+    }
+
+  @pytest.mark.parametrize("profile", list(AccelProfile))
+  def test_lookup_interpolates_and_stays_inside_global_limit(self, profile):
+    for speed, expected in zip(ACCEL_PROFILE_MAX_BP, ACCEL_PROFILE_MAX_V[profile], strict=True):
+      assert AccelController.get_profile_accel_max(profile, speed) == expected
+
+    limits = [AccelController.get_profile_accel_max(profile, speed) for speed in np.linspace(-1.0, 50.0, 201)]
+    assert all(0.0 <= limit <= ACCEL_MAX for limit in limits)
+    assert np.all(np.diff(limits) <= 0.0)
+
+  @pytest.mark.parametrize("speed", ACCEL_PROFILE_MAX_BP)
+  def test_profile_order_is_distinct(self, speed):
+    eco, normal, sport = [AccelController.get_profile_accel_max(profile, speed) for profile in AccelProfile]
+    assert eco < normal < sport
+
+  def test_invalid_profile_defaults_to_normal(self):
+    assert update(make_controller(), profile=999).profile == AccelProfile.normal
+
+  def test_stock_limit_intersects_profile_before_mpc(self):
+    controller = make_controller()
+    results = [update(controller, v_ego=10.0, profile=AccelProfile.sport, stock_accel_max=0.30)
+               for _ in range(controller.lead_loss_hold_frames)]
+    result = results[-1]
+    assert result.profile_accel_max == pytest.approx(1.15)
+    assert result.positive_accel_max == pytest.approx(0.30)
+    assert result.effective_accel_max == pytest.approx(0.30)
+    assert all(sample.mpc_accel_max is not None for sample in results)
+    assert all(max(sample.mpc_accel_max) <= 0.30 + 1e-9 for sample in results)
+
+  def test_runtime_profile_switch_applies_the_lookup_value_directly(self):
+    controller = make_controller()
+    sport = [update(controller, v_ego=10.0, profile=AccelProfile.sport, stock_accel_max=1.20)
+             for _ in range(controller.lead_loss_hold_frames)][-1]
+    eco = update(controller, v_ego=10.0, profile=AccelProfile.eco, stock_accel_max=1.20)
+
+    assert sport.effective_accel_max == pytest.approx(1.15)
+    assert eco.effective_accel_max == pytest.approx(0.72)
+
+  def test_matched_lead_waits_until_ego_catches_the_lead(self):
+    radar = make_radar(make_lead(status=True, d_rel=20.0, v_lead_k=8.0))
+    slow_controller, caught_controller = make_controller(), make_controller()
+    for controller in (slow_controller, caught_controller):
+      for _ in range(CAP_FILTER_FRAMES + 10):
+        update(controller, radar, v_ego=10.0, planner_accel=-0.2)
+
+    update(slow_controller, radar, v_ego=3.0, planner_accel=-0.2)
+    update(caught_controller, radar, v_ego=8.0, planner_accel=-0.2)
+
+    assert not slow_controller.live.matched_lead
+    assert caught_controller.live.matched_lead
+
+  def test_stock_limit_reduction_applies_immediately(self):
+    controller = make_controller()
+    for _ in range(controller.lead_loss_hold_frames):
+      update(controller, v_ego=10.0, profile=AccelProfile.sport, stock_accel_max=1.20)
+
+    reduced = update(controller, v_ego=10.0, profile=AccelProfile.sport, stock_accel_max=0.30)
+    assert reduced.effective_accel_max == pytest.approx(0.30)
+    assert reduced.mpc_accel_max is not None
+    assert max(reduced.mpc_accel_max) <= 0.30 + 1e-9
+
+  def test_one_frame_stock_zero_does_not_poison_profile_recovery(self):
+    clean_controller, glitch_controller = make_controller(), make_controller()
+    for _ in range(clean_controller.lead_loss_hold_frames + 10):
+      clean = update(clean_controller, v_ego=10.0, stock_accel_max=1.5)
+      recovered = update(glitch_controller, v_ego=10.0, stock_accel_max=1.5)
+
+    limited = update(glitch_controller, v_ego=10.0, stock_accel_max=0.0)
+    clean = update(clean_controller, v_ego=10.0, stock_accel_max=1.5)
+    recovered = update(glitch_controller, v_ego=10.0, stock_accel_max=1.5)
+
+    assert limited.effective_accel_max == 0.0
+    assert recovered.effective_accel_max == pytest.approx(clean.effective_accel_max)
+
+  @pytest.mark.parametrize("radar_fresh", (True, False), ids=("dropout", "stale"))
+  def test_matched_lead_ceiling_obeys_current_stock_limit(self, radar_fresh):
+    controller = make_controller()
+    radar = make_radar(make_lead(status=True, d_rel=20.0, v_lead_k=8.0))
+    for _ in range(CAP_FILTER_FRAMES + 10):
+      update(controller, radar, v_ego=10.0, planner_accel=-0.2)
+    for _ in range(20):
+      update(controller, radar, v_ego=8.0, planner_accel=-0.2)
+    assert controller.live.matched_lead
+
+    limited = update(controller, stock_accel_max=0.0, radar_fresh=radar_fresh)
+    assert limited.effective_accel_max == 0.0
+    assert limited.mpc_accel_max is not None
+    assert max(limited.mpc_accel_max) == 0.0
+
+  def test_exact_global_max_uses_stock_ceiling(self):
+    result = update(make_controller(), base_speed=8.0, v_ego=0.0, profile=AccelProfile.sport)
+    assert result.positive_accel_max == ACCEL_MAX
+    assert result.mpc_accel_max is None
+
+
+class TestMpcCeiling:
+  @pytest.mark.parametrize("planner_accel", (-1.0, 0.0, 1.2, ACCEL_MAX))
+  def test_ceiling_is_finite_feasible_and_jerk_bounded(self, planner_accel):
+    limit = 0.50
+    ceiling = np.asarray(AccelController._build_accel_ceiling(limit, planner_accel))
+    a0 = float(np.clip(planner_accel, ACCEL_MIN, ACCEL_MAX))
+
+    assert ceiling.shape == T_IDXS.shape
+    assert np.all(np.isfinite(ceiling))
+    assert np.all((0.0 <= ceiling) & (ceiling <= ACCEL_MAX))
+    assert ceiling[0] + 1e-9 >= a0
+    assert np.all(ceiling + 1e-9 >= limit)
+    assert np.all(np.diff(ceiling) <= 1e-9)
+    assert np.all(-np.diff(ceiling) <= ACCEL_LIMIT_HORIZON_JERK * np.diff(T_IDXS) + 1e-9)
+
+  def test_zero_limit_remains_feasible_for_positive_x0(self):
+    ceiling = np.asarray(AccelController._build_accel_ceiling(0.0, 0.8))
+    assert ceiling[0] == pytest.approx(0.8)
+    assert ceiling[-1] == pytest.approx(0.0)
+    assert np.all(ceiling >= 0.0)
+
+  def test_inactive_controller_has_no_custom_ceiling(self):
+    result = update(make_controller(), enabled=False)
+    assert not result.active
+    assert result.mpc_accel_max is None
+    assert math.isinf(result.effective_accel_max)
+
+  def test_profile_ceiling_does_not_interfere_while_planner_is_braking(self):
+    controller = make_controller()
+    radar = restrictive_radar()
+    warmup = [update(controller, radar, planner_accel=-0.2) for _ in range(controller.lead_loss_hold_frames)]
+
+    assert all(sample.mpc_accel_max is None for sample in warmup)
+    assert controller.live.braking_limited
+
+    bypassed = update(controller, radar, planner_accel=-0.2, acc_selected=False)
+    assert not bypassed.active and bypassed.mpc_accel_max is None
+    assert not controller.live.braking_limited
+
+  def test_profile_ceiling_stays_continuous_while_a_lead_begins_pulling_away(self):
+    controller = make_controller()
+    for _ in range(CAP_FILTER_FRAMES + 5):
+      update(controller, restrictive_radar(), v_ego=10.0, planner_accel=-0.2)
+
+    pulling_away = make_radar(make_lead(status=True, d_rel=20.0, v_lead_k=12.0))
+    result = update(controller, pulling_away, v_ego=10.0, planner_accel=0.2)
+
+    assert result.state == AccelControllerState.restrict
+    assert result.effective_accel_max == pytest.approx(result.positive_accel_max)
+    assert result.mpc_accel_max is not None
+
+  def test_matched_lead_terminal_taper_changes_smoothly(self):
+    controller = make_controller()
+    radar = make_radar(make_lead(status=True, d_rel=20.0, v_lead_k=8.0))
+    for _ in range(CAP_FILTER_FRAMES + 5):
+      update(controller, radar, v_ego=10.0, planner_accel=-0.2)
+
+    braking = update(controller, radar, v_ego=8.0, planner_accel=-0.2)
+    braking_limit = controller.live.matched_accel_limit
+    accelerating = update(controller, radar, v_ego=8.0, planner_accel=0.2)
+
+    assert controller.live.matched_lead
+    assert braking.mpc_accel_max is not None and accelerating.mpc_accel_max is not None
+    assert braking_limit is not None
+    assert abs(controller.live.matched_accel_limit - braking_limit) <= LEAD_MATCH_ACCEL_SLEW * DT_MDL + 1e-9
+    assert braking.effective_accel_max <= braking.positive_accel_max
+    assert accelerating.effective_accel_max <= accelerating.positive_accel_max
+
+  def test_matched_lead_ignores_two_frame_speed_jump(self):
+    clean_controller, noisy_controller = make_controller(), make_controller()
+    radar = make_radar(make_lead(status=True, d_rel=20.0, v_lead_k=8.0))
+    for controller in (clean_controller, noisy_controller):
+      for _ in range(CAP_FILTER_FRAMES + 10):
+        update(controller, radar, v_ego=10.0, planner_accel=-0.2)
+      for _ in range(20):
+        update(controller, radar, v_ego=8.0, planner_accel=-0.2)
+
+    speed_jump = make_radar(make_lead(status=True, d_rel=20.0, v_lead_k=16.0))
+    for _ in range(2):
+      clean = update(clean_controller, radar, v_ego=8.0)
+      noisy = update(noisy_controller, speed_jump, v_ego=8.0)
+      assert noisy.effective_accel_max == pytest.approx(clean.effective_accel_max)
+      assert noisy.target_speed == pytest.approx(clean.target_speed)
+
+  def test_matched_lead_ignores_two_frame_acceleration_jump(self):
+    clean_controller, noisy_controller = make_controller(), make_controller()
+    steady = make_radar(make_lead(status=True, d_rel=20.0, v_lead_k=8.0))
+    for controller in (clean_controller, noisy_controller):
+      for _ in range(CAP_FILTER_FRAMES + 10):
+        update(controller, steady, v_ego=10.0, planner_accel=-0.2)
+      for _ in range(20):
+        update(controller, steady, v_ego=8.0, planner_accel=-0.2)
+
+    braking_jump = make_radar(make_lead(status=True, d_rel=20.0, v_lead_k=8.0, a_lead_k=-1.0))
+    for _ in range(2):
+      clean = update(clean_controller, steady, v_ego=8.0)
+      noisy = update(noisy_controller, braking_jump, v_ego=8.0)
+      assert noisy.effective_accel_max == pytest.approx(clean.effective_accel_max)
+      assert noisy.target_speed == pytest.approx(clean.target_speed)
+
+
+class TestEnergyEnvelope:
+  def test_relative_pace_energy_formula(self):
+    controller = make_controller()
+    lead = make_lead(status=True, d_rel=50.0, v_lead_k=8.0)
+    envelope = controller.calculate_energy_envelope(make_radar(lead), 10.0, 0.0, AccelProfile.normal)
+    delay = controller._delay()
+    lead_xv = LongitudinalMpc.extrapolate_lead(lead.dRel, lead.vLeadK, lead.aLeadK, lead.aLeadTau)
+    x_lead = float(np.interp(delay, T_IDXS, lead_xv[:, 0]))
+    v_lead = float(np.interp(delay, T_IDXS, lead_xv[:, 1]))
+    x_ego, _ = controller._project_ego(10.0, 0.0, delay)
+    safety_gap = max(x_lead - x_ego - STOP_DISTANCE - get_T_FOLLOW(log.LongitudinalPersonality.standard) * v_lead, 0.0)
+    expected = v_lead + math.sqrt(2.0 * PROFILE_CONFIGS[AccelProfile.normal].comfort_decel * safety_gap)
+
+    assert envelope.cap == pytest.approx(expected)
+    assert envelope.cap != pytest.approx(math.sqrt(v_lead**2 + 2.0 * PROFILE_CONFIGS[AccelProfile.normal].comfort_decel * safety_gap))
+
+  def test_profile_order_controls_approach_timing(self):
+    radar = make_radar(make_lead(status=True, d_rel=50.0, v_lead_k=8.0))
+    caps = [make_controller().calculate_energy_envelope(radar, 10.0, 0.0, profile).cap for profile in AccelProfile]
+    assert caps[0] < caps[1] < caps[2]
+
+  def test_stopped_lead_reserve_only_reduces_comfort_gap(self):
+    envelope = make_controller().calculate_energy_envelope(
+      make_radar(make_lead(status=True, d_rel=60.0, v_lead_k=0.0)), 5.0, 0.0, AccelProfile.normal,
+    )
+    comfort_decel = PROFILE_CONFIGS[AccelProfile.normal].comfort_decel
+    safety_gap = (envelope.departure_cap - envelope.departure_lead_speed) ** 2 / (2.0 * comfort_decel)
+    assert envelope.required_decel < 0.30
+    assert safety_gap - envelope.usable_gap == pytest.approx(STOP_GAP_RESERVE)
+    assert envelope.departure_cap > envelope.cap
+
+  def test_more_restrictive_lead_is_selected(self):
+    radar = make_radar(make_lead(status=True, d_rel=70.0, v_lead_k=12.0), make_lead(status=True, d_rel=25.0, v_lead_k=8.0))
+    assert make_controller().calculate_energy_envelope(radar, 10.0, 0.0, AccelProfile.normal).selected_lead == 1
+
+  @pytest.mark.parametrize("field,value", [
+    ("aLeadK", math.nan), ("aLeadK", math.inf), ("aLeadTau", math.nan), ("aLeadTau", -1.0), ("radarTrackId", math.nan),
+  ])
+  def test_nonessential_invalid_lead_fields_are_sanitized(self, field, value):
+    lead = make_lead(status=True, d_rel=30.0, v_lead_k=8.0)
+    setattr(lead, field, value)
+    envelope = make_controller().calculate_energy_envelope(make_radar(lead), 10.0, 0.0, AccelProfile.normal)
+    assert envelope.selected_lead == 0
+    assert math.isfinite(envelope.cap)
+
+  @pytest.mark.parametrize("field,value", [("dRel", math.nan), ("dRel", -1.0), ("vLeadK", math.nan), ("vLeadK", -2.0)])
+  def test_invalid_geometry_is_not_used(self, field, value):
+    lead = make_lead(status=True, d_rel=30.0, v_lead_k=8.0)
+    setattr(lead, field, value)
+    envelope = make_controller().calculate_energy_envelope(make_radar(lead), 10.0, 0.0, AccelProfile.normal)
+    assert envelope.selected_lead == -1
+    assert envelope.lead_status
+    assert math.isinf(envelope.cap)
+
+  def test_raw_radar_is_never_mutated(self):
+    lead = make_lead(status=True, d_rel=30.0, v_lead_k=8.0, a_lead_k=-15.0, a_lead_tau=math.nan)
+    before = vars(lead).copy()
+    make_controller().calculate_energy_envelope(make_radar(lead), 10.0, 0.0, AccelProfile.normal)
+    assert vars(lead) == before
+
+
+class TestPaceAndLifecycle:
+  def test_five_frame_median_needs_three_restrictive_samples(self):
+    controller = make_controller()
+    results = [update(controller, restrictive_radar()) for _ in range(CAP_FILTER_FRAMES)]
+    assert math.isinf(results[1].live_filtered_cap)
+    assert math.isfinite(results[2].live_filtered_cap)
+
+  def test_restriction_uses_comfort_rate_with_one_bounded_reserve_step(self):
+    controller = make_controller()
+    results = [update(controller, restrictive_radar()) for _ in range(CAP_FILTER_FRAMES + 10)]
+    targets = np.asarray([result.target_speed for result in results])
+    max_step = PROFILE_CONFIGS[AccelProfile.normal].comfort_decel * DT_MDL
+
+    target_steps = -np.diff(targets)
+    assert np.count_nonzero(target_steps > max_step + 1e-9) == 1
+    assert np.max(target_steps) <= PACE_TARGET_RESERVE + max_step + 1e-9
+    assert results[-1].state == AccelControllerState.restrict
+    assert results[-1].target_speed < results[0].target_speed
+
+  @pytest.mark.parametrize("clear_frames", (1, 2, CAP_FILTER_FRAMES + 1))
+  def test_lead_acquired_after_clear_road_cannot_step_pace_to_planner(self, clear_frames):
+    controller = make_controller()
+    for _ in range(clear_frames):
+      update(controller, base_speed=25.0, v_ego=20.0, planner_speed=25.0)
+
+    results = [update(controller, restrictive_radar(), base_speed=25.0, v_ego=20.0, planner_speed=20.0)
+               for _ in range(CAP_FILTER_FRAMES)]
+    targets = np.asarray([25.0, *(result.target_speed for result in results)])
+    max_step = PROFILE_CONFIGS[AccelProfile.normal].comfort_decel * DT_MDL
+
+    target_steps = -np.diff(targets)
+    assert np.count_nonzero(target_steps > max_step + 1e-9) == 1
+    assert np.max(target_steps) <= PACE_TARGET_RESERVE + max_step + 1e-9
+
+  def test_lead_slot_is_forgotten_before_reacquisition(self):
+    controller = make_controller()
+    lead_one = restrictive_radar()
+    for _ in range(CAP_FILTER_FRAMES + 10):
+      update(controller, lead_one, base_speed=25.0, v_ego=20.0, planner_speed=20.0, planner_accel=-0.2)
+
+    for _ in range(controller.lead_loss_hold_frames):
+      before = update(controller, base_speed=25.0, v_ego=20.0, planner_speed=20.0, planner_accel=-0.2)
+    assert controller.live.selected_lead == -1
+
+    lead_two = make_radar(lead_two=make_lead(status=True, d_rel=20.0, v_lead_k=8.0, a_lead_k=-0.5))
+    results = [update(controller, lead_two, base_speed=25.0, v_ego=20.0, planner_speed=5.0, planner_accel=-0.2)
+               for _ in range(CAP_FILTER_FRAMES)]
+    targets = np.asarray([before.target_speed, *(result.target_speed for result in results)])
+    max_step = PROFILE_CONFIGS[AccelProfile.normal].comfort_decel * DT_MDL
+
+    target_steps = -np.diff(targets)
+    assert np.count_nonzero(target_steps > max_step + 1e-9) == 1
+    assert np.max(target_steps) <= PACE_TARGET_RESERVE + max_step + 1e-9
+
+  @pytest.mark.parametrize("replacement_track_id", (200, -1), ids=("radar-track", "vision-track"))
+  def test_false_relief_track_replacement_freezes_bounded_pace_release(self, replacement_track_id):
+    controller = make_controller()
+    original = make_radar(make_lead(status=True, d_rel=20.0, v_lead_k=8.0, radar_track_id=100))
+    for _ in range(CAP_FILTER_FRAMES + 10):
+      update(controller, original, base_speed=25.0, v_ego=10.0, planner_speed=10.0, planner_accel=-0.2)
+    for _ in range(20):
+      before = update(controller, original, base_speed=25.0, v_ego=8.0, planner_speed=8.0, planner_accel=-0.2)
+    assert controller.live.matched_lead
+
+    replacement = make_radar(make_lead(status=True, d_rel=40.0, v_lead_k=12.0, radar_track_id=replacement_track_id))
+    switched = update(controller, replacement, base_speed=25.0, v_ego=8.0, planner_speed=5.0, planner_accel=-0.2)
+
+    target_drop = before.target_speed - switched.target_speed
+    assert -PACE_TARGET_RESERVE - 1e-9 <= target_drop <= MATCHED_PACE_DECEL_RATE * DT_MDL + 1e-9
+    assert switched.effective_accel_max <= switched.positive_accel_max + 1e-9
+    assert switched.target_speed < switched.base_speed
+    assert controller.live.lead_switch_guard_frames == controller.lead_loss_hold_frames
+
+  def test_track_id_churn_without_false_relief_does_not_arm_guard(self):
+    controller = make_controller()
+    original = make_radar(make_lead(status=True, d_rel=20.0, v_lead_k=8.0, radar_track_id=100))
+    for _ in range(CAP_FILTER_FRAMES + 10):
+      update(controller, original, base_speed=25.0, v_ego=10.0, planner_speed=10.0, planner_accel=-0.2)
+
+    replacement = make_radar(make_lead(status=True, d_rel=20.0, v_lead_k=8.0, radar_track_id=200))
+    update(controller, replacement, base_speed=25.0, v_ego=10.0, planner_speed=10.0, planner_accel=-0.2)
+
+    assert controller.live.lead_switch_guard_frames == 0
+
+  def test_short_dropout_holds_then_releases_without_a_second_accel_cap(self):
+    controller = make_controller()
+    for _ in range(CAP_FILTER_FRAMES + 20):
+      restricted = update(controller, restrictive_radar())
+
+    held = [update(controller) for _ in range(controller.lead_loss_hold_frames - 1)]
+    assert all(result.target_speed <= restricted.target_speed + 1e-9 for result in held)
+
+    released = update(controller)
+    assert released.target_speed == released.base_speed
+
+  def test_previous_lead_source_synchronizes_down_to_planner(self):
+    controller = make_controller()
+    for _ in range(CAP_FILTER_FRAMES + 10):
+      restricted = update(controller, restrictive_radar())
+    planner_speed = restricted.target_speed - 2.0
+    synchronized = update(controller, previous_mpc_source=LongitudinalPlanSource.lead0, planner_speed=planner_speed)
+    assert restricted.target_speed - synchronized.target_speed == pytest.approx(MATCHED_PACE_DECEL_RATE * DT_MDL)
+    assert synchronized.state == AccelControllerState.hold
+
+  def test_matched_lead_dropout_synchronizes_down_to_planner(self):
+    controller = make_controller()
+    radar = make_radar(make_lead(status=True, d_rel=20.0, v_lead_k=8.0))
+    for _ in range(CAP_FILTER_FRAMES + 10):
+      update(controller, radar, v_ego=10.0, planner_accel=-0.2)
+    for _ in range(20):
+      matched = update(controller, radar, v_ego=8.0, planner_accel=-0.2)
+    assert controller.live.matched_lead
+
+    planner_speed = matched.target_speed - 2.0
+    synchronized = update(controller, previous_mpc_source=LongitudinalPlanSource.lead0, planner_speed=planner_speed)
+    assert matched.target_speed - synchronized.target_speed == pytest.approx(MATCHED_PACE_DECEL_RATE * DT_MDL)
+    assert synchronized.state == AccelControllerState.hold
+
+  def test_reused_radar_holds_matched_lead_until_a_fresh_dropout(self):
+    controller = make_controller()
+    radar = make_radar(make_lead(status=True, d_rel=20.0, v_lead_k=8.0))
+    for _ in range(CAP_FILTER_FRAMES + 10):
+      update(controller, radar, v_ego=10.0, planner_accel=-0.2)
+    for _ in range(20):
+      matched = update(controller, radar, v_ego=8.0, planner_accel=-0.2)
+    assert controller.live.matched_lead
+
+    planner_speed = matched.target_speed - 2.0
+    held = update(controller, radar, previous_mpc_source=LongitudinalPlanSource.lead0,
+                  planner_speed=planner_speed, radar_fresh=False)
+    synchronized = update(controller, previous_mpc_source=LongitudinalPlanSource.lead0, planner_speed=planner_speed)
+
+    assert held.target_speed == pytest.approx(matched.target_speed)
+    assert held.state == matched.state
+    assert held.target_speed - synchronized.target_speed == pytest.approx(MATCHED_PACE_DECEL_RATE * DT_MDL)
+    assert synchronized.state == AccelControllerState.hold
+
+  def test_clear_road_launch_has_immediate_headroom_and_bounded_target_slew(self):
+    controller = make_controller()
+    initial = update(controller, base_speed=12.0, v_ego=0.0, profile=AccelProfile.normal)
+    rolling = update(controller, base_speed=12.0, v_ego=0.31, profile=AccelProfile.normal)
+
+    assert initial.active and initial.launching
+    assert LAUNCH_TARGET_HEADROOM <= initial.target_speed <= LAUNCH_TARGET_HEADROOM + LAUNCH_TARGET_SLEW * DT_MDL
+    assert rolling.launching
+    assert rolling.target_speed >= 0.31 + LAUNCH_TARGET_HEADROOM
+    assert rolling.target_speed - max(initial.target_speed, 0.31 + LAUNCH_TARGET_HEADROOM) <= LAUNCH_TARGET_SLEW * DT_MDL + 1e-9
+
+    finished = update(controller, base_speed=12.0, v_ego=LAUNCH_END_SPEED, profile=AccelProfile.normal)
+    assert not finished.launching
+
+  def test_far_stopped_lead_does_not_create_stop_hold(self):
+    controller = make_controller()
+    far_stopped = make_radar(make_lead(status=True, d_rel=60.0, v_lead_k=0.0))
+    results = [update(controller, far_stopped, base_speed=12.0, v_ego=0.0) for _ in range(4)]
+    assert all(result.state != AccelControllerState.stopHold for result in results)
+
+  def test_far_stopped_lead_does_not_use_sticky_braking_history_as_stop_evidence(self):
+    controller = make_controller()
+    far_stopped = make_radar(make_lead(status=True, d_rel=60.0, v_lead_k=0.0))
+    for _ in range(controller.lead_loss_hold_frames):
+      update(controller, far_stopped, base_speed=12.0, v_ego=10.0, planner_accel=-0.2)
+    assert controller.live.braking_limited
+
+    result = update(controller, far_stopped, base_speed=12.0, v_ego=0.2, planner_accel=-0.2)
+    assert result.state != AccelControllerState.stopHold
+    assert result.target_speed > 0.0
+
+  def test_near_stopped_lead_uses_braking_history_to_hold_completed_stop(self):
+    controller = make_controller()
+    stopped = make_radar(make_lead(status=True, d_rel=20.0, v_lead_k=0.0))
+    for _ in range(controller.lead_loss_hold_frames):
+      update(controller, stopped, base_speed=12.0, v_ego=10.0, planner_accel=-0.2)
+    assert controller.live.braking_limited
+
+    result = update(controller, stopped, base_speed=12.0, v_ego=0.2, planner_accel=-0.2)
+    assert result.state == AccelControllerState.stopHold
+    assert controller.live.pace == 0.0
+    assert result.target_speed == 0.0
+    assert math.isinf(result.effective_accel_max)
+    assert result.mpc_accel_max is None
+
+    stock_limited = update(controller, stopped, base_speed=12.0, v_ego=0.2, stock_accel_max=0.0)
+    assert math.isinf(stock_limited.effective_accel_max)
+    assert stock_limited.mpc_accel_max is None
+
+  def test_stop_hold_needs_four_confirmed_departure_frames(self):
+    controller = make_controller()
+    held = enter_stop_hold(controller)
+    assert controller.live.pace == 0.0
+    departing = make_radar(make_lead(status=True, d_rel=8.0, v_lead_k=2.0))
+    results = [update(controller, departing, base_speed=8.0, v_ego=0.1) for _ in range(CAP_FILTER_FRAMES + STOP_HOLD_EXIT_FRAMES)]
+    launch_index = next(index for index, result in enumerate(results) if result.launching)
+
+    assert held.state == AccelControllerState.stopHold
+    assert held.target_speed == 0.0 and math.isinf(held.effective_accel_max)
+    assert held.mpc_accel_max is None
+    assert all(result.state == AccelControllerState.stopHold and not result.launching for result in results[:launch_index])
+    assert launch_index == STOP_HOLD_EXIT_FRAMES - 1
+    assert results[launch_index].target_speed >= 0.1 + LAUNCH_TARGET_HEADROOM
+    assert results[launch_index].departure_launching
+    assert results[launch_index].effective_accel_max == pytest.approx(results[launch_index].positive_accel_max)
+
+  def test_reused_radar_does_not_pulse_stop_hold_or_departure_target(self):
+    controller = make_controller()
+    enter_stop_hold(controller)
+    departing = make_radar(make_lead(status=True, d_rel=8.0, v_lead_k=2.0))
+
+    for frame in range(STOP_HOLD_EXIT_FRAMES):
+      fresh = update(controller, departing, base_speed=8.0, v_ego=0.1)
+      held = update(controller, departing, base_speed=8.0, v_ego=0.1, radar_fresh=False,
+                    previous_mpc_source=LongitudinalPlanSource.lead0, planner_speed=0.01)
+      assert held.target_speed == pytest.approx(fresh.target_speed)
+      assert held.state == fresh.state
+      assert held.selected_lead == fresh.selected_lead == 0
+      assert held.effective_accel_max == pytest.approx(fresh.effective_accel_max)
+      if frame < STOP_HOLD_EXIT_FRAMES - 1:
+        assert fresh.state == AccelControllerState.stopHold
+        assert math.isinf(fresh.effective_accel_max)
+        assert fresh.mpc_accel_max is None
+
+    assert fresh.launching and held.launching
+    assert fresh.departure_launching and held.departure_launching
+    assert fresh.target_speed == held.target_speed == 8.0
+    assert fresh.effective_accel_max == pytest.approx(fresh.positive_accel_max)
+
+  def test_single_frame_departure_stays_at_zero_target_without_an_accel_ceiling(self):
+    controller = make_controller()
+    enter_stop_hold(controller)
+    departing = make_radar(make_lead(status=True, d_rel=8.0, v_lead_k=2.0))
+    stopped = make_radar(make_lead(status=True, d_rel=8.0, v_lead_k=0.0))
+
+    warm = update(controller, departing, base_speed=8.0, v_ego=0.0)
+    held = update(controller, stopped, base_speed=8.0, v_ego=0.0)
+
+    assert warm.state == held.state == AccelControllerState.stopHold
+    assert not warm.launching and not held.launching
+    assert math.isinf(warm.effective_accel_max) and warm.mpc_accel_max is None
+    assert math.isinf(held.effective_accel_max) and held.mpc_accel_max is None
+    assert held.target_speed == 0.0
+
+  def test_previous_stop_without_a_lead_does_not_latch_stop_hold(self):
+    result = update(
+      make_controller(), base_speed=8.0, v_ego=0.0, previous_should_stop=True,
+      previous_mpc_source=LongitudinalPlanSource.cruise,
+    )
+
+    assert result.state != AccelControllerState.stopHold
+    assert result.target_speed >= LAUNCH_TARGET_HEADROOM
+
+  def test_previous_lead_stop_survives_a_fresh_full_field_dropout(self):
+    result = update(
+      make_controller(), base_speed=8.0, v_ego=0.0, previous_should_stop=True,
+      previous_mpc_source=LongitudinalPlanSource.lead0,
+    )
+
+    assert result.state == AccelControllerState.stopHold
+    assert result.target_speed == 0.0
+    assert math.isinf(result.effective_accel_max)
+    assert result.mpc_accel_max is None
+
+  def test_stop_hold_without_usable_lead_stays_pinned_to_zero(self):
+    controller = make_controller()
+    enter_stop_hold(controller)
+    missing = update(controller, base_speed=8.0, v_ego=0.1)
+
+    assert missing.state == AccelControllerState.stopHold
+    assert missing.target_speed == 0.0
+    assert math.isinf(missing.effective_accel_max)
+    assert missing.mpc_accel_max is None
+
+  def test_confirmed_creep_departure_does_not_reenter_stop_hold(self):
+    controller = make_controller()
+    enter_stop_hold(controller, v_ego=0.0)
+    results = []
+    for frame in range(60):
+      creeping = make_radar(make_lead(status=True, d_rel=6.0 + frame * 0.01, v_lead_k=0.2))
+      results.append(update(controller, creeping, base_speed=8.0, v_ego=0.0))
+
+    launch_index = next(index for index, result in enumerate(results) if result.launching)
+    assert all(result.state != AccelControllerState.stopHold for result in results[launch_index:])
+    assert all(result.target_speed > 0.0 for result in results[launch_index:])
+
+  def test_departure_dropout_holds_without_resurrecting_stop_hold(self):
+    controller = make_controller()
+    enter_stop_hold(controller)
+    departing = make_radar(make_lead(status=True, d_rel=8.0, v_lead_k=2.0))
+    results = [update(controller, departing, base_speed=8.0, v_ego=0.1) for _ in range(CAP_FILTER_FRAMES + STOP_HOLD_EXIT_FRAMES)]
+    launched = next(result for result in results if result.launching)
+    before_dropout = results[-1]
+    dropout = [update(controller, base_speed=8.0, v_ego=0.1) for _ in range(controller.lead_loss_hold_frames + 1)]
+
+    assert launched.launching
+    assert all(result.state != AccelControllerState.stopHold for result in dropout)
+    assert all(result.target_speed <= before_dropout.target_speed + 1e-9 for result in dropout[:controller.lead_loss_hold_frames - 1])
+    assert dropout[controller.lead_loss_hold_frames - 1].target_speed > before_dropout.target_speed
+    assert dropout[-1].launching
+
+  def test_invalid_departure_geometry_returns_to_stop_hold(self):
+    controller = make_controller()
+    enter_stop_hold(controller)
+    departing = make_radar(make_lead(status=True, d_rel=8.0, v_lead_k=2.0))
+    for _ in range(CAP_FILTER_FRAMES + STOP_HOLD_EXIT_FRAMES):
+      launched = update(controller, departing, base_speed=8.0, v_ego=0.1)
+    invalid = make_radar(make_lead(status=True, d_rel=math.nan, v_lead_k=2.0))
+    guarded = update(controller, invalid, base_speed=8.0, v_ego=0.1)
+    assert launched.launching
+    assert guarded.state == AccelControllerState.stopHold
+    assert guarded.target_speed == 0.0
+
+  def test_stale_timeout_fully_resets_live_and_shadow(self):
+    controller = make_controller()
+    for _ in range(CAP_FILTER_FRAMES + 10):
+      restricted = update(controller, restrictive_radar())
+    stale_frames = math.ceil(RADAR_STALE_TIMEOUT / DT_MDL)
+    held = [update(controller, radar_fresh=False) for _ in range(stale_frames - 1)]
+    timed_out = update(controller, radar_fresh=False)
+
+    assert all(result.active and result.target_speed == pytest.approx(restricted.target_speed) for result in held)
+    assert not timed_out.active and not timed_out.shadow_active
+    assert timed_out.target_speed == timed_out.base_speed
+    assert timed_out.mpc_accel_max is None
+    assert timed_out.selected_lead == -1 and math.isinf(timed_out.raw_energy_cap)
+    assert controller.live.pace is None and controller.shadow.pace is None
+
+  @pytest.mark.parametrize("override", [{"enabled": False}, {"acc_selected": False}, {"engaged": False}, {"cruise_initialized": False}, {"a_ego": math.inf}])
+  def test_bypass_or_invalid_context_resets_live_state(self, override):
+    controller = make_controller()
+    for _ in range(CAP_FILTER_FRAMES + 10):
+      update(controller, restrictive_radar())
+    result = update(controller, restrictive_radar(), **override)
+
+    assert not result.active
+    assert result.target_speed == result.base_speed
+    assert result.mpc_accel_max is None
+    assert controller.live.pace is None
+
+  def test_shadow_history_never_steps_into_live_actuation(self):
+    controller = make_controller()
+    for _ in range(CAP_FILTER_FRAMES + 20):
+      shadow = update(controller, restrictive_radar(), acc_selected=False)
+    live = update(controller)
+
+    assert shadow.shadow_active and not shadow.active
+    assert shadow.shadow_filtered_cap < math.inf
+    assert live.active and live.target_speed == live.base_speed
+    assert math.isinf(live.live_filtered_cap)
+
+  def test_explicit_reset_clears_every_path_field(self):
+    controller = make_controller()
+    for _ in range(CAP_FILTER_FRAMES + 10):
+      update(controller, restrictive_radar())
+    controller.reset()
+
+    assert controller._held_envelope is None
+    for path in (controller.live, controller.shadow):
+      assert path.pace is None and path.matched_accel_limit is None
+      assert path.state == AccelControllerState.inactive
+      assert path.departure_frames == path.active_frames == path.lead_loss_frames == path.stale_frames == 0
+      assert not path.launching and not path.departure_launch and not path.matched_lead
+      assert not path.braking_limited and not path.braking_handoff and not path.pace_reserve_armed
+      assert math.isinf(path.filtered_cap) and math.isinf(path.filtered_lead_speed) and path.filtered_lead_accel == 0.0

sunnypilot/selfdrive/controls/lib/accel_personality/tests/test_accel_controller_interfaces.py

@@ -0,0 +1,406 @@
+import inspect
+import math
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+
+from cereal import custom, log, messaging
+from opendbc.car.interfaces import ACCEL_MAX, ACCEL_MIN
+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_personality import AccelControllerState, AccelProfile
+from openpilot.sunnypilot.selfdrive.controls.lib.accel_personality.constants import (
+  MPC_DECEL_JERK_COST_MULTIPLIER, MPC_DECEL_JERK_MAX_REQUIRED_DECEL, MPC_DECEL_JERK_MAX_TARGET_REDUCTION,
+)
+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),
+      selfdriveState=SimpleNamespace(personality=0),
+    )
+    self.valid = {"radarState": True}
+    self.alive = {"radarState": True}
+    self.logMonoTime = {"radarState": radar_log_mono_time}
+
+
+def planner_for_mpc_test(*, target_speed=15.0, active=True, is_e2e=False, mpc_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._accel_jerk_smoothing_blocked = False
+  planner.mpc = SimpleNamespace(source=mpc_source, last_solution_status=0)
+  planner.update_accel_controller = lambda *_args, **_kwargs: setattr(
+    planner, "accel_controller_result",
+    SimpleNamespace(
+      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,
+    ),
+  )
+  return planner, is_e2e_calls
+
+
+def run_controller_mpc(planner, *, mpc_v_cruise=20.0, force_decel=False):
+  calls = []
+  planner._run_mpc = lambda *args, **kwargs: calls.append((args, kwargs))
+  is_e2e = planner.update_accel_controller_mpc(
+    {}, 20.0, mpc_v_cruise, True, reset_state=False, cruise_initialized=True,
+    available_accel_max=ACCEL_MAX, previous_should_stop=False, force_decel=force_decel,
+  )
+  return is_e2e, calls
+
+
+def test_profile_enum_keeps_toyota_importable():
+  expected = {"eco": 0, "normal": 1, "sport": 2}
+  assert custom.LongitudinalPlanSP.AccelerationPersonality.schema.enumerants == expected
+  assert custom.LongitudinalPlanSP.AccelController.Profile.schema.enumerants == expected
+  from opendbc.car.toyota.carstate import AccelPersonality, CarState
+
+  assert AccelPersonality.schema.enumerants == expected
+  assert CarState.__module__ == "opendbc.car.toyota.carstate"
+
+
+def test_mpc_accepts_optional_acceleration_ceiling_without_changing_stock_bounds():
+  assert tuple(inspect.signature(LongitudinalMpc.update).parameters) == ("self", "radarstate", "v_cruise", "personality", "accel_max")
+  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)
+
+  requested_ceiling = np.full(N + 1, 0.4)
+  mpc.update(radar, 30.0, accel_max=requested_ceiling)
+  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.update(radar, 30.0, accel_max=malformed_ceiling)
+    np.testing.assert_array_equal(mpc.params[:, 0], ACCEL_MIN)
+    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)
+  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_weights(True, personality=log.LongitudinalPersonality.standard, jerk_cost_multiplier=1.0)
+  explicit_costs, explicit_constraints = captured[-1]
+  mpc.set_weights(True, personality=log.LongitudinalPersonality.standard, jerk_cost_multiplier=1.2)
+  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, jerk_cost_multiplier=1.2)
+  assert captured[-1][0][-2] == 0.0
+  assert captured[-1][0][-1] == pytest.approx(default_costs[-1] * 1.2)
+
+
+def test_inherited_planner_uses_real_state_raw_radar_and_one_mpc_solve():
+  radar = radar_state()
+  planner = LongitudinalPlannerSP.__new__(LongitudinalPlannerSP)
+  planner.a_desired = -0.2
+  planner.v_desired_filter = SimpleNamespace(x=12.0)
+  calls = []
+
+  def update_mpc(radar_arg, target, *, personality, accel_max):
+    calls.append(("update", radar_arg, target, personality, accel_max))
+
+  planner.mpc = SimpleNamespace(
+    set_weights=lambda constraint, personality, jerk_cost_multiplier: calls.append(
+      ("weights", constraint, personality, jerk_cost_multiplier),
+    ),
+    set_cur_state=lambda speed, accel: calls.append(("state", speed, accel)),
+    update=update_mpc,
+  )
+  ceiling = tuple(np.linspace(0.8, 0.4, N + 1))
+  sm = {"radarState": radar, "selfdriveState": SimpleNamespace(personality=2)}
+  planner._run_mpc(sm, 17.5, True, ceiling, jerk_cost_multiplier=1.2)
+
+  assert calls == [
+    ("weights", True, 2, 1.2),
+    ("state", 12.0, -0.2),
+    ("update", radar, 17.5, 2, ceiling),
+  ]
+  assert calls[-1][1] is radar
+
+
+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, calls = run_controller_mpc(planner)
+
+  assert not is_e2e
+  assert len(mode_calls) == 1
+  assert calls == [(({}, 15.0, True, ceiling), {"jerk_cost_multiplier": 1.0})]
+
+
+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,
+  )
+  _, calls = run_controller_mpc(planner)
+
+  assert calls == [(({}, 20.0, True, None), {"jerk_cost_multiplier": 1.0})]
+
+
+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,
+  )
+  _, calls = run_controller_mpc(planner)
+
+  assert calls == [(({}, 0.0, True, None), {"jerk_cost_multiplier": 1.0})]
+
+
+@pytest.mark.parametrize(
+  ("active", "departure_launching", "is_e2e", "expected"),
+  [
+    (True, True, False, False),
+    (True, False, False, True),
+    (False, True, False, True),
+    (True, True, True, True),
+  ],
+)
+def test_only_confirmed_live_acc_departure_clears_should_stop(active, departure_launching, is_e2e, expected):
+  planner = LongitudinalPlannerSP.__new__(LongitudinalPlannerSP)
+  planner.accel_controller_result = SimpleNamespace(
+    active=active, departure_launching=departure_launching, state=AccelControllerState.stopHold,
+  )
+  assert planner.accel_controller_should_stop(True, is_e2e) is expected
+  expected_hold = active and not departure_launching and not is_e2e
+  assert planner.accel_controller_should_stop(False, is_e2e) is expected_hold
+
+
+@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, calls = run_controller_mpc(planner)
+
+  assert returned_e2e is is_e2e
+  assert len(mode_calls) == 1
+  assert calls == [(({}, 20.0, True, None), {"jerk_cost_multiplier": 1.0})]
+
+
+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)
+  _, calls = run_controller_mpc(planner, mpc_v_cruise=0.0, force_decel=True)
+
+  assert len(mode_calls) == 1
+  assert calls == [(({}, 0.0, True, None), {"jerk_cost_multiplier": 1.0})]
+
+
+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)
+  resets = []
+  planner.accel_controller = SimpleNamespace(reset=lambda: resets.append(True))
+  planner.mpc = SimpleNamespace(last_solution_status=4)
+
+  _, failed_recovery_calls = run_controller_mpc(planner)
+  assert resets == [True]
+  assert len(mode_calls) == 1
+  assert failed_recovery_calls == [(({}, 20.0, True, None), {"jerk_cost_multiplier": 1.0})]
+
+  planner.mpc.last_solution_status = 0
+  _, recovered_calls = run_controller_mpc(planner)
+  assert resets == [True]
+  assert len(mode_calls) == 2
+  assert recovered_calls == [(({}, 15.0, True, ceiling), {"jerk_cost_multiplier": 1.0})]
+
+
+def test_routine_governor_restriction_forwards_the_jerk_cost_multiplier():
+  planner, _ = planner_for_mpc_test(
+    state=AccelControllerState.restrict, selected_lead=0, required_decel=0.30,
+    mpc_source=MpcLongitudinalPlanSource.cruise,
+  )
+  _, calls = run_controller_mpc(planner)
+
+  assert calls == [(({}, 15.0, True, None), {"jerk_cost_multiplier": MPC_DECEL_JERK_COST_MULTIPLIER})]
+
+
+def test_lead_source_blocks_smoothing_only_until_the_restriction_episode_ends():
+  planner, _ = planner_for_mpc_test(
+    state=AccelControllerState.restrict, selected_lead=0, required_decel=0.30,
+    mpc_source=MpcLongitudinalPlanSource.cruise,
+  )
+  _, initial_calls = run_controller_mpc(planner)
+  routine_result = planner.accel_controller_result
+  assert initial_calls[0][1] == {"jerk_cost_multiplier": MPC_DECEL_JERK_COST_MULTIPLIER}
+
+  planner.mpc.source = MpcLongitudinalPlanSource.lead0
+  _, lead_calls = run_controller_mpc(planner)
+  assert lead_calls[0][1] == {"jerk_cost_multiplier": 1.0}
+
+  planner.mpc.source = MpcLongitudinalPlanSource.cruise
+  _, flicker_calls = run_controller_mpc(planner)
+  assert flicker_calls[0][1] == {"jerk_cost_multiplier": 1.0}
+
+  free_result = SimpleNamespace(**(vars(routine_result) | {"state": AccelControllerState.free, "target_speed": 20.0}))
+  planner.update_accel_controller = lambda *_args, **_kwargs: setattr(planner, "accel_controller_result", free_result)
+  run_controller_mpc(planner)
+  planner.update_accel_controller = lambda *_args, **_kwargs: setattr(planner, "accel_controller_result", routine_result)
+  _, rearmed_calls = run_controller_mpc(planner)
+  assert rearmed_calls[0][1] == {"jerk_cost_multiplier": MPC_DECEL_JERK_COST_MULTIPLIER}
+
+
+@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),
+    (AccelControllerState.restrict, 0, False, 0.30, 15.0, MpcLongitudinalPlanSource.lead0),
+    (AccelControllerState.restrict, 0, False, 0.30, 15.0, MpcLongitudinalPlanSource.lead1),
+  ],
+)
+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,
+  )
+  _, calls = run_controller_mpc(planner)
+
+  assert calls[0][1] == {"jerk_cost_multiplier": 1.0}
+
+
+def test_controller_receives_previous_mpc_state_and_cached_radar_freshness():
+  radar = radar_state()
+  planner = LongitudinalPlannerSP.__new__(LongitudinalPlannerSP)
+  planner.accel_personality = int(AccelProfile.normal)
+  planner.accel_personality_enabled = True
+  planner._radar_fresh_this_cycle = True
+  planner.a_desired = -0.4
+  planner.v_desired_filter = SimpleNamespace(x=9.5)
+  planner.mpc = SimpleNamespace(source=log.LongitudinalPlan.LongitudinalPlanSource.lead0)
+  received = {}
+  planner.accel_controller = SimpleNamespace(
+    update=lambda *_args, **kwargs: received.update(kwargs) or SimpleNamespace(target_speed=12.0),
+  )
+  sm = {
+    "radarState": radar,
+    "carState": SimpleNamespace(vEgo=10.0, aEgo=-0.2),
+    "selfdriveState": SimpleNamespace(personality=0),
+  }
+  planner.update_accel_controller(sm, 20.0, True, True, True, ACCEL_MAX, False)
+
+  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_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._read_accel_controller_params = lambda: None
+  planner.events_sp = SimpleNamespace(clear=lambda: None)
+  dec_freshness = []
+  planner.dec = SimpleNamespace(update=lambda _sm, *, radar_fresh: dec_freshness.append(radar_fresh))
+  planner.e2e_alerts_helper = SimpleNamespace(update=lambda *_args: None)
+  planner.accel_personality = int(AccelProfile.normal)
+  planner.accel_personality_enabled = True
+  planner.a_desired = 0.0
+  planner.v_desired_filter = SimpleNamespace(x=10.0)
+  planner.mpc = SimpleNamespace(source=log.LongitudinalPlan.LongitudinalPlanSource.cruise)
+  controller_freshness = []
+  planner.accel_controller = SimpleNamespace(
+    update=lambda *_args, **kwargs: controller_freshness.append(kwargs["radar_fresh"]) or SimpleNamespace(target_speed=20.0),
+  )
+
+  sm = PlannerSM(100)
+  for expected in (True, False):
+    planner.update(sm)
+    planner.update_accel_controller(sm, 20.0, True, True, True, ACCEL_MAX, False)
+    assert dec_freshness[-1] is expected and controller_freshness[-1] is expected
+
+  sm.logMonoTime["radarState"] = 101
+  planner.update(sm)
+  planner.update_accel_controller(sm, 20.0, True, True, True, ACCEL_MAX, False)
+  assert dec_freshness[-1] is True and controller_freshness[-1] is True
+
+
+def test_shadow_telemetry_publishes_controller_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_result = SimpleNamespace(
+    enabled=True, active=False, shadow_active=True, profile=AccelProfile.normal,
+    state=AccelControllerState.inactive, shadow_state=AccelControllerState.restrict,
+    base_speed=20.0, raw_energy_cap=15.0, live_filtered_cap=math.inf, shadow_filtered_cap=12.5,
+    selected_lead=1, usable_gap=30.0, closing_speed=5.0, required_decel=0.4,
+    profile_accel_max=math.inf, effective_accel_max=math.inf,
+  )
+  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 and telemetry.shadowOnly
+  assert telemetry.profile == int(AccelProfile.normal)
+  assert telemetry.state == int(AccelControllerState.restrict)
+  assert telemetry.vTargetBase == pytest.approx(20.0)
+  assert telemetry.vTargetRaw == pytest.approx(15.0)
+  assert telemetry.vTargetShadow == pytest.approx(12.5)
+  assert telemetry.leadIndex == 1
+  assert telemetry.usableGap == pytest.approx(30.0)
+  assert telemetry.closingSpeed == pytest.approx(5.0)
+  assert telemetry.requiredDecel == pytest.approx(0.4)
+  assert telemetry.aMaxProfile == math.inf
+  assert telemetry.aMaxEffective == math.inf

sunnypilot/selfdrive/controls/lib/dec/constants.py

@@ -1,17 +1,42 @@
+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
+  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

sunnypilot/selfdrive/controls/lib/dec/dec.py

@@ -6,129 +6,116 @@ See the LICENSE.md file in the root directory for more details.
 """
 # Version = 2025-6-30
 
-from cereal import messaging
-from opendbc.car import structs
-from numpy import interp
-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
+from cereal import messaging
+from numpy import interp
+from opendbc.car import structs
+from openpilot.common.params import Params
+from openpilot.sunnypilot.selfdrive.controls.lib.dec.constants import WMACConstants
 
-# 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 +129,32 @@ 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 +163,14 @@ 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
 
   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 +183,151 @@ 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.status))
-    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.status))
+      self._has_any_lead = bool(lead_one.status or lead_two.status)
+      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
+    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 _radar_acc_lead_score(self, lead_one) -> float:
+    radar_track_id = int(getattr(lead_one, 'radarTrackId', -1))
+    return float(lead_one.status 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 _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:
+      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:
+      return 'acc', True
 
-    # 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
 
+    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) -> None:
+    self._read_params()
+    self.set_mpc_fcw_crash_cnt()
+    self._update_calculations(sm, radar_fresh)
+
+    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._active = sm['selfdriveState'].experimentalMode and self._enabled
     self._frame += 1

sunnypilot/selfdrive/controls/lib/dec/tests/pytest_dynamic_controller.py

@@ -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)  # Ensure urgency triggers slowdown
-  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"

sunnypilot/selfdrive/controls/lib/dec/tests/test_dynamic_controller.py

@@ -0,0 +1,479 @@
+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.status = 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
+  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_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"

sunnypilot/selfdrive/controls/lib/longitudinal_planner.py

@@ -8,7 +8,15 @@ See the LICENSE.md file in the root directory for more details.
 from cereal import messaging, custom
 from opendbc.car import structs
 from openpilot.common.constants import CV
+from openpilot.common.params import Params
+from openpilot.common.realtime import DT_MDL
 from openpilot.selfdrive.car.cruise import V_CRUISE_MAX
+from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import LongitudinalPlanSource as MpcLongitudinalPlanSource
+from openpilot.sunnypilot import get_sanitize_int_param
+from openpilot.sunnypilot.selfdrive.controls.lib.accel_personality import AccelController, AccelControllerState, AccelProfile
+from openpilot.sunnypilot.selfdrive.controls.lib.accel_personality.constants import (
+  MPC_DECEL_JERK_COST_MULTIPLIER, MPC_DECEL_JERK_MAX_REQUIRED_DECEL, MPC_DECEL_JERK_MAX_TARGET_REDUCTION,
+)
 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
@@ -22,7 +30,8 @@ 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.params = Params()
     self.events_sp = EventsSP()
     self.resolver = SpeedLimitResolver()
     self.dec = DynamicExperimentalController(CP, mpc)
@@ -32,10 +41,29 @@ 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.accel_controller = AccelController(CP, dt=dt)
+    self.accel_controller_result = None
+    self._accel_jerk_smoothing_blocked = False
+    self._radar_log_mono_time = None
+    self._radar_fresh_this_cycle = True
+
+    self._param_read_frames = max(1, int(round(0.25 / dt)))
+    self._param_frame = 0
+    self.accel_personality_enabled = False
+    self.accel_personality = int(AccelProfile.normal)
 
     self.output_v_target = 0.
     self.output_a_target = 0.
 
+  def _read_accel_controller_params(self) -> None:
+    if self._param_frame % self._param_read_frames == 0:
+      self.accel_personality_enabled = self.params.get_bool("AccelPersonalityEnabled")
+      self.accel_personality = get_sanitize_int_param(
+        "AccelPersonality", int(AccelProfile.eco), int(AccelProfile.sport), self.params,
+      )
+
+    self._param_frame += 1
+
   def is_e2e(self, sm: messaging.SubMaster) -> bool:
     experimental_mode = sm['selfdriveState'].experimentalMode
     if not self.dec.active():
@@ -73,9 +101,92 @@ 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:
+    try:
+      radar_log_mono_time = int(sm.logMonoTime['radarState'])
+      radar_healthy = bool(sm.valid['radarState'] and sm.alive['radarState'])
+    except (AttributeError, KeyError, TypeError, ValueError):
+      return True
+
+    previous_log_mono_time = getattr(self, '_radar_log_mono_time', None)
+    radar_advanced = previous_log_mono_time is None or radar_log_mono_time > previous_log_mono_time
+    if radar_advanced:
+      self._radar_log_mono_time = radar_log_mono_time
+    return radar_healthy and radar_advanced
+
+  def update_accel_controller(self, sm: messaging.SubMaster, base_speed: float, engaged: bool, cruise_initialized: bool,
+                              acc_selected: bool, stock_accel_max: float, previous_should_stop: bool) -> float:
+    self.accel_controller_result = self.accel_controller.update(
+      sm['radarState'], base_speed=base_speed, v_ego=sm['carState'].vEgo, a_ego=sm['carState'].aEgo,
+      profile=self.accel_personality, follow_personality=sm['selfdriveState'].personality,
+      enabled=self.accel_personality_enabled, acc_selected=acc_selected, engaged=engaged, cruise_initialized=cruise_initialized,
+      stock_accel_max=stock_accel_max, previous_should_stop=previous_should_stop,
+      radar_fresh=getattr(self, '_radar_fresh_this_cycle', True),
+      previous_mpc_source=getattr(getattr(self, 'mpc', None), 'source', None),
+      planner_speed=getattr(getattr(self, 'v_desired_filter', None), 'x', sm['carState'].vEgo),
+      planner_accel=getattr(self, 'a_desired', sm['carState'].aEgo),
+    )
+    return self.accel_controller_result.target_speed
+
+  def _run_mpc(self, sm: messaging.SubMaster, v_cruise: float, prev_accel_constraint: bool, accel_max=None,
+               *, jerk_cost_multiplier: float = 1.0) -> None:
+    self.mpc.set_weights(
+      prev_accel_constraint, personality=sm['selfdriveState'].personality, jerk_cost_multiplier=jerk_cost_multiplier,
+    )
+    self.mpc.set_cur_state(self.v_desired_filter.x, self.a_desired)
+    self.mpc.update(sm['radarState'], v_cruise, personality=sm['selfdriveState'].personality, accel_max=accel_max)
+
+  def update_accel_controller_mpc(self, sm: messaging.SubMaster, base_v_cruise: float, mpc_v_cruise: float,
+                                  prev_accel_constraint: bool, *, reset_state: bool, cruise_initialized: bool,
+                                  available_accel_max: float, previous_should_stop: bool, force_decel: bool):
+    is_e2e = self.is_e2e(sm)
+    previous_mpc_failed = getattr(getattr(self, 'mpc', None), 'last_solution_status', 0) != 0
+    if previous_mpc_failed and hasattr(self, 'accel_controller'):
+      self.accel_controller.reset()
+
+    self.update_accel_controller(
+      sm, base_v_cruise, engaged=not reset_state and not force_decel, cruise_initialized=cruise_initialized,
+      acc_selected=not is_e2e and not previous_mpc_failed, stock_accel_max=available_accel_max, previous_should_stop=previous_should_stop,
+    )
+    result = self.accel_controller_result
+    actuating = result.active and not is_e2e and not force_decel and not previous_mpc_failed
+    valid_lead_stop_hold = (actuating and result.state == AccelControllerState.stopHold
+                            and result.selected_lead >= 0)
+    controller_v_cruise = mpc_v_cruise if valid_lead_stop_hold else min(mpc_v_cruise, result.target_speed) if actuating else mpc_v_cruise
+    accel_max = result.mpc_accel_max if actuating else None
+    target_reduction = mpc_v_cruise - controller_v_cruise
+    lead_restriction = (
+      actuating and prev_accel_constraint and result.state == AccelControllerState.restrict and result.selected_lead >= 0
+      and not result.launching and target_reduction > 1e-6
+    )
+    smoothing_eligible = (lead_restriction and target_reduction < MPC_DECEL_JERK_MAX_TARGET_REDUCTION
+                          and 0.0 < result.required_decel < MPC_DECEL_JERK_MAX_REQUIRED_DECEL)
+    smoothing_blocked = getattr(self, '_accel_jerk_smoothing_blocked', False)
+    if previous_mpc_failed:
+      smoothing_blocked = True
+    elif not lead_restriction:
+      smoothing_blocked = False
+    elif not smoothing_blocked and (getattr(self.mpc, 'source', None) != MpcLongitudinalPlanSource.cruise or not smoothing_eligible):
+      smoothing_blocked = True
+    self._accel_jerk_smoothing_blocked = smoothing_blocked
+    jerk_cost_multiplier = MPC_DECEL_JERK_COST_MULTIPLIER if smoothing_eligible and not smoothing_blocked else 1.0
+    self._run_mpc(sm, controller_v_cruise, prev_accel_constraint, accel_max, jerk_cost_multiplier=jerk_cost_multiplier)
+
+    return is_e2e
+
+  def accel_controller_should_stop(self, should_stop: bool, is_e2e: bool) -> bool:
+    result = self.accel_controller_result
+    if result is None or not result.active or is_e2e:
+      return should_stop
+    if result.departure_launching:
+      return False
+    return should_stop or result.state == AccelControllerState.stopHold
+
   def update(self, sm: messaging.SubMaster) -> None:
+    self._radar_fresh_this_cycle = self._update_radar_freshness(sm)
+    self._read_accel_controller_params()
     self.events_sp.clear()
-    self.dec.update(sm)
+    self.dec.update(sm, radar_fresh=self._radar_fresh_this_cycle)
     self.e2e_alerts_helper.update(sm, self.events_sp)
 
   def publish_longitudinal_plan_sp(self, sm: messaging.SubMaster, pm: messaging.PubMaster) -> None:
@@ -95,6 +206,25 @@ class LongitudinalPlannerSP:
     dec.enabled = self.dec.enabled()
     dec.active = self.dec.active()
 
+    if self.accel_controller_result is not None:
+      result = self.accel_controller_result
+      accel_controller = longitudinalPlanSP.accelController
+      accel_controller.enabled = result.enabled
+      accel_controller.active = result.active
+      accel_controller.shadowOnly = result.shadow_active and not result.active
+      accel_controller.profile = int(result.profile)
+      accel_controller.state = int(result.state if result.active else result.shadow_state)
+      accel_controller.vTargetBase = float(result.base_speed)
+      accel_controller.vTargetRaw = float(result.raw_energy_cap)
+      accel_controller.vTargetFiltered = float(result.live_filtered_cap)
+      accel_controller.vTargetShadow = float(result.shadow_filtered_cap)
+      accel_controller.leadIndex = result.selected_lead
+      accel_controller.usableGap = float(result.usable_gap)
+      accel_controller.closingSpeed = float(result.closing_speed)
+      accel_controller.requiredDecel = float(result.required_decel)
+      accel_controller.aMaxProfile = float(result.profile_accel_max)
+      accel_controller.aMaxEffective = float(result.effective_accel_max)
+
     # Smart Cruise Control
     smartCruiseControl = longitudinalPlanSP.smartCruiseControl
     # Vision Control

sunnypilot/selfdrive/controls/lib/relc.py

@@ -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

sunnypilot/selfdrive/controls/lib/smart_cruise_control/tests/test_vision_controller.py

@@ -4,6 +4,8 @@ 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
+
 import numpy as np
 import pytest
 
@@ -13,8 +15,12 @@ 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
 
@@ -118,6 +124,21 @@ 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
@@ -143,6 +164,253 @@ 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",
     [

sunnypilot/selfdrive/controls/lib/smart_cruise_control/tests/test_vision_controller_closed_loop.py

@@ -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.selfdrive.test.longitudinal_maneuvers.plant import Plant
+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
+
+
+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_personality_enabled = False
+  planner._read_accel_controller_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.

sunnypilot/selfdrive/controls/lib/smart_cruise_control/vision_controller.py

@@ -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()

sunnypilot/selfdrive/controls/lib/tests/test_accel_controller_closed_loop.py

@@ -0,0 +1,998 @@
+from collections.abc import Callable
+from dataclasses import dataclass
+import gc
+
+import numpy as np
+import pytest
+
+from opendbc.car.interfaces import ACCEL_MAX, ACCEL_MIN
+from openpilot.common.realtime import DT_MDL
+from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import STOP_DISTANCE, get_T_FOLLOW
+from openpilot.selfdrive.controls.lib.longitudinal_planner import get_max_accel
+from openpilot.selfdrive.test.longitudinal_maneuvers.plant import PRIUS_TSS2_ROUTE_MODEL, LeadObservation, Plant
+from openpilot.sunnypilot.selfdrive.controls.lib import longitudinal_planner as longitudinal_planner_sp
+from openpilot.sunnypilot.selfdrive.controls.lib.accel_personality import AccelControllerState, AccelProfile
+from openpilot.sunnypilot.selfdrive.controls.lib.accel_personality.constants import (
+  MATCHED_PACE_DECEL_RATE, MPC_DECEL_JERK_COST_MULTIPLIER, PACE_TARGET_RESERVE,
+)
+
+ACTUATOR_DYNAMICS = (
+  (0.10, 0.20),
+  (0.15, 0.25),
+  (0.20, 0.20),
+  (0.25, 0.30),
+  (0.30, 0.35),
+)
+ACTUATOR_IDS = ("toyota", "honda", "gm", "hyundai", "ford")
+ROUTINE_GAP_TOLERANCE = 0.10
+ROUTINE_DECEL_TOLERANCE = 0.10
+DROPOUT_GAP_TOLERANCE = 0.15
+MOVING_LEAD_GAP_TOLERANCE = 0.12
+
+
+@dataclass
+class ClosedLoopTrace:
+  time: np.ndarray
+  speed: np.ndarray
+  distance: np.ndarray
+  distance_lead: np.ndarray
+  a_target: np.ndarray
+  acceleration: np.ndarray
+  should_stop: np.ndarray
+  fcw: np.ndarray
+  source: list
+  dec_mode: list[str]
+  active: np.ndarray
+  shadow_active: np.ndarray
+  launching: np.ndarray
+  target_speed: np.ndarray
+  raw_cap: np.ndarray
+  filtered_cap: np.ndarray
+  selected_lead: np.ndarray
+  profile_accel_max: np.ndarray
+  effective_accel_max: np.ndarray
+  state: np.ndarray
+  required_decel: np.ndarray
+  planner_seed_accel: np.ndarray
+  mpc_seed_accel: np.ndarray
+  mpc_upper_first: np.ndarray
+  mpc_upper_min: np.ndarray
+  mpc_upper_max: np.ndarray
+  stock_bounds_valid: np.ndarray
+  raw_radar_passthrough: np.ndarray
+  actuator_command: np.ndarray
+  solver_status: np.ndarray
+  mpc_calls: np.ndarray
+  solver_failures: int
+  solver_failure_times: list[float]
+
+
+def _configure_plant(plant: Plant, *, enabled: bool, profile: int = 1, dec_enabled: bool = False) -> None:
+  plant.planner.accel_personality_enabled = enabled
+  plant.planner.accel_personality = profile
+  plant.planner._read_accel_controller_params = lambda: None
+  plant.planner.dec._enabled = dec_enabled
+  plant.planner.dec._read_params = lambda: None
+
+
+def _run(
+  *,
+  duration: float,
+  controller_enabled: bool,
+  profile: int = 1,
+  v_lead: float | Callable[[float], float] = 0.0,
+  v_cruise: float = 30.0,
+  dec_enabled: bool = False,
+  radar_fresh_fn: Callable[[int], bool] | None = None,
+  **plant_kwargs,
+) -> ClosedLoopTrace:
+  gc.collect()
+  plant = Plant(**plant_kwargs)
+  _configure_plant(plant, enabled=controller_enabled, profile=profile, dec_enabled=dec_enabled)
+  plant.v_lead_prev = float(v_lead) if isinstance(v_lead, (int, float)) else float(v_lead(0.0))
+  if radar_fresh_fn is not None:
+    radar_frame = 0
+
+    def patterned_radar_freshness(_sm):
+      nonlocal radar_frame
+      fresh = radar_fresh_fn(radar_frame)
+      radar_frame += 1
+      return fresh
+
+    plant.planner._update_radar_freshness = patterned_radar_freshness
+
+  solver_failures = 0
+  solver_failure_times = []
+  mpc_call_count = 0
+  controller_radar = None
+  radar_passthrough = []
+  seed_calls = []
+  original_controller_update = plant.planner.accel_controller.update
+  original_mpc_reset = plant.planner.mpc.reset
+  original_mpc_set_cur_state = plant.planner.mpc.set_cur_state
+  original_mpc_update = plant.planner.mpc.update
+
+  def record_controller_radar(radar_state, *args, **kwargs):
+    nonlocal controller_radar
+    controller_radar = radar_state
+    return original_controller_update(radar_state, *args, **kwargs)
+
+  def count_failed_solve(*args, **kwargs) -> None:
+    nonlocal solver_failures
+    if plant.planner.mpc.solution_status != 0:
+      solver_failures += 1
+      solver_failure_times.append(plant.current_time)
+    original_mpc_reset(*args, **kwargs)
+
+  def record_seed(v_ego, a_ego):
+    seed_calls.append((float(plant.planner.a_desired), float(a_ego)))
+    return original_mpc_set_cur_state(v_ego, a_ego)
+
+  def count_mpc_call(radar_state, *args, **kwargs):
+    nonlocal mpc_call_count
+    mpc_call_count += 1
+    radar_passthrough.append(radar_state is controller_radar)
+    return original_mpc_update(radar_state, *args, **kwargs)
+
+  plant.planner.accel_controller.update = record_controller_radar
+  plant.planner.mpc.reset = count_failed_solve
+  plant.planner.mpc.set_cur_state = record_seed
+  plant.planner.mpc.update = count_mpc_call
+  rows = []
+  sources = []
+  dec_modes = []
+  try:
+    while plant.current_time < duration:
+      lead_speed = float(v_lead) if isinstance(v_lead, (int, float)) else float(v_lead(plant.current_time))
+      calls_before = mpc_call_count
+      radar_checks_before = len(radar_passthrough)
+      seed_calls_before = len(seed_calls)
+      result = plant.step(v_lead=lead_speed, v_cruise=v_cruise)
+      controller = plant.planner.accel_controller_result
+      calls_this_frame = mpc_call_count - calls_before
+      passthrough_this_frame = (len(radar_passthrough) > radar_checks_before
+                                and all(radar_passthrough[radar_checks_before:]))
+      if len(seed_calls) > seed_calls_before:
+        planner_seed_accel, mpc_seed_accel = seed_calls[-1]
+      else:
+        planner_seed_accel = mpc_seed_accel = np.nan
+      lower = plant.planner.mpc.params[:, 0]
+      upper = plant.planner.mpc.params[:, 1]
+      bounds_valid = (np.allclose(lower, ACCEL_MIN) and np.all(np.isfinite(upper))
+                      and np.all(upper >= lower) and np.all(upper <= ACCEL_MAX + 1e-9))
+      rows.append((
+        plant.current_time, result["speed"], result["distance"], result["distance_lead"], result["a_target"],
+        result["realized_acceleration"], result["should_stop"], result["fcw"], controller.active,
+        controller.shadow_active, controller.launching, controller.target_speed, controller.raw_energy_cap,
+        controller.live_filtered_cap, controller.selected_lead, controller.profile_accel_max,
+        controller.effective_accel_max, controller.state, controller.required_decel, planner_seed_accel,
+        mpc_seed_accel, upper[0], np.min(upper), np.max(upper), bounds_valid, passthrough_this_frame,
+        result["actuator_command"], plant.planner.mpc.last_solution_status, calls_this_frame,
+      ))
+      sources.append(result["mpc_source"])
+      dec_modes.append(result["dec_mode"])
+  finally:
+    plant.planner.accel_controller.update = original_controller_update
+    plant.planner.mpc.reset = original_mpc_reset
+    plant.planner.mpc.set_cur_state = original_mpc_set_cur_state
+    plant.planner.mpc.update = original_mpc_update
+
+  data = np.asarray(rows, dtype=float)
+  trace = ClosedLoopTrace(
+    time=data[:, 0], speed=data[:, 1], distance=data[:, 2], distance_lead=data[:, 3], a_target=data[:, 4], acceleration=data[:, 5],
+    should_stop=data[:, 6].astype(bool), fcw=data[:, 7].astype(bool), source=sources, dec_mode=dec_modes,
+    active=data[:, 8].astype(bool), shadow_active=data[:, 9].astype(bool), launching=data[:, 10].astype(bool), target_speed=data[:, 11],
+    raw_cap=data[:, 12], filtered_cap=data[:, 13], selected_lead=data[:, 14].astype(int), profile_accel_max=data[:, 15],
+    effective_accel_max=data[:, 16], state=data[:, 17].astype(int), required_decel=data[:, 18], planner_seed_accel=data[:, 19],
+    mpc_seed_accel=data[:, 20], mpc_upper_first=data[:, 21], mpc_upper_min=data[:, 22], mpc_upper_max=data[:, 23],
+    stock_bounds_valid=data[:, 24].astype(bool), raw_radar_passthrough=data[:, 25].astype(bool), actuator_command=data[:, 26],
+    solver_status=data[:, 27].astype(int), mpc_calls=data[:, 28].astype(int), solver_failures=solver_failures,
+    solver_failure_times=solver_failure_times,
+  )
+  gc.collect()
+  return trace
+
+
+def _first_time_below(trace: ClosedLoopTrace, threshold: float) -> float:
+  indices = np.flatnonzero(trace.a_target <= threshold)
+  assert len(indices), f"never reached {threshold} m/s²"
+  return float(trace.time[indices[0]])
+
+
+def _sustained_time_below(trace: ClosedLoopTrace, threshold: float, *, after: float = 0.5, duration: float = 0.5) -> float:
+  required_frames = round(duration / DT_MDL)
+  below = (trace.time >= after) & (trace.a_target <= threshold)
+  sustained = np.convolve(below.astype(int), np.ones(required_frames, dtype=int), mode="valid") == required_frames
+  indices = np.flatnonzero(sustained)
+  assert len(indices), f"never sustained {threshold} m/s² for {duration} s"
+  return float(trace.time[indices[0]])
+
+
+def _command_jerk(trace: ClosedLoopTrace, after: float = 0.0) -> np.ndarray:
+  indices = np.flatnonzero(trace.time >= after)
+  assert len(indices) >= 2
+  return np.diff(trace.a_target[indices]) / DT_MDL
+
+
+def _filtered_realized_jerk(trace: ClosedLoopTrace, after: float = 1.0) -> np.ndarray:
+  filtered_acceleration = np.convolve(trace.acceleration, np.ones(3) / 3.0, mode="valid")
+  samples = trace.time[2:-1] >= after
+  return (np.diff(filtered_acceleration) / DT_MDL)[samples]
+
+
+def _has_brake_coast_brake(values: np.ndarray, brake: float = -0.8, coast: float = -0.35, frames: int = 2) -> bool:
+  phase = 0
+  for index in range(len(values) - frames + 1):
+    window = values[index:index + frames]
+    if np.all(window <= brake):
+      if phase == 2:
+        return True
+      phase = 1
+    elif phase == 1 and np.all(window >= coast):
+      phase = 2
+  return False
+
+
+def _has_propulsion_after_braking(values: np.ndarray, propulsion: float = 0.2, brake: float = -0.2, frames: int = 2) -> bool:
+  braking = False
+  for index in range(len(values) - frames + 1):
+    window = values[index:index + frames]
+    if np.all(window <= brake):
+      braking = True
+    elif braking and np.all(window >= propulsion):
+      return True
+  return False
+
+
+def _has_propulsion_brake_cycle(values: np.ndarray, propulsion: float = 0.2, brake: float = -0.2, frames: int = 2) -> bool:
+  phases = []
+  for index in range(len(values) - frames + 1):
+    window = values[index:index + frames]
+    phase = 1 if np.all(window >= propulsion) else -1 if np.all(window <= brake) else 0
+    if phase and (not phases or phase != phases[-1]):
+      phases.append(phase)
+      if len(phases) >= 3 and phases[-1] == phases[-3]:
+        return True
+  return False
+
+
+def _assert_non_actuating_matches_stock(trace: ClosedLoopTrace, baseline: ClosedLoopTrace) -> None:
+  np.testing.assert_allclose(trace.a_target, baseline.a_target, atol=1e-6, rtol=0.0)
+  np.testing.assert_array_equal(trace.should_stop, baseline.should_stop)
+  np.testing.assert_array_equal(trace.fcw, baseline.fcw)
+  np.testing.assert_array_equal(trace.solver_status, baseline.solver_status)
+  assert trace.source == baseline.source
+  assert trace.solver_failures == baseline.solver_failures
+  assert trace.solver_failure_times == baseline.solver_failure_times
+
+
+def _assert_no_new_solver_failures(trace: ClosedLoopTrace, baseline: ClosedLoopTrace) -> None:
+  assert trace.solver_failures <= baseline.solver_failures
+
+
+@pytest.mark.parametrize(
+  ("plant_kwargs", "expect_shadow"),
+  [
+    ({"enabled": False, "lead_relevancy": True, "speed": 20.0, "distance_lead": 70.0}, False),
+    ({"e2e": True, "lead_relevancy": False, "speed": 20.0}, True),
+  ],
+  ids=("disengaged", "e2e-shadow"),
+)
+def test_non_actuating_modes_match_stock(plant_kwargs, expect_shadow):
+  common = dict(duration=2.0, v_lead=14.0, **plant_kwargs)
+  baseline = _run(controller_enabled=False, **common)
+  trace = _run(controller_enabled=True, **common)
+
+  _assert_non_actuating_matches_stock(trace, baseline)
+  assert not trace.active.any()
+  np.testing.assert_array_equal(trace.shadow_active, np.full_like(trace.active, expect_shadow))
+  np.testing.assert_allclose(trace.mpc_upper_min, baseline.mpc_upper_min)
+  assert trace.raw_radar_passthrough.all()
+  assert np.all(trace.mpc_calls == 1)
+
+
+def test_disabled_profiles_are_identical():
+  common = dict(duration=2.0, controller_enabled=False, lead_relevancy=True, speed=20.0, distance_lead=70.0, v_lead=14.0)
+  traces = [_run(profile=profile, **common) for profile in range(3)]
+  for trace in traces[1:]:
+    _assert_non_actuating_matches_stock(trace, traces[0])
+
+
+@pytest.mark.parametrize("lead_relevancy", (False, True), ids=("clear-road", "lead"))
+def test_force_decel_matches_stock(lead_relevancy):
+  common = dict(duration=2.0, force_decel=True, lead_relevancy=lead_relevancy, speed=20.0,
+                distance_lead=70.0, v_lead=14.0, profile=0)
+  baseline = _run(controller_enabled=False, **common)
+  trace = _run(controller_enabled=True, **common)
+  _assert_non_actuating_matches_stock(trace, baseline)
+  assert not trace.active.any()
+  np.testing.assert_allclose(trace.mpc_upper_min, ACCEL_MAX)
+
+
+@pytest.mark.parametrize("profile", range(3), ids=("eco", "normal", "sport"))
+def test_active_controller_uses_one_raw_mpc_solve_and_feasible_stock_bounds(profile):
+  trace = _run(
+    duration=4.0, controller_enabled=True, profile=profile, lead_relevancy=False, speed=0.0,
+    v_cruise=22.352, actuator_delay=0.15, actuator_lag=0.20,
+  )
+
+  assert trace.active.all()
+  assert np.all(trace.mpc_calls == 1)
+  assert trace.raw_radar_passthrough.all()
+  assert trace.stock_bounds_valid.all()
+  np.testing.assert_allclose(trace.mpc_seed_accel, trace.planner_seed_accel, atol=1e-12, rtol=0.0)
+  assert np.all(trace.mpc_upper_first + 1e-9 >= trace.mpc_seed_accel)
+  assert np.all(trace.mpc_upper_min >= 0.0)
+  assert np.any(trace.mpc_upper_min < ACCEL_MAX - 0.05)
+  assert trace.solver_failures == 0
+
+
+def test_e2e_to_radar_acc_handoff_keeps_braking_continuous():
+  def run_handoff(controller_enabled: bool):
+    plant = Plant(
+      lead_relevancy=True, speed=10.0, distance_lead=30.0, actuator_delay=0.15, actuator_lag=0.20,
+      model_action_fn=lambda current_time, _v_ego, _a_ego: (-1.0 if current_time < 2.0 else 0.0, False),
+    )
+    _configure_plant(plant, enabled=controller_enabled)
+    rows = []
+    while plant.current_time < 2.4:
+      plant.e2e = plant.current_time < 2.0
+      result = plant.step(v_lead=8.0, v_cruise=20.0)
+      rows.append((plant.current_time, result["a_target"], plant.planner.mpc.last_solution_status,
+                   plant.planner.accel_controller_result.active))
+    return np.asarray(rows, dtype=float).T
+
+  baseline_time, baseline_accel, baseline_status, _ = run_handoff(False)
+  time_values, acceleration, solver_status, active = run_handoff(True)
+  np.testing.assert_allclose(time_values, baseline_time, atol=0.0, rtol=0.0)
+  transition = np.flatnonzero(time_values > 2.0)[0]
+  baseline_jump = abs(baseline_accel[transition] - baseline_accel[transition - 1])
+  controlled_jump = abs(acceleration[transition] - acceleration[transition - 1])
+  baseline_jerk = np.max(np.abs(np.diff(baseline_accel[transition:]) / DT_MDL))
+  controlled_jerk = np.max(np.abs(np.diff(acceleration[transition:]) / DT_MDL))
+
+  assert controlled_jump <= baseline_jump + 1e-6
+  assert controlled_jerk <= baseline_jerk + 0.10
+  assert np.count_nonzero(solver_status[transition:]) <= np.count_nonzero(baseline_status[transition:])
+  assert active[transition]
+
+
+def test_dec_retains_acc_through_route_like_radar_marker_dropout():
+  dropout_start = 1.0
+  reacquisition_time = 1.8
+
+  def observe(current_time: float, lead_name: str, truth: LeadObservation) -> LeadObservation:
+    frame = round(current_time / DT_MDL)
+    if current_time < dropout_start:
+      marked_slot = "leadOne" if frame % 2 == 0 else "leadTwo"
+      return truth | {"radar": lead_name == marked_slot, "radarTrackId": 985 + frame if lead_name == marked_slot else -1}
+    if current_time < reacquisition_time:
+      return truth | {"radar": False, "radarTrackId": -1}
+    return truth | {"radar": lead_name == "leadOne", "radarTrackId": 1263 if lead_name == "leadOne" else -1}
+
+  trace = _run(
+    duration=2.5, controller_enabled=True, dec_enabled=True, e2e=True, lead_relevancy=True, speed=20.0,
+    distance_lead=35.0, v_lead=18.0, v_cruise=30.0, lead_observation_fn=observe,
+    model_action_fn=lambda _current_time, _v_ego, _a_ego: (-2.0, False), actuator_delay=0.15, actuator_lag=0.20,
+  )
+  response = (trace.time >= dropout_start - DT_MDL) & (trace.time <= reacquisition_time + 0.5)
+
+  assert all(mode == "acc" for mode in trace.dec_mode)
+  assert all(str(source) != "e2e" for source in trace.source)
+  assert not trace.fcw.any()
+  assert not _has_propulsion_brake_cycle(trace.a_target[response])
+  assert np.max(np.abs(np.diff(trace.a_target[response]) / DT_MDL)) < 3.0
+  assert trace.raw_radar_passthrough.all()
+  assert np.all(trace.mpc_calls == 1)
+  assert trace.solver_failures == 0
+
+
+def test_clear_road_launch_is_prompt_and_profiles_separate_above_launch_speed():
+  traces = [
+    _run(
+      duration=12.0, controller_enabled=True, profile=profile, lead_relevancy=False, speed=0.0,
+      v_cruise=22.352, actuator_delay=0.15, actuator_lag=0.20,
+    )
+    for profile in range(3)
+  ]
+
+  for trace in traces:
+    positive = np.flatnonzero(trace.a_target > 0.05)
+    moving = np.flatnonzero(trace.speed > 0.01)
+    assert len(positive) and trace.time[positive[0]] <= 4 * DT_MDL
+    assert len(moving) and trace.time[moving[0]] <= 1.0
+    assert np.interp(1.0, trace.time, trace.speed) >= 0.33
+    assert not np.any(trace.a_target < -0.05)
+    assert trace.solver_failures == 0
+
+  launch_window = traces[0].time <= 0.5
+  np.testing.assert_allclose(traces[1].a_target[launch_window], traces[0].a_target[launch_window], atol=0.10, rtol=0.0)
+  np.testing.assert_allclose(traces[2].a_target[launch_window], traces[0].a_target[launch_window], atol=0.10, rtol=0.0)
+  speed_at_eight = [float(np.interp(8.0, trace.time, trace.speed)) for trace in traces]
+  assert speed_at_eight[0] + 0.75 < speed_at_eight[1]
+  assert speed_at_eight[1] + 0.30 < speed_at_eight[2]
+  final_speed = [float(trace.speed[-1]) for trace in traces]
+  assert final_speed[0] + 1.25 < final_speed[1]
+  assert final_speed[1] + 0.75 < final_speed[2]
+  ceiling_at_ten = [float(np.interp(10.0, trace.speed, trace.mpc_upper_min)) for trace in traces]
+  assert ceiling_at_ten[0] < ceiling_at_ten[1] < ceiling_at_ten[2]
+
+
+@pytest.mark.parametrize(
+  ("speed", "v_cruise"),
+  ((0.0, 22.352), (25.0, 30.0), (35.0, 35.0)),
+)
+def test_decel_smoothing_does_not_change_clear_road_acceleration_at_representative_speeds(monkeypatch, speed, v_cruise):
+  common = dict(
+    duration=3.0, controller_enabled=True, profile=AccelProfile.sport, lead_relevancy=False,
+    speed=speed, v_cruise=v_cruise, actuator_delay=0.15, actuator_lag=0.20,
+  )
+  monkeypatch.setattr(longitudinal_planner_sp, "MPC_DECEL_JERK_COST_MULTIPLIER", 1.0)
+  stock_weight = _run(**common)
+  monkeypatch.setattr(longitudinal_planner_sp, "MPC_DECEL_JERK_COST_MULTIPLIER", MPC_DECEL_JERK_COST_MULTIPLIER)
+  smoothed = _run(**common)
+
+  np.testing.assert_allclose(smoothed.a_target, stock_weight.a_target, atol=1e-9, rtol=0.0)
+  np.testing.assert_allclose(smoothed.speed, stock_weight.speed, atol=1e-9, rtol=0.0)
+  np.testing.assert_allclose(smoothed.effective_accel_max, stock_weight.effective_accel_max, atol=1e-9, rtol=0.0)
+  assert smoothed.solver_failures == stock_weight.solver_failures == 0
+
+
+def test_prius_route_model_launches_without_a_dead_pedal():
+  trace = _run(
+    duration=3.0, controller_enabled=True, profile=1, lead_relevancy=False, speed=0.0,
+    v_cruise=22.352, actuator_model=PRIUS_TSS2_ROUTE_MODEL,
+  )
+  positive = np.flatnonzero(trace.a_target > 0.05)
+  moving = np.flatnonzero(trace.speed > 0.05)
+  assert len(positive) and trace.time[positive[0]] <= 4 * DT_MDL
+  assert len(moving) and trace.time[moving[0]] <= 1.0
+  assert trace.solver_failures == 0
+
+
+def test_stop_hold_survives_short_full_field_dropout():
+  def observe(current_time: float, _lead_name: str, truth: LeadObservation) -> LeadObservation | None:
+    return None if 1.0 <= current_time < 1.1 else truth
+
+  common = dict(
+    duration=2.0, controller_enabled=True, lead_relevancy=True, speed=0.0, distance_lead=6.0,
+    v_lead=0.0, v_cruise=8.0, lead_observation_fn=observe, actuator_delay=0.15, actuator_lag=0.20,
+  )
+  baseline = _run(**(common | {"controller_enabled": False}))
+  trace = _run(**common)
+  assert np.max(trace.speed) < 1e-3
+  assert np.all(trace.target_speed == 0.0)
+  assert np.all(trace.state == int(AccelControllerState.stopHold))
+  assert not trace.fcw.any()
+  _assert_no_new_solver_failures(trace, baseline)
+
+
+@pytest.mark.parametrize(("actuator_delay", "actuator_lag"), ACTUATOR_DYNAMICS, ids=ACTUATOR_IDS)
+def test_stopped_lead_requires_four_departure_frames_and_launches_within_one_second(actuator_delay, actuator_lag):
+  departure_time = 1.0
+
+  def lead_speed(current_time: float) -> float:
+    return 0.0 if current_time < departure_time else 2.0
+
+  trace = _run(
+    duration=2.5, controller_enabled=True, lead_relevancy=True, speed=0.0, distance_lead=6.0,
+    v_lead=lead_speed, v_cruise=8.0, actuator_delay=actuator_delay, actuator_lag=actuator_lag,
+  )
+  first_three = (trace.time > departure_time) & (trace.time <= departure_time + 3 * DT_MDL + 1e-9)
+  release = np.flatnonzero((trace.time >= departure_time) & trace.launching)
+  moving = np.flatnonzero((trace.time >= departure_time) & (trace.speed > 0.05))
+
+  assert not trace.launching[first_three].any()
+  assert trace.should_stop[first_three].all()
+  assert len(release) and trace.time[release[0]] >= departure_time + 3 * DT_MDL
+  assert not trace.should_stop[release[0]]
+  assert len(moving) and trace.time[moving[0]] <= departure_time + 3 * DT_MDL + 1.0
+  assert not _has_propulsion_brake_cycle(trace.a_target[trace.time >= departure_time])
+  assert trace.solver_failures == 0
+
+
+def test_reused_radar_frames_do_not_pulse_stop_state_during_departure():
+  departure_time = 1.0
+  trace = _run(
+    duration=3.0, controller_enabled=True, lead_relevancy=True, speed=0.0, distance_lead=6.0,
+    v_lead=lambda current_time: 0.0 if current_time < departure_time else 2.0,
+    v_cruise=8.0, actuator_delay=0.15, actuator_lag=0.25, radar_fresh_fn=lambda frame: frame % 2 == 0,
+  )
+  after_departure = trace.time >= departure_time
+  should_stop = trace.should_stop[after_departure]
+  release = np.flatnonzero(after_departure & trace.launching)
+  moving = np.flatnonzero(after_departure & (trace.speed > 0.05))
+
+  assert np.count_nonzero(np.diff(should_stop.astype(int))) <= 1
+  assert len(release) and len(moving)
+  assert trace.time[moving[0]] <= trace.time[release[0]] + 1.0
+  assert not _has_propulsion_brake_cycle(trace.a_target[after_departure])
+  assert trace.solver_failures == 0
+
+
+@pytest.mark.parametrize("departure_frames", [1, 2, 3])
+def test_short_false_departure_does_not_launch_the_vehicle(departure_frames):
+  trace = _run(
+    duration=2.5, controller_enabled=True, lead_relevancy=True, speed=0.0, distance_lead=6.0,
+    v_lead=lambda current_time: 2.0 if 1.0 <= current_time < 1.0 + departure_frames * DT_MDL else 0.0,
+    v_cruise=8.0, actuator_delay=0.10, actuator_lag=0.20,
+  )
+
+  assert np.max(trace.speed) < 0.01
+  assert not trace.launching.any()
+  assert trace.should_stop.all()
+  assert trace.state[-1] == int(AccelControllerState.stopHold)
+  assert trace.solver_failures == 0
+
+
+@pytest.mark.parametrize(("actuator_delay", "actuator_lag"), ACTUATOR_DYNAMICS, ids=ACTUATOR_IDS)
+def test_matched_lead_recovery_preserves_profile_ordering(actuator_delay, actuator_lag):
+  traces = [
+    _run(
+      duration=32.0, controller_enabled=True, profile=profile, lead_relevancy=True, speed=20.0,
+      distance_lead=100.0, v_lead=10.0, v_cruise=30.0, actuator_delay=actuator_delay, actuator_lag=actuator_lag,
+    )
+    for profile in range(3)
+  ]
+  response = (traces[0].time >= 15.0) & (traces[0].time <= 28.5)
+  mean_accel = [float(np.mean(trace.a_target[response])) for trace in traces]
+  final_speed = [float(trace.speed[np.flatnonzero(response)[-1]]) for trace in traces]
+
+  assert mean_accel[0] + 0.06 < mean_accel[1]
+  assert mean_accel[1] + 0.025 < mean_accel[2]
+  assert final_speed[0] < final_speed[1] < final_speed[2]
+  assert max(final_speed) < 13.5
+  assert all(not _has_propulsion_brake_cycle(trace.a_target[response]) for trace in traces)
+  assert all(trace.solver_failures == 0 for trace in traces)
+
+
+def test_creeping_lead_departure_is_prompt_and_safe():
+  departure_time = 1.0
+
+  def lead_speed(current_time: float) -> float:
+    if current_time < departure_time:
+      return 0.0
+    if current_time < departure_time + 0.5:
+      return 1.6 * (current_time - departure_time)
+    return min(2.5, 0.8 + 0.7 * (current_time - departure_time - 0.5))
+
+  def observe(_current_time: float, lead_name: str, truth: LeadObservation) -> LeadObservation | None:
+    return None if lead_name == "leadTwo" else truth | {"aLeadK": 0.0, "radarTrackId": 2133, "radar": True}
+
+  common = dict(
+    duration=6.0, profile=0, lead_relevancy=True, speed=0.0, distance_lead=3.6, v_lead=lead_speed,
+    v_cruise=22.352, lead_observation_fn=observe, actuator_delay=0.15, actuator_lag=0.20,
+  )
+  baseline = _run(controller_enabled=False, **common)
+  trace = _run(controller_enabled=True, **common)
+  after_departure = trace.time >= departure_time
+  lead_speeds = np.array([lead_speed(max(0.0, current_time - DT_MDL)) for current_time in trace.time])
+  baseline_moving = np.flatnonzero((baseline.time >= departure_time) & (baseline.speed > 0.05))
+  moving = np.flatnonzero(after_departure & (trace.speed > 0.05))
+
+  assert len(baseline_moving) and len(moving)
+  assert trace.time[moving[0]] <= baseline.time[baseline_moving[0]]
+  assert np.all(trace.speed[after_departure] <= lead_speeds[after_departure] + 0.20)
+  assert not _has_brake_coast_brake(trace.a_target[after_departure])
+  assert np.min(trace.distance_lead - trace.distance) >= np.min(baseline.distance_lead - baseline.distance) - 1e-3
+  assert trace.solver_failures == 0
+
+
+def test_constant_creep_departure_does_not_pulse_between_launch_and_stop_hold():
+  departure_time = 1.0
+
+  def lead_speed(current_time: float) -> float:
+    return 0.0 if current_time < departure_time else 0.2
+
+  def observe(_current_time: float, lead_name: str, truth: LeadObservation) -> LeadObservation | None:
+    return None if lead_name == "leadTwo" else truth | {"aLeadK": 0.0, "radarTrackId": 2133, "radar": True}
+
+  trace = _run(
+    duration=8.0, controller_enabled=True, profile=0, lead_relevancy=True, speed=0.0, distance_lead=3.6,
+    v_lead=lead_speed, v_cruise=8.0, lead_observation_fn=observe, actuator_delay=0.15, actuator_lag=0.20,
+  )
+  launched = np.flatnonzero((trace.time >= departure_time) & trace.launching)
+  assert len(launched)
+  after_launch = slice(launched[0], None)
+
+  assert not np.any(trace.state[after_launch] == int(AccelControllerState.stopHold))
+  assert not _has_propulsion_brake_cycle(trace.a_target[after_launch])
+  assert np.max(trace.speed[after_launch]) <= 0.4
+  assert not trace.fcw.any()
+  assert trace.solver_failures == 0
+
+
+def test_invalid_departure_geometry_aborts_launch_until_reconfirmed():
+  def lead_speed(current_time: float) -> float:
+    return 0.0 if current_time < 1.0 else 2.0
+
+  def observe(current_time: float, _lead_name: str, truth: LeadObservation) -> LeadObservation:
+    return truth | {"vLeadK": -2.0} if 1.45 <= current_time < 1.70 else truth
+
+  trace = _run(
+    duration=2.5, controller_enabled=True, lead_relevancy=True, speed=0.0, distance_lead=6.0,
+    v_lead=lead_speed, v_cruise=8.0, lead_observation_fn=observe, actuator_delay=0.15, actuator_lag=0.20,
+  )
+  invalid = (trace.time >= 1.50) & (trace.time < 1.75)
+  assert invalid.any()
+  assert not trace.launching[invalid].any()
+  assert np.max(trace.speed[invalid]) < 0.10
+  assert np.max(trace.target_speed[invalid]) == 0.0
+  assert np.all(np.isfinite(trace.a_target))
+  assert trace.solver_failures == 0
+
+
+def test_moving_full_field_dropout_never_releases_pace_or_adds_solver_failures():
+  dropout_start = 2.0
+  dropout_end = 2.15
+
+  def observe(current_time: float, _lead_name: str, truth: LeadObservation) -> LeadObservation | None:
+    return None if dropout_start <= current_time < dropout_end else truth
+
+  common = dict(
+    duration=4.0, lead_relevancy=True, speed=22.0, distance_lead=85.0, v_lead=14.0, v_cruise=30.0,
+    lead_observation_fn=observe, actuator_delay=0.20, actuator_lag=0.25,
+  )
+  baseline = _run(controller_enabled=False, **common)
+  trace = _run(controller_enabled=True, **common)
+  before = trace.target_speed[np.flatnonzero(trace.time < dropout_start)[-1]]
+  response = (trace.time >= dropout_start) & (trace.time <= dropout_end + 0.5)
+
+  assert np.max(trace.target_speed[response]) <= before + 1e-6
+  assert not _has_propulsion_after_braking(trace.a_target[response])
+  assert not _has_propulsion_brake_cycle(trace.a_target[response])
+  assert np.min(trace.distance_lead - trace.distance) >= np.min(baseline.distance_lead - baseline.distance) - DROPOUT_GAP_TOLERANCE
+  _assert_no_new_solver_failures(trace, baseline)
+
+
+def test_false_range_relief_matches_clean_controller_response():
+  glitch_start = 3.0
+  glitch_end = 3.15
+
+  def observe(current_time: float, _lead_name: str, truth: LeadObservation) -> LeadObservation:
+    return truth | {"dRel": truth["dRel"] + 5.0} if glitch_start <= current_time < glitch_end else truth
+
+  common = dict(
+    duration=4.0, lead_relevancy=True, speed=22.0, distance_lead=85.0, v_lead=14.0,
+    v_cruise=30.0, actuator_delay=0.20, actuator_lag=0.25,
+  )
+  baseline = _run(controller_enabled=False, lead_observation_fn=observe, **common)
+  clean = _run(controller_enabled=True, **common)
+  trace = _run(controller_enabled=True, lead_observation_fn=observe, **common)
+  response = (trace.time >= glitch_start) & (trace.time <= glitch_end + 0.5)
+  jerk_response = (trace.time[1:] >= glitch_start) & (trace.time[1:] <= glitch_end + 0.5)
+
+  assert np.max(np.abs(trace.a_target[response] - clean.a_target[response])) < 0.07
+  assert np.max(np.abs(np.diff(trace.a_target)[jerk_response] / DT_MDL)) < 3.0
+  assert not _has_propulsion_after_braking(trace.a_target[response])
+  assert not _has_propulsion_brake_cycle(trace.a_target[response])
+  _assert_no_new_solver_failures(trace, baseline)
+
+
+@pytest.mark.parametrize("profile", range(3), ids=("eco", "normal", "sport"))
+@pytest.mark.parametrize(("actuator_delay", "actuator_lag"), ACTUATOR_DYNAMICS, ids=ACTUATOR_IDS)
+def test_route_507_braking_lead_slot_switch_has_no_false_relief_cycle(profile, actuator_delay, actuator_lag):
+  glitch_start = 67.0
+  glitch_end = 67.5
+
+  def lead_speed(current_time: float) -> float:
+    braking_time = np.clip(current_time - 60.0, 0.0, 7.0)
+    return 10.0 - 0.42 * braking_time
+
+  def observe(current_time: float, lead_name: str, truth: LeadObservation) -> LeadObservation | None:
+    if glitch_start <= current_time < glitch_end:
+      if lead_name == "leadOne":
+        return None
+      return truth | {
+        "dRel": truth["dRel"] + 20.0,
+        "vLead": truth["vLead"] + 4.0,
+        "vLeadK": truth["vLeadK"] + 4.0,
+        "vRel": truth["vRel"] + 4.0,
+        "aLeadK": 0.0,
+        "radar": True,
+        "radarTrackId": 200,
+      }
+    if lead_name == "leadTwo":
+      return None
+    return truth | {"aLeadK": -0.42 if 60.0 <= current_time < glitch_start else 0.0, "radar": True, "radarTrackId": 100}
+
+  common = dict(
+    duration=73.0, controller_enabled=True, profile=profile, lead_relevancy=True, speed=20.0,
+    distance_lead=100.0, v_lead=lead_speed, v_cruise=30.0, actuator_delay=actuator_delay, actuator_lag=actuator_lag,
+  )
+  clean = _run(**common)
+  trace = _run(lead_observation_fn=observe, **common)
+  response = (trace.time >= 66.0) & (trace.time <= 72.0)
+  jerk_response = (trace.time[1:] >= 66.0) & (trace.time[1:] <= 72.0)
+  clean_gap = clean.distance_lead - clean.distance
+  gap = trace.distance_lead - trace.distance
+
+  assert not _has_propulsion_brake_cycle(trace.a_target[response])
+  assert not _has_brake_coast_brake(trace.a_target[response])
+  assert np.max(np.abs(np.diff(trace.a_target)[jerk_response] / DT_MDL)) < 3.0
+  assert np.max(-np.diff(trace.target_speed)[jerk_response]) <= max(PACE_TARGET_RESERVE, MATCHED_PACE_DECEL_RATE * DT_MDL) + 1e-9
+  assert np.min(gap[response]) >= np.min(clean_gap[response]) - DROPOUT_GAP_TOLERANCE
+  assert not trace.fcw.any()
+  assert trace.solver_failures == 0
+  assert trace.raw_radar_passthrough.all()
+  assert np.all(trace.mpc_calls == 1)
+
+
+@pytest.mark.parametrize("profile", range(3), ids=("eco", "normal", "sport"))
+def test_profile_ceiling_and_pace_stay_smooth_through_slot_switch_noise(profile):
+  glitch_start = 24.0
+  glitch_end = 28.0
+
+  def observe(current_time: float, lead_name: str, truth: LeadObservation) -> LeadObservation | None:
+    if not glitch_start <= current_time < glitch_end:
+      return truth if lead_name == "leadOne" else None
+
+    selected_slot = "leadOne" if round(current_time / DT_MDL) % 2 == 0 else "leadTwo"
+    if lead_name != selected_slot:
+      return None
+    sign = 1.0 if lead_name == "leadOne" else -1.0
+    speed_offset = 0.25 * sign
+    return truth | {
+      "dRel": max(0.0, truth["dRel"] + 1.5 * sign),
+      "vLead": max(0.0, truth["vLead"] + speed_offset),
+      "vLeadK": max(0.0, truth["vLeadK"] + speed_offset),
+      "vRel": truth["vRel"] + speed_offset,
+      "aLeadK": 0.0,
+      "radar": True,
+      "radarTrackId": 100 if lead_name == "leadOne" else 200,
+    }
+
+  common = dict(
+    duration=32.0, controller_enabled=True, profile=profile, lead_relevancy=True, speed=20.0,
+    distance_lead=100.0, v_lead=10.0, v_cruise=30.0, actuator_delay=0.15, actuator_lag=0.25,
+  )
+  baseline = _run(**common)
+  trace = _run(lead_observation_fn=observe, **common)
+  glitch = (trace.time >= glitch_start) & (trace.time < glitch_end)
+  response = (trace.time >= glitch_start - 0.5) & (trace.time <= glitch_end + 1.0)
+  effective_accel_max = trace.effective_accel_max[response]
+  selected_leads = trace.selected_lead[glitch]
+  finite_limits = np.isfinite(effective_accel_max)
+  stock_accel_max = np.asarray([get_max_accel(speed) for speed in trace.speed[response]])
+
+  assert set(selected_leads) == {0, 1}
+  assert np.count_nonzero(np.diff(selected_leads)) > 20
+  assert np.all(effective_accel_max[finite_limits] <= trace.profile_accel_max[response][finite_limits] + 1e-9)
+  assert np.all(effective_accel_max[finite_limits] <= stock_accel_max[finite_limits] + 1e-9)
+  assert np.max(trace.a_target[response]) > 0.2
+  assert not _has_propulsion_brake_cycle(trace.a_target[response])
+  assert not _has_brake_coast_brake(trace.a_target[response])
+  assert np.max(np.abs(_command_jerk(trace)[response[1:]])) < 3.0
+  assert trace.raw_radar_passthrough.all()
+  assert not trace.fcw.any()
+  _assert_no_new_solver_failures(trace, baseline)
+
+
+@pytest.mark.parametrize("profile", range(3), ids=("eco", "normal", "sport"))
+def test_matched_lead_dropout_keeps_the_profile_acceleration_ceiling(profile):
+  dropout_start = 25.0
+  dropout_end = 25.15
+
+  def observe(current_time: float, lead_name: str, truth: LeadObservation) -> LeadObservation | None:
+    if lead_name == "leadTwo" or dropout_start <= current_time < dropout_end:
+      return None
+    return truth
+
+  common = dict(
+    duration=28.0, controller_enabled=True, profile=profile, lead_relevancy=True, speed=20.0,
+    distance_lead=100.0, v_lead=10.0, v_cruise=30.0, actuator_delay=0.15, actuator_lag=0.25,
+  )
+  clean = _run(**common)
+  trace = _run(lead_observation_fn=observe, **common)
+  dropout = (trace.time >= dropout_start) & (trace.time <= dropout_end)
+  response = (trace.time >= dropout_start - 0.5) & (trace.time <= dropout_end + 0.75)
+
+  assert np.all(np.isfinite(trace.effective_accel_max[dropout]))
+  assert np.max(np.abs(trace.a_target[response] - clean.a_target[response])) < 0.08
+  assert np.max(np.abs(_command_jerk(trace)[response[1:]])) < 3.0
+  assert not _has_propulsion_brake_cycle(trace.a_target[response])
+  assert trace.raw_radar_passthrough.all()
+  assert np.all(trace.mpc_calls == 1)
+  assert not trace.fcw.any()
+  _assert_no_new_solver_failures(trace, clean)
+
+
+def test_low_speed_lead_stop_has_no_release_then_rebrake():
+  def lead_speed(current_time: float) -> float:
+    return max(0.0, 1.9 - 1.16 * current_time)
+
+  def observe(current_time: float, lead_name: str, truth: LeadObservation) -> LeadObservation | None:
+    if lead_name == "leadTwo":
+      return None
+    moving = lead_speed(current_time) > 0.0
+    return truth | {"vLeadK": truth["vLeadK"] if moving else -0.01, "aLeadK": -1.16 if moving else 0.0,
+                    "radarTrackId": 7, "radar": True}
+
+  common = dict(
+    duration=6.0, profile=0, lead_relevancy=True, speed=4.5, distance_lead=18.0, v_lead=lead_speed,
+    v_cruise=23.056, lead_observation_fn=observe, actuator_delay=0.15, actuator_lag=0.20,
+  )
+  baseline = _run(controller_enabled=False, **common)
+  trace = _run(controller_enabled=True, **common)
+  stop_hold = trace.state == int(AccelControllerState.stopHold)
+  stopped_response = trace.time >= trace.time[np.flatnonzero(trace.speed < 1e-3)[0]]
+
+  assert stop_hold.any()
+  assert np.max(trace.speed[stopped_response]) <= np.max(baseline.speed[stopped_response]) + 0.01
+  assert np.max(trace.a_target[stopped_response]) <= np.max(baseline.a_target[stopped_response]) + 0.05
+  assert not _has_brake_coast_brake(trace.a_target[trace.time >= 1.0])
+  assert np.min(trace.a_target) >= np.min(baseline.a_target) - ROUTINE_GAP_TOLERANCE
+  assert np.min(trace.distance_lead - trace.distance) >= np.min(baseline.distance_lead - baseline.distance) - ROUTINE_GAP_TOLERANCE
+  assert not trace.fcw.any()
+  assert trace.solver_failures == 0
+
+
+@pytest.mark.parametrize(("actuator_delay", "actuator_lag"), ACTUATOR_DYNAMICS, ids=ACTUATOR_IDS)
+def test_high_speed_stopped_lead_approach_holds_the_completed_stop(actuator_delay, actuator_lag):
+  trace = _run(
+    duration=14.0, controller_enabled=True, profile=1, lead_relevancy=True, speed=20.0,
+    distance_lead=130.0, v_lead=0.0, v_cruise=30.0, actuator_delay=actuator_delay, actuator_lag=actuator_lag,
+  )
+  stopped = np.flatnonzero(trace.speed < 0.05)
+  assert len(stopped)
+  after_stop = slice(stopped[0], None)
+  gap = trace.distance_lead - trace.distance
+
+  assert STOP_DISTANCE <= np.min(gap[after_stop]) <= 25.0
+  assert np.max(trace.speed[after_stop]) < 0.10
+  assert not _has_propulsion_brake_cycle(trace.a_target[after_stop])
+  assert trace.state[-1] == int(AccelControllerState.stopHold)
+  assert trace.raw_radar_passthrough.all()
+  assert np.all(trace.mpc_calls == 1)
+  assert not trace.fcw.any()
+  assert trace.solver_failures == 0
+
+
+def test_decelerating_moving_lead_is_stock_safe_without_propulsion_reversal():
+  def lead_speed(current_time: float) -> float:
+    if current_time < 2.0:
+      return 15.0
+    progress = min((current_time - 2.0) / 6.0, 1.0)
+    return 15.0 - 5.0 * (3.0 * progress**2 - 2.0 * progress**3)
+
+  common = dict(
+    duration=14.0, profile=1, lead_relevancy=True, speed=20.0, distance_lead=110.0,
+    v_lead=lead_speed, v_cruise=30.0, actuator_delay=0.20, actuator_lag=0.25,
+  )
+  baseline = _run(controller_enabled=False, **common)
+  trace = _run(controller_enabled=True, **common)
+  response = trace.time >= 1.0
+  baseline_p95 = float(np.percentile(np.abs(_filtered_realized_jerk(baseline)), 95))
+  trace_p95 = float(np.percentile(np.abs(_filtered_realized_jerk(trace)), 95))
+
+  assert not _has_brake_coast_brake(trace.a_target[response])
+  assert not _has_propulsion_after_braking(trace.a_target[response])
+  assert trace_p95 <= baseline_p95 + 0.02
+  assert np.min(trace.acceleration) >= np.min(baseline.acceleration) - ROUTINE_DECEL_TOLERANCE
+  assert np.min(trace.distance_lead - trace.distance) >= np.min(baseline.distance_lead - baseline.distance) - MOVING_LEAD_GAP_TOLERANCE
+  assert not trace.fcw.any()
+  _assert_no_new_solver_failures(trace, baseline)
+
+
+def test_severe_closing_never_delays_stock_braking_or_reduces_clearance():
+  common = dict(
+    duration=12.0, lead_relevancy=True, speed=20.0, distance_lead=160.0, v_lead=3.5,
+    actuator_delay=0.20, actuator_lag=0.20,
+  )
+  baseline = _run(controller_enabled=False, **common)
+  trace = _run(controller_enabled=True, **common)
+  for threshold in (-1.0, -2.0):
+    assert _first_time_below(trace, threshold) <= _first_time_below(baseline, threshold) + 1e-9
+
+  baseline_gap = baseline.distance_lead - baseline.distance
+  controlled_gap = trace.distance_lead - trace.distance
+  baseline_closing = baseline.speed - 3.5
+  controlled_closing = trace.speed - 3.5
+  baseline_ttc = np.min(baseline_gap[baseline_closing > 0.1] / baseline_closing[baseline_closing > 0.1])
+  controlled_ttc = np.min(controlled_gap[controlled_closing > 0.1] / controlled_closing[controlled_closing > 0.1])
+  onset = (trace.time[1:] > 0.5) & (trace.time[1:] < 3.0)
+
+  assert np.min(controlled_gap) >= np.min(baseline_gap) - 0.02
+  assert controlled_ttc >= baseline_ttc - 0.02
+  assert np.min(controlled_gap) > 0.0
+  assert np.max(np.abs(np.diff(trace.a_target)[onset] / DT_MDL)) < 4.0
+  assert trace.solver_failures == 0
+
+
+@pytest.mark.parametrize(("actuator_delay", "actuator_lag"), ACTUATOR_DYNAMICS, ids=ACTUATOR_IDS)
+def test_far_lead_profiles_start_early_in_order_without_solver_failures(actuator_delay, actuator_lag):
+  common = dict(
+    duration=11.0, lead_relevancy=True, speed=25.0, distance_lead=200.0, v_lead=15.0,
+    actuator_delay=actuator_delay, actuator_lag=actuator_lag,
+  )
+  baseline = _run(controller_enabled=False, **common)
+  traces = [_run(controller_enabled=True, profile=profile, **common) for profile in range(3)]
+  baseline_onset = _sustained_time_below(baseline, -0.10)
+  baseline_jerk_p95 = float(np.percentile(np.abs(_filtered_realized_jerk(baseline)), 95))
+  required_improvement = max(0.002, 0.02 * baseline_jerk_p95)
+  onsets = [_sustained_time_below(trace, -0.10) for trace in traces]
+
+  assert onsets[0] <= baseline_onset - 0.5 + 1e-9
+  assert onsets[1] <= baseline_onset + 1e-9
+  assert onsets[2] <= baseline_onset + 1e-9
+  assert onsets[0] <= onsets[1] + DT_MDL + 1e-9
+  assert onsets[1] <= onsets[2] + DT_MDL + 1e-9
+  first_finite_caps = [trace.raw_cap[np.flatnonzero(np.isfinite(trace.raw_cap))[0]] for trace in traces]
+  assert first_finite_caps[0] < first_finite_caps[1] < first_finite_caps[2]
+  for trace in traces:
+    assert trace.acceleration.min() >= baseline.acceleration.min() - 0.1
+    assert float(np.percentile(np.abs(_filtered_realized_jerk(trace)), 95)) <= baseline_jerk_p95 - required_improvement
+    assert np.max(np.abs(_command_jerk(trace, after=0.5))) < 1.0
+    assert not _has_brake_coast_brake(trace.a_target[trace.time >= 1.0])
+    assert not _has_propulsion_brake_cycle(trace.a_target[trace.time >= 1.0])
+    assert not trace.fcw.any()
+    assert trace.solver_failures == 0
+
+
+def test_steady_slow_lead_has_no_gas_brake_cycle():
+  duration = 60.0
+  lead_speed = 10.0
+  common = dict(
+    duration=duration, lead_relevancy=True, speed=20.0, distance_lead=100.0, v_lead=lead_speed,
+    v_cruise=30.0, actuator_delay=0.15, actuator_lag=0.25,
+  )
+  baseline = _run(controller_enabled=False, **common)
+  trace = _run(controller_enabled=True, **common)
+  response = trace.time >= 1.0
+  settled = trace.time >= duration - 5.0
+  desired_gap = STOP_DISTANCE + get_T_FOLLOW() * lead_speed
+  baseline_gap = baseline.distance_lead - baseline.distance
+  gap = trace.distance_lead - trace.distance
+  max_settled_gap = max(np.mean(baseline_gap[settled]) + 10.0, desired_gap + 30.0)
+
+  assert np.mean(trace.speed[settled]) >= lead_speed - 2.0
+  assert np.mean(gap[settled]) <= max_settled_gap
+  assert not _has_brake_coast_brake(trace.a_target[response])
+  assert not _has_propulsion_brake_cycle(trace.a_target[response])
+  assert np.min(gap) >= desired_gap - 1.6
+  assert np.min(trace.a_target) >= np.min(baseline.a_target) - ROUTINE_GAP_TOLERANCE
+  _assert_no_new_solver_failures(trace, baseline)
+
+
+def test_matched_lead_slowdown_stays_smooth_without_a_second_braking_stage():
+  slowdown_time = 70.0
+  settled_lead_speed = 7.0
+
+  def lead_speed(current_time: float) -> float:
+    return 10.0 if current_time < slowdown_time else max(settled_lead_speed, 10.0 - 0.5 * (current_time - slowdown_time))
+
+  trace = _run(
+    duration=100.0, controller_enabled=True, profile=1, lead_relevancy=True, speed=20.0,
+    distance_lead=100.0, v_lead=lead_speed, v_cruise=30.0, actuator_delay=0.15, actuator_lag=0.25,
+  )
+  matched = (trace.time >= slowdown_time - 5.0) & (trace.time < slowdown_time)
+  response = trace.time >= slowdown_time
+  settled = trace.time >= 95.0
+  gap = trace.distance_lead - trace.distance
+  desired_gap = STOP_DISTANCE + get_T_FOLLOW() * settled_lead_speed
+
+  assert abs(np.mean(trace.speed[matched]) - 10.0) < 0.5
+  np.testing.assert_allclose(trace.effective_accel_max[matched], trace.profile_accel_max[matched], atol=1e-9)
+  assert not np.any(trace.state[response] == int(AccelControllerState.stopHold))
+  assert not trace.launching[response].any()
+  assert not _has_brake_coast_brake(trace.a_target[response])
+  assert not _has_propulsion_brake_cycle(trace.a_target[response])
+  assert np.max(np.abs(_command_jerk(trace)[response[1:]])) < 3.0
+  assert float(np.percentile(np.abs(_filtered_realized_jerk(trace, after=slowdown_time)), 95)) < 0.30
+  assert np.min(gap) > STOP_DISTANCE
+  assert np.mean(trace.speed[settled]) >= settled_lead_speed - 1.5
+  assert np.mean(gap[settled]) <= desired_gap + 20.0
+  assert not trace.fcw.any()
+  assert trace.solver_failures == 0
+
+
+def test_acceleration_output_remains_inside_stock_limits():
+  trace = _run(
+    duration=12.0, controller_enabled=True, profile=AccelProfile.sport, lead_relevancy=False,
+    speed=0.0, v_cruise=30.0, actuator_delay=0.15, actuator_lag=0.20,
+  )
+  stock_max = np.asarray([get_max_accel(speed) for speed in trace.speed])
+  assert np.all(trace.a_target >= ACCEL_MIN - 1e-9)
+  assert np.all(trace.a_target <= stock_max + 0.06)
+  assert trace.stock_bounds_valid.all()
+  assert trace.solver_failures == 0

sunnypilot/selfdrive/controls/lib/tests/test_lane_turn_desire.py

@@ -5,6 +5,8 @@ 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 +109,11 @@ 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
+

sunnypilot/selfdrive/controls/lib/tests/test_relc.py

@@ -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

sunnypilot/selfdrive/selfdrived/events.py

@@ -243,4 +243,12 @@ 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),
+  },
 }

sunnypilot/sunnylink/params_metadata.json

@@ -1,4 +1,26 @@
 {
+  "AccelPersonality": {
+    "title": "Acceleration Profile",
+    "description": "Eco slows earliest and recovers gently, Normal balances comfort and response, and Sport reacts and recovers more quickly.",
+    "options": [
+      {
+        "value": 0,
+        "label": "Eco"
+      },
+      {
+        "value": 1,
+        "label": "Normal"
+      },
+      {
+        "value": 2,
+        "label": "Sport"
+      }
+    ]
+  },
+  "AccelPersonalityEnabled": {
+    "title": "Enable Accel Controller",
+    "description": "Begin slowing early and smoothly behind lead vehicles. Stock longitudinal control retains braking and stopping authority."
+  },
   "AccessToken": {
     "title": "AccessTokenIsNice",
     "description": ""
@@ -1118,6 +1140,10 @@
     "title": "Record Front Lock",
     "description": ""
   },
+  "RoadEdgeLaneChangeEnabled": {
+    "title": "Block Lane Change: Road Edge Detection",
+    "description": ""
+  },
   "RoadName": {
     "title": "Road Name",
     "description": ""
@@ -1323,6 +1349,22 @@
     "max": 5.0,
     "step": 0.1,
     "unit": "m/s\u00b2"
+  },
+    "ToyotaAutoHold": {
+    "title": "Toyota: Auto Brake Hold FOR TSS2 HYBRID CARS",
+    "description": ""
+  },
+    "ToyotaEnhancedBsm": {
+    "title": "Toyota: Prius TSS2 BSM and some tssp",
+    "description": ""
+  },
+    "ToyotaTSS2Long": {
+    "title": "Toyota: custom longitudinal for TSS2",
+    "description": ""
+  },
+    "ToyotaDriveMode": {
+    "title": "Enable drive mode btn link",
+    "description": ""
   },
   "ToyotaEnforceStockLongitudinal": {
     "title": "Toyota: Enforce Factory Longitudinal Control",

sunnypilot/sunnylink/settings_ui.json

@@ -620,6 +620,58 @@
                 }
               ]
             },
+            {
+              "key": "AccelPersonalityEnabled",
+              "widget": "toggle",
+              "title": "Enable Accel Controller",
+              "description": "Begin slowing early and smoothly behind lead vehicles. Stock longitudinal control retains braking and stopping authority.",
+              "visibility": [
+                {
+                  "type": "capability",
+                  "field": "has_longitudinal_control",
+                  "equals": true
+                }
+              ],
+              "enablement": [
+                {
+                  "type": "capability",
+                  "field": "has_longitudinal_control",
+                  "equals": true
+                }
+              ]
+            },
+            {
+              "key": "AccelPersonality",
+              "widget": "multiple_button",
+              "title": "Acceleration Profile",
+              "description": "Eco slows earliest and recovers gently, Normal balances comfort and response, and Sport reacts and recovers more quickly.",
+              "options": [
+                {
+                  "value": 0,
+                  "label": "Eco"
+                },
+                {
+                  "value": 1,
+                  "label": "Normal"
+                },
+                {
+                  "value": 2,
+                  "label": "Sport"
+                }
+              ],
+              "enablement": [
+                {
+                  "type": "capability",
+                  "field": "has_longitudinal_control",
+                  "equals": true
+                },
+                {
+                  "type": "param",
+                  "key": "AccelPersonalityEnabled",
+                  "equals": true
+                }
+              ]
+            },
             {
               "key": "IntelligentCruiseButtonManagement",
               "widget": "toggle",
@@ -2001,6 +2053,22 @@
                   "equals": true
                 }
               ]
+            },
+            {
+              "key": "PlanplusControl",
+              "widget": "option",
+              "title": "Plan Plus Controls",
+              "description": "Adjust planplus model recentering strength. The higher this number the more aggressively the model will recover to lane center; too high and it will ping-pong.",
+              "min": 0.0,
+              "max": 2.0,
+              "step": 0.1,
+              "enablement": [
+                {
+                  "type": "param",
+                  "key": "ShowAdvancedControls",
+                  "equals": true
+                }
+              ]
             }
           ]
         },
@@ -2168,6 +2236,50 @@
       "title": "Toyota / Lexus Settings",
       "description": "",
       "items": [
+        {
+          "key": "ToyotaAutoHold",
+          "widget": "toggle",
+          "needs_onroad_cycle": true,
+          "title": "Toyota: Auto Brake Hold FOR TSS2 HYBRID CARS",
+          "enablement": [
+            {
+              "type": "not_engaged"
+            }
+          ]
+        },
+        {
+          "key": "ToyotaEnhancedBsm",
+          "widget": "toggle",
+          "needs_onroad_cycle": true,
+          "title": "Toyota: Prius TSS2 BSM and some tssp",
+          "enablement": [
+            {
+              "type": "not_engaged"
+            }
+          ]
+        },
+        {
+          "key": "ToyotaTSS2Long",
+          "widget": "toggle",
+          "needs_onroad_cycle": true,
+          "title": "Toyota: custom longitudinal for TSS2",
+          "enablement": [
+            {
+              "type": "not_engaged"
+            }
+          ]
+        },
+        {
+          "key": "ToyotaDriveMode",
+          "widget": "toggle",
+          "needs_onroad_cycle": true,
+          "title": "Enable drive mode btn link",
+          "enablement": [
+            {
+              "type": "not_engaged"
+            }
+          ]
+        },
         {
           "key": "ToyotaEnforceStockLongitudinal",
           "widget": "toggle",

sunnypilot/sunnylink/settings_ui_src/pages/cruise.yaml

@@ -43,6 +43,32 @@ sections:
       label: Relaxed
     enablement:
     - $ref: '#/macros/longitudinal'
+  - key: AccelPersonalityEnabled
+    widget: toggle
+    title: Enable Accel Controller
+    description: Begin slowing early and smoothly behind lead vehicles. Stock longitudinal control retains braking
+      and stopping authority.
+    visibility:
+    - $ref: '#/macros/longitudinal'
+    enablement:
+    - $ref: '#/macros/longitudinal'
+  - key: AccelPersonality
+    widget: multiple_button
+    title: Acceleration Profile
+    description: Eco slows earliest and recovers gently, Normal balances comfort and response, and Sport reacts
+      and recovers more quickly.
+    options:
+    - value: 0
+      label: Eco
+    - value: 1
+      label: Normal
+    - value: 2
+      label: Sport
+    enablement:
+    - $ref: '#/macros/longitudinal'
+    - type: param
+      key: AccelPersonalityEnabled
+      equals: true
   - key: IntelligentCruiseButtonManagement
     widget: toggle
     title: Intelligent Cruise Button Management (ICBM) (Alpha)

sunnypilot/sunnylink/settings_ui_src/pages/models.yaml

@@ -51,6 +51,16 @@ sections:
         key: LagdToggle
         equals: true
     - $ref: '#/macros/advanced_only'
+  - key: PlanplusControl
+    widget: option
+    title: Plan Plus Controls
+    description: Adjust planplus model recentering strength. The higher this number the more aggressively the model will recover
+      to lane center; too high and it will ping-pong.
+    min: 0.0
+    max: 2.0
+    step: 0.1
+    enablement:
+    - $ref: '#/macros/advanced_only'
 - id: lateral_control
   title: Lateral Control
   description: Neural network lateral control for supported models

sunnypilot/sunnylink/settings_ui_src/pages/vehicle.yaml

@@ -60,6 +60,30 @@ sections:
   title: Toyota / Lexus Settings
   description: ''
   items:
+  - key: ToyotaAutoHold
+    widget: toggle
+    needs_onroad_cycle: true
+    title: 'Toyota: Auto Brake Hold FOR TSS2 HYBRID CARS'
+    enablement:
+    - $ref: '#/macros/not_engaged'
+  - key: ToyotaEnhancedBsm
+    widget: toggle
+    needs_onroad_cycle: true
+    title: 'Toyota: Prius TSS2 BSM and some tssp'
+    enablement:
+    - $ref: '#/macros/not_engaged'
+  - key: ToyotaTSS2Long
+    widget: toggle
+    needs_onroad_cycle: true
+    title: 'Toyota: custom longitudinal for TSS2'
+    enablement:
+    - $ref: '#/macros/not_engaged'
+  - key: ToyotaDriveMode
+    widget: toggle
+    needs_onroad_cycle: true
+    title: Enable drive mode btn link
+    enablement:
+    - $ref: '#/macros/not_engaged'
   - key: ToyotaEnforceStockLongitudinal
     widget: toggle
     needs_onroad_cycle: true

sunnypilot/sunnylink/tests/test_settings_schema.py

@@ -272,6 +272,22 @@ class TestKnownPanels:
     nnlc_enable_keys = {r.get("key") for r in nnlc.get("enablement", []) if r.get("type") == "param"}
     assert "EnforceTorqueControl" in nnlc_enable_keys
 
+  def test_accel_controller_profile_mapping_and_enablement(self, schema):
+    cruise = next(p for p in schema["panels"] if p["id"] == "cruise")
+    items = {item["key"]: item for item in _iter_panel_items(cruise)}
+
+    assert items["AccelPersonalityEnabled"]["widget"] == "toggle"
+    assert items["AccelPersonality"]["options"] == [
+      {"value": 0, "label": "Eco"},
+      {"value": 1, "label": "Normal"},
+      {"value": 2, "label": "Sport"},
+    ]
+    assert {
+      "type": "param",
+      "key": "AccelPersonalityEnabled",
+      "equals": True,
+    } in items["AccelPersonality"]["enablement"]
+
 
 class TestKnownVehicleSettings:
   def test_hyundai_has_longitudinal_tuning(self, schema):

system/ui/lib/scroll_panel2.py

@@ -45,8 +45,9 @@ class ScrollState(Enum):
 
 
 class GuiScrollPanel2:
-  def __init__(self, horizontal: bool = True) -> None:
+  def __init__(self, horizontal: bool = True, handle_out_of_bounds: bool = True) -> None:
     self._horizontal = horizontal
+    self._handle_out_of_bounds = handle_out_of_bounds
     self._state = ScrollState.STEADY
     self._offset: rl.Vector2 = rl.Vector2(0, 0)
     self._initial_click_event: MouseEvent | None = None
@@ -98,7 +99,7 @@ class GuiScrollPanel2:
       # simple exponential return if out of bounds
       # out of bounds is handled by snapping, so skip if set
       out_of_bounds = self.get_offset() > max_offset or self.get_offset() < min_offset
-      if out_of_bounds and snap_target is None:
+      if out_of_bounds and snap_target is None and self._handle_out_of_bounds:
         target = max_offset if self.get_offset() > max_offset else min_offset
 
         dt = rl.get_frame_time() or 1e-6

system/ui/widgets/scroller.py

@@ -75,7 +75,6 @@ class _Scroller(Widget):
     self._items: list[Widget] = []
     self._horizontal = horizontal
     self._snap_items = snap_items
-    assert not self._snap_items or self._horizontal, "Snapping is only supported for horizontal scrolling"
     self._spacing = spacing
     self._pad = pad
 
@@ -191,8 +190,12 @@ class _Scroller(Widget):
     snap_target: float | None = None
     if self._snap_items and visible_items and self._scrolling_to[0] is None:
       # TODO: this doesn't handle two small buttons at the edges well
-      center_pos = self._rect.x + self._rect.width / 2
-      closest_delta_pos = min((((item.rect.x + item.rect.width / 2) - center_pos) for item in visible_items), key=abs)
+      if self._horizontal:
+        center_pos = self._rect.x + self._rect.width / 2
+        closest_delta_pos = min((((item.rect.x + item.rect.width / 2) - center_pos) for item in visible_items), key=abs)
+      else:
+        center_pos = self._rect.y + self._rect.height / 2
+        closest_delta_pos = min((((item.rect.y + item.rect.height / 2) - center_pos) for item in visible_items), key=abs)
       snap_target = self.scroll_panel.get_offset() - closest_delta_pos
 
     return self.scroll_panel.update(self._rect, content_size, snap_target=snap_target)