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firestar5683
681 lines
31 KiB
Python
Executable File
681 lines
31 KiB
Python
Executable File
#!/usr/bin/env python3
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import math
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import numpy as np
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import time
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import cereal.messaging as messaging
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from opendbc.car.interfaces import ACCEL_MIN, ACCEL_MAX
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from openpilot.common.constants import CV
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from openpilot.common.filter_simple import FirstOrderFilter
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from openpilot.common.realtime import DT_MDL
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from openpilot.selfdrive.modeld.constants import ModelConstants
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from openpilot.selfdrive.controls.lib.longcontrol import LongCtrlState
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from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import LongitudinalMpc
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from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import desired_follow_distance
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from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import STOP_DISTANCE
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from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import T_IDXS as T_IDXS_MPC
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from openpilot.selfdrive.controls.lib.drive_helpers import CONTROL_N
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from openpilot.selfdrive.car.cruise import V_CRUISE_UNSET
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from openpilot.common.swaglog import cloudlog
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LON_MPC_STEP = 0.2 # first step is 0.2s
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A_CRUISE_MIN = -1.0
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A_CRUISE_MAX_BP = [0.0, 5., 10., 15., 20., 25., 40.]
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A_CRUISE_MAX_VALS = [1.125, 1.125, 1.125, 1.125, 1.25, 1.25, 1.5]
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CONTROL_N_T_IDX = ModelConstants.T_IDXS[:CONTROL_N]
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ALLOW_THROTTLE_THRESHOLD = 0.4
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ALLOW_THROTTLE_HYSTERESIS = 0.05
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ALLOW_THROTTLE_ENABLE_THRESHOLD = ALLOW_THROTTLE_THRESHOLD + ALLOW_THROTTLE_HYSTERESIS
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ALLOW_THROTTLE_DISABLE_THRESHOLD = ALLOW_THROTTLE_THRESHOLD - ALLOW_THROTTLE_HYSTERESIS
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MIN_ALLOW_THROTTLE_SPEED = 2.5
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RAW_LEAD_SAFETY_MIN_CLOSING_SPEED = 0.5
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RAW_LEAD_SAFETY_TTC = 7.0
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RAW_LEAD_SAFETY_DISTANCE = 40.0
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CLOSE_LEAD_BRAKE_CAP_MAX_TTC = 25.0
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VISION_LEAD_APPROACH_MIN_CLOSING_SPEED = 2.0
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VISION_LEAD_APPROACH_TRIGGER_TIME = 4.0
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VISION_LEAD_APPROACH_FULL_TIME = 1.0
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VISION_LEAD_APPROACH_TIGHT_BUFFER = 2.0
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VISION_LEAD_APPROACH_MAX_DECEL = 0.45
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VISION_LEAD_APPROACH_MIN_DECEL = 0.12
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VISION_LEAD_APPROACH_MIN_MODEL_PROB = 0.85
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VISION_LEAD_APPROACH_FULL_MODEL_PROB = 0.98
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LEAD_APPROACH_TFOLLOW_TRIGGER_TIME = 4.5
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LEAD_APPROACH_TFOLLOW_FULL_TIME = 1.5
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LEAD_APPROACH_TFOLLOW_MAX_DELTA = 0.18
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LEAD_APPROACH_TFOLLOW_MAX_CLOSING_SPEED = 6.0
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LEAD_APPROACH_TFOLLOW_MAX_LEAD_BRAKE = 2.5
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LEAD_APPROACH_TFOLLOW_MIN_CLOSING_SPEED = 0.75
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LEAD_APPROACH_TFOLLOW_MIN_LEAD_BRAKE = 0.2
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LEAD_APPROACH_TFOLLOW_WINDOW_MIN = 6.0
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LEAD_APPROACH_TFOLLOW_WINDOW_GAIN = 0.35
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LEAD_APPROACH_TFOLLOW_RATE_UP = 1.0
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LEAD_APPROACH_TFOLLOW_RATE_DOWN = 0.18
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# Uncertainty-based filter disable thresholds
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UNCERT_SLOPE_TRIG = 0.12 # per second
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UNCERT_MAG_TRIG = 0.50
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# Lookup table for turns
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_A_TOTAL_MAX_V = [1.7, 3.2]
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_A_TOTAL_MAX_BP = [20., 40.]
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def get_longitudinal_personality(sm):
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return sm['selfdriveState'].personality
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def get_max_accel(v_ego):
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return np.interp(v_ego, A_CRUISE_MAX_BP, A_CRUISE_MAX_VALS)
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def get_coast_accel(pitch):
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return np.sin(pitch) * -5.65 - 0.3 # fitted from data using xx/projects/allow_throttle/compute_coast_accel.py
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def limit_accel_in_turns(v_ego, angle_steers, a_target, CP):
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"""
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This function returns a limited long acceleration allowed, depending on the existing lateral acceleration
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this should avoid accelerating when losing the target in turns
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"""
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# FIXME: This function to calculate lateral accel is incorrect and should use the VehicleModel
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# The lookup table for turns should also be updated if we do this
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a_total_max = np.interp(v_ego, _A_TOTAL_MAX_BP, _A_TOTAL_MAX_V)
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a_y = v_ego ** 2 * angle_steers * CV.DEG_TO_RAD / (CP.steerRatio * CP.wheelbase)
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a_x_allowed = math.sqrt(max(a_total_max ** 2 - a_y ** 2, 0.))
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return [a_target[0], min(a_target[1], a_x_allowed)]
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def get_vehicle_min_accel(CP, v_ego):
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# Planner-side physical decel capability estimate for GM pedal-long paths.
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if getattr(CP, "carName", "") == "gm" and getattr(CP, "enableGasInterceptorDEPRECATED", False):
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try:
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from opendbc.car.gm.values import GMFlags, CAR
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if bool(CP.flags & GMFlags.PEDAL_LONG.value):
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bolt_pedal_long_cars = {
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CAR.CHEVROLET_BOLT_CC_2017,
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CAR.CHEVROLET_BOLT_CC_2018_2021,
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CAR.CHEVROLET_BOLT_ACC_2022_2023_PEDAL,
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CAR.CHEVROLET_BOLT_CC_2022_2023,
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CAR.CHEVROLET_MALIBU_HYBRID_CC,
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}
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if CP.carFingerprint in bolt_pedal_long_cars:
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return float(np.interp(v_ego, [0.0, 1.5, 4.0, 8.0, 15.0, 30.0],
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[-0.93, -1.28, -1.98, -2.58, -2.86, -2.95]))
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return float(np.interp(v_ego, [0.0, 1.5, 4.0, 8.0, 15.0, 30.0],
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[-0.95, -1.3, -1.85, -2.3, -2.6, -2.8]))
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except Exception:
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pass
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return float(ACCEL_MIN)
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def get_planner_v_ego(CP, car_state):
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v_ego = max(car_state.vEgo, car_state.vEgoCluster)
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is_gm = getattr(CP, "carName", "") == "gm" or getattr(CP, "brand", "") == "gm"
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if is_gm and getattr(CP, "enableGasInterceptorDEPRECATED", False):
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try:
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from opendbc.car.gm.values import GMFlags
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is_gm_pedal_long = bool(CP.flags & GMFlags.PEDAL_LONG.value)
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if is_gm_pedal_long:
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return float(car_state.vEgo)
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except Exception:
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pass
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return float(v_ego)
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def get_accel_from_plan_classic(CP, speeds, accels, vEgoStopping):
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if len(speeds) == CONTROL_N:
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v_target_now = np.interp(DT_MDL, CONTROL_N_T_IDX, speeds)
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a_target_now = np.interp(DT_MDL, CONTROL_N_T_IDX, accels)
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v_target = np.interp(CP.longitudinalActuatorDelay + DT_MDL, CONTROL_N_T_IDX, speeds)
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if v_target != v_target_now:
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a_target = 2 * (v_target - v_target_now) / CP.longitudinalActuatorDelay - a_target_now
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else:
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a_target = a_target_now
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v_target_1sec = np.interp(CP.longitudinalActuatorDelay + DT_MDL + 1.0, CONTROL_N_T_IDX, speeds)
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else:
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v_target = 0.0
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v_target_1sec = 0.0
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a_target = 0.0
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should_stop = (v_target < vEgoStopping and
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v_target_1sec < vEgoStopping)
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return a_target, should_stop
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def get_accel_from_plan(speeds, accels, action_t=DT_MDL, vEgoStopping=0.05):
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if len(speeds) == CONTROL_N:
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v_now = speeds[0]
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a_now = accels[0]
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v_target = np.interp(action_t, CONTROL_N_T_IDX, speeds)
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a_target = 2 * (v_target - v_now) / (action_t) - a_now
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v_target_1sec = np.interp(action_t + 1.0, CONTROL_N_T_IDX, speeds)
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else:
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v_target = 0.0
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v_target_1sec = 0.0
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a_target = 0.0
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should_stop = (v_target < vEgoStopping and
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v_target_1sec < vEgoStopping)
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return a_target, should_stop
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class LongitudinalPlanner:
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def __init__(self, CP, init_v=0.0, init_a=0.0, dt=DT_MDL):
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self.CP = CP
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self.mpc = LongitudinalMpc(dt=dt)
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self.fcw = False
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self.dt = dt
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self.model_allow_throttle = True
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self.allow_throttle = True
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self.mode = 'acc'
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self.generation = None
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self.a_desired = init_a
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self.v_desired_filter = FirstOrderFilter(init_v, 2.0, self.dt)
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self.v_model_error = 0.0
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self.output_a_target = 0.0
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self.output_should_stop = False
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self.v_desired_trajectory = np.zeros(CONTROL_N)
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self.a_desired_trajectory = np.zeros(CONTROL_N)
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self.j_desired_trajectory = np.zeros(CONTROL_N)
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self.solverExecutionTime = 0.0
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# ---- Rubberband mitigation state ----
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# Two uncertainty tracks (slow/fast) for asymmetric gating
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self.uncert_slow = FirstOrderFilter(0.0, 1.6, self.dt) # ~lam=0.6
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self.uncert_fast = FirstOrderFilter(0.0, 0.9, self.dt) # faster cool-down for accel decisions
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# Lead stability tracking
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self.prev_lead_dist = None
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self.last_big_brake_t = 0.0
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self.stable_lead = False
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# Smoothed lead distance
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self.lead_dist_f = None
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# Uncertainty slope tracking
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self._uncert_last = 0.0
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self._uncert_last_t = None
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self.effective_t_follow = None
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@property
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def mlsim(self):
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return self.generation in ("v8", "v10", "v11", "v12")
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def get_mpc_mode(self) -> str:
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if not self.mlsim:
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return self.mode
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return getattr(self.mpc, 'mode', 'acc')
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@staticmethod
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def get_model_speed_error(model_msg, v_ego):
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try:
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if len(model_msg.temporalPose.trans):
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return float(np.clip(model_msg.temporalPose.trans[0] - v_ego, -5.0, 5.0))
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except AttributeError:
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pass
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return 0.0
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@staticmethod
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def parse_model(model_msg, model_error, v_ego, starpilot_toggles):
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if (len(model_msg.position.x) == ModelConstants.IDX_N and
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len(model_msg.velocity.x) == ModelConstants.IDX_N and
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len(model_msg.acceleration.x) == ModelConstants.IDX_N):
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x = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.position.x) - model_error * T_IDXS_MPC
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v = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.velocity.x) - model_error
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a = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.acceleration.x)
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j = np.zeros(len(T_IDXS_MPC))
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else:
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x = np.zeros(len(T_IDXS_MPC))
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v = np.zeros(len(T_IDXS_MPC))
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a = np.zeros(len(T_IDXS_MPC))
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j = np.zeros(len(T_IDXS_MPC))
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if starpilot_toggles.taco_tune:
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max_lat_accel = np.interp(v_ego, [5, 10, 20], [1.5, 2.0, 3.0])
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curvatures = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.orientationRate.z) / np.clip(v, 0.3, 100.0)
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max_v = np.sqrt(max_lat_accel / (np.abs(curvatures) + 1e-3)) - 2.0
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v = np.minimum(max_v, v)
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if len(model_msg.meta.disengagePredictions.gasPressProbs) > 1:
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throttle_prob = model_msg.meta.disengagePredictions.gasPressProbs[1]
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else:
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throttle_prob = 1.0
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return x, v, a, j, throttle_prob
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def get_close_lead_brake_cap(self, lead, v_ego, accel_min):
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if lead is None or not lead.status:
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return None
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lead_brake = max(0.0, -float(lead.aLeadK))
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reaction_t = max(self.CP.longitudinalActuatorDelay, self.dt)
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closing_speed = max(0.0, v_ego - lead.vLead)
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projected_closing_speed = closing_speed + lead_brake * reaction_t
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if projected_closing_speed < 0.1 and lead_brake < 0.5:
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return None
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target_gap = float(np.clip(2.0 + 0.2 * v_ego, 2.0, 6.0))
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delay_buffer = projected_closing_speed * reaction_t
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available_gap = max(float(lead.dRel) - target_gap - delay_buffer, 0.5)
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projected_ttc = available_gap / max(projected_closing_speed, 0.1)
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if projected_ttc > CLOSE_LEAD_BRAKE_CAP_MAX_TTC:
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return None
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required_decel = (projected_closing_speed ** 2) / (2.0 * available_gap) + 0.7 * lead_brake
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if required_decel < 0.2:
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return None
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return max(accel_min, -required_decel)
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def get_vision_lead_approach_cap(self, lead, v_ego, accel_min, t_follow):
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if lead is None or not lead.status or bool(getattr(lead, "radar", False)):
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return None
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lead_prob = float(getattr(lead, "modelProb", 0.0))
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if lead_prob < VISION_LEAD_APPROACH_MIN_MODEL_PROB:
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return None
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lead_brake = max(0.0, -float(lead.aLeadK))
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reaction_t = max(self.CP.longitudinalActuatorDelay, self.dt)
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closing_speed = max(0.0, v_ego - lead.vLead)
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projected_closing_speed = closing_speed + lead_brake * reaction_t
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if projected_closing_speed < VISION_LEAD_APPROACH_MIN_CLOSING_SPEED:
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return None
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tight_follow_gap = float(t_follow * v_ego + VISION_LEAD_APPROACH_TIGHT_BUFFER)
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gap_to_tight_follow = float(lead.dRel) - tight_follow_gap
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time_to_tight_follow = gap_to_tight_follow / max(projected_closing_speed, 0.1)
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if time_to_tight_follow > VISION_LEAD_APPROACH_TRIGGER_TIME:
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return None
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desired_gap = float(desired_follow_distance(v_ego, lead.vLead, t_follow))
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if float(lead.dRel) > desired_gap + VISION_LEAD_APPROACH_TIGHT_BUFFER:
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return None
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time_factor = float(np.clip((VISION_LEAD_APPROACH_TRIGGER_TIME - time_to_tight_follow) /
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(VISION_LEAD_APPROACH_TRIGGER_TIME - VISION_LEAD_APPROACH_FULL_TIME), 0.0, 1.0))
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prob_factor = float(np.clip((lead_prob - VISION_LEAD_APPROACH_MIN_MODEL_PROB) /
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(VISION_LEAD_APPROACH_FULL_MODEL_PROB - VISION_LEAD_APPROACH_MIN_MODEL_PROB), 0.0, 1.0))
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approach_decel = VISION_LEAD_APPROACH_MAX_DECEL * time_factor * prob_factor
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if approach_decel < VISION_LEAD_APPROACH_MIN_DECEL:
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return None
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return max(accel_min, -approach_decel)
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def get_dynamic_t_follow(self, base_t_follow, lead, v_ego):
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base_t_follow = float(base_t_follow)
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target_t_follow = base_t_follow
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if lead is not None and lead.status:
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lead_prob = float(getattr(lead, "modelProb", 1.0 if bool(getattr(lead, "radar", False)) else 0.0))
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if bool(getattr(lead, "radar", False)) or lead_prob >= VISION_LEAD_APPROACH_MIN_MODEL_PROB:
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lead_brake = max(0.0, -float(lead.aLeadK))
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closing_speed = max(0.0, v_ego - lead.vLead)
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if closing_speed >= LEAD_APPROACH_TFOLLOW_MIN_CLOSING_SPEED or lead_brake >= LEAD_APPROACH_TFOLLOW_MIN_LEAD_BRAKE:
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desired_gap = float(desired_follow_distance(v_ego, lead.vLead, base_t_follow))
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approach_window = max(LEAD_APPROACH_TFOLLOW_WINDOW_MIN, LEAD_APPROACH_TFOLLOW_WINDOW_GAIN * float(v_ego))
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if float(lead.dRel) <= desired_gap + approach_window:
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reaction_t = max(self.CP.longitudinalActuatorDelay, self.dt)
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projected_closing_speed = closing_speed + 0.5 * lead_brake * reaction_t
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gap_to_follow = max(float(lead.dRel) - desired_gap, 0.0)
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time_to_follow = gap_to_follow / max(projected_closing_speed, 0.1)
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time_factor = float(np.clip((LEAD_APPROACH_TFOLLOW_TRIGGER_TIME - time_to_follow) /
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(LEAD_APPROACH_TFOLLOW_TRIGGER_TIME - LEAD_APPROACH_TFOLLOW_FULL_TIME), 0.0, 1.0))
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closing_factor = float(np.clip(closing_speed / LEAD_APPROACH_TFOLLOW_MAX_CLOSING_SPEED, 0.0, 1.0))
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brake_factor = float(np.clip(lead_brake / LEAD_APPROACH_TFOLLOW_MAX_LEAD_BRAKE, 0.0, 1.0))
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target_delta = LEAD_APPROACH_TFOLLOW_MAX_DELTA * np.clip(
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0.55 * time_factor + 0.25 * closing_factor + 0.20 * brake_factor, 0.0, 1.0)
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target_t_follow = base_t_follow + float(target_delta)
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if self.effective_t_follow is None:
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self.effective_t_follow = base_t_follow
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rate = LEAD_APPROACH_TFOLLOW_RATE_UP if target_t_follow > self.effective_t_follow else LEAD_APPROACH_TFOLLOW_RATE_DOWN
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step = rate * self.dt
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self.effective_t_follow = float(np.clip(target_t_follow, self.effective_t_follow - step, self.effective_t_follow + step))
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self.effective_t_follow = max(base_t_follow, self.effective_t_follow)
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return self.effective_t_follow
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@staticmethod
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def raw_close_lead_needs_control(lead, v_ego):
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if lead is None or not lead.status:
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return False
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closing_speed = float(v_ego - lead.vLead)
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lead_braking = float(lead.aLeadK) < -0.5
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if closing_speed <= RAW_LEAD_SAFETY_MIN_CLOSING_SPEED and not lead_braking:
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return False
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d_rel = max(float(lead.dRel), 0.0)
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dynamic_distance = max(RAW_LEAD_SAFETY_DISTANCE, 3.0 * float(v_ego))
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ttc = d_rel / max(closing_speed, 0.1) if closing_speed > 0.1 else float("inf")
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return d_rel < dynamic_distance and (ttc < RAW_LEAD_SAFETY_TTC or lead_braking)
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def update(self, sm, starpilot_toggles):
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||
self.generation = getattr(starpilot_toggles, "model_version", None)
|
||
self.mode = 'blended' if sm['selfdriveState'].experimentalMode else 'acc'
|
||
self.mpc.mode = 'acc'
|
||
if not self.mlsim:
|
||
self.mpc.mode = self.mode
|
||
|
||
if len(sm['carControl'].orientationNED) == 3:
|
||
accel_coast = get_coast_accel(sm['carControl'].orientationNED[1])
|
||
else:
|
||
accel_coast = ACCEL_MAX
|
||
|
||
v_ego = get_planner_v_ego(self.CP, sm['carState'])
|
||
v_cruise = sm['starpilotPlan'].vCruise
|
||
if not np.isfinite(v_cruise):
|
||
cloudlog.error(f"Longitudinal planner received non-finite vCruise={v_cruise}, falling back to v_ego={v_ego:.2f}")
|
||
v_cruise = max(v_ego, 0.0)
|
||
v_cruise_initialized = sm['carState'].vCruise != V_CRUISE_UNSET
|
||
|
||
long_control_off = sm['controlsState'].longControlState == LongCtrlState.off
|
||
force_slow_decel = sm['controlsState'].forceDecel
|
||
|
||
# Reset current state when not engaged, or user is controlling the speed
|
||
reset_state = long_control_off if self.CP.openpilotLongitudinalControl else not sm['selfdriveState'].enabled
|
||
# PCM cruise speed may be updated a few cycles later, check if initialized
|
||
reset_state = reset_state or not v_cruise_initialized
|
||
|
||
# No change cost when user is controlling the speed, or when standstill
|
||
prev_accel_constraint = not (reset_state or sm['carState'].standstill)
|
||
|
||
if self.mpc.mode == 'acc':
|
||
accel_limits = [sm['starpilotPlan'].minAcceleration, sm['starpilotPlan'].maxAcceleration]
|
||
steer_angle_without_offset = sm['carState'].steeringAngleDeg - sm['liveParameters'].angleOffsetDeg
|
||
accel_limits_turns = limit_accel_in_turns(v_ego, steer_angle_without_offset, accel_limits, self.CP)
|
||
accel_limits_turns[0] = max(get_vehicle_min_accel(self.CP, v_ego), accel_limits_turns[0])
|
||
else:
|
||
accel_limits = [ACCEL_MIN, ACCEL_MAX]
|
||
accel_limits_turns = [ACCEL_MIN, ACCEL_MAX]
|
||
|
||
if reset_state:
|
||
self.v_desired_filter.x = v_ego
|
||
# Clip aEgo to cruise limits to prevent large accelerations when becoming active
|
||
self.a_desired = np.clip(sm['carState'].aEgo, accel_limits[0], accel_limits[1])
|
||
self.model_allow_throttle = True
|
||
|
||
# Prevent divergence, smooth in current v_ego
|
||
self.v_desired_filter.x = max(0.0, self.v_desired_filter.update(v_ego))
|
||
# Compute model v_ego error
|
||
self.v_model_error = self.get_model_speed_error(sm['modelV2'], v_ego)
|
||
x, v, a, j, throttle_prob = self.parse_model(sm['modelV2'], self.v_model_error, v_ego, starpilot_toggles)
|
||
# Don't clip at low speeds since throttle_prob doesn't account for creep. Use
|
||
# hysteresis here because raw gasPressProb noise can chatter the throttle gate.
|
||
if v_ego <= MIN_ALLOW_THROTTLE_SPEED:
|
||
self.model_allow_throttle = True
|
||
elif self.model_allow_throttle:
|
||
self.model_allow_throttle = throttle_prob > ALLOW_THROTTLE_DISABLE_THRESHOLD
|
||
else:
|
||
self.model_allow_throttle = throttle_prob > ALLOW_THROTTLE_ENABLE_THRESHOLD
|
||
self.allow_throttle = self.model_allow_throttle and not sm['starpilotPlan'].disableThrottle
|
||
|
||
if not self.allow_throttle:
|
||
clipped_accel_coast = max(accel_coast, accel_limits_turns[0])
|
||
clipped_accel_coast_interp = np.interp(v_ego, [MIN_ALLOW_THROTTLE_SPEED, MIN_ALLOW_THROTTLE_SPEED*2], [accel_limits_turns[1], clipped_accel_coast])
|
||
accel_limits_turns[1] = min(accel_limits_turns[1], clipped_accel_coast_interp)
|
||
no_throttle_output_max = accel_limits_turns[1]
|
||
|
||
if force_slow_decel:
|
||
v_cruise = 0.0
|
||
# clip limits, cannot init MPC outside of bounds
|
||
accel_limits_turns[0] = min(accel_limits_turns[0], self.a_desired + 0.05)
|
||
accel_limits_turns[1] = max(accel_limits_turns[1], self.a_desired - 0.05)
|
||
|
||
tracking_lead = bool(sm['starpilotPlan'].trackingLead)
|
||
self.lead_one = sm['radarState'].leadOne
|
||
self.lead_two = sm['radarState'].leadTwo
|
||
raw_close_lead_control = any(self.raw_close_lead_needs_control(lead, v_ego) for lead in (self.lead_one, self.lead_two))
|
||
# StarPilot trackingLead is debounce/model-length based. Keep a raw close-lead
|
||
# safety path so ACC/chill does not ignore a visible lead during that debounce.
|
||
lead_control_active = tracking_lead or raw_close_lead_control
|
||
lead_one_active = bool(self.lead_one.status and lead_control_active)
|
||
effective_t_follow = self.get_dynamic_t_follow(sm['starpilotPlan'].tFollow, self.lead_one if lead_one_active else None, v_ego)
|
||
|
||
lead_dist = self.lead_one.dRel if lead_one_active else 50.0
|
||
|
||
# Smooth lead distance (EMA) to avoid chatter in thresholds
|
||
alpha = max(0.02, min(0.15, 0.05 + 0.002 * v_ego))
|
||
if self.lead_dist_f is None:
|
||
self.lead_dist_f = float(lead_dist)
|
||
else:
|
||
self.lead_dist_f += alpha * (float(lead_dist) - self.lead_dist_f)
|
||
|
||
# Lead stability estimation and recent-brake timer
|
||
now_t = time.monotonic()
|
||
# relative speed (ego - lead) positive when closing
|
||
v_rel = (v_ego - self.lead_one.vLead) if lead_one_active else 0.0
|
||
if self.prev_lead_dist is None:
|
||
d_rel_dot = 0.0
|
||
else:
|
||
d_rel_dot = (lead_dist - self.prev_lead_dist) / max(self.dt, 1e-3)
|
||
self.prev_lead_dist = lead_dist
|
||
|
||
# Remember time of last non-trivial model brake risk
|
||
if 'raw_brake_max' in locals() and raw_brake_max is not None and raw_brake_max > 0.02:
|
||
self.last_big_brake_t = now_t
|
||
|
||
# Stable lead heuristic (short window, cheap to compute)
|
||
recently_braked = (now_t - self.last_big_brake_t) < 0.7
|
||
self.stable_lead = (
|
||
lead_one_active and
|
||
abs(v_rel) < 0.5 and
|
||
abs(d_rel_dot) < 0.5 and
|
||
not recently_braked
|
||
)
|
||
|
||
# Calculate scene uncertainty from model desire prediction entropy and disengage predictions
|
||
uncertainty = 0.0
|
||
if hasattr(sm['modelV2'], 'meta'):
|
||
# Desire prediction entropy (maneuver uncertainty), normalized to [0, 1]
|
||
desire_entropy = 0.0
|
||
if hasattr(sm['modelV2'].meta, 'desirePrediction'):
|
||
desire_probs = sm['modelV2'].meta.desirePrediction
|
||
if len(desire_probs) > 1:
|
||
probs = np.asarray(desire_probs, dtype=float)
|
||
total = float(np.sum(probs))
|
||
if total > 1e-6:
|
||
p = probs / total
|
||
entropy = -np.sum(p * np.log(p + 1e-10))
|
||
max_entropy = np.log(len(p))
|
||
desire_entropy = float(entropy / max(max_entropy, 1e-6)) # normalized entropy in [0,1]
|
||
else:
|
||
desire_entropy = 0.0 # guard against all-zero vector
|
||
|
||
# Disengage prediction risk (intervention likelihood)
|
||
disengage_risk = 0.0
|
||
raw_brake_max = -1.0
|
||
lam = -1.0
|
||
if hasattr(sm['modelV2'].meta, 'disengagePredictions'):
|
||
# Use brake press probabilities as primary risk indicator
|
||
brake_probs = sm['modelV2'].meta.disengagePredictions.brakePressProbs
|
||
if len(brake_probs) > 0:
|
||
# Exponentially decayed max over the full horizon
|
||
probs = np.asarray(brake_probs, dtype=float)
|
||
# Clip tiny brake blips so they don't inflate uncertainty
|
||
if float(np.max(probs)) < 0.015:
|
||
probs = probs * 0.5
|
||
raw_brake_max = float(np.max(probs))
|
||
# Time vector assuming model horizon step = DT_MDL
|
||
t = np.arange(len(probs), dtype=float) * DT_MDL
|
||
lam = 0.6 # decay rate per second (tunable: 0.5–0.9 typical)
|
||
weights = np.exp(-lam * t)
|
||
disengage_risk = float(np.max(probs * weights))
|
||
|
||
# Combined uncertainty metric (range roughly 0..2), with dual-track filtering
|
||
raw_uncertainty = desire_entropy + disengage_risk
|
||
# Update filters
|
||
self.uncert_slow.update(raw_uncertainty)
|
||
self.uncert_fast.update(raw_uncertainty)
|
||
# Use a more permissive track for accel decisions
|
||
uncertainty = self.uncert_slow.x
|
||
uncertainty_accel = min(self.uncert_slow.x, self.uncert_fast.x)
|
||
|
||
# --- Slope-based panic bypass ---
|
||
if self._uncert_last_t is None:
|
||
uncert_slope = 0.0
|
||
else:
|
||
dt_u = max(1e-3, now_t - self._uncert_last_t)
|
||
uncert_slope = (uncertainty - self._uncert_last) / dt_u
|
||
self._uncert_last = uncertainty
|
||
self._uncert_last_t = now_t
|
||
|
||
closing_fast = lead_one_active and (v_ego - self.lead_one.vLead) > 0.5
|
||
# Trigger if either slope is high or magnitude is high; require a valid lead and closing
|
||
panic_bypass = closing_fast and (uncert_slope > UNCERT_SLOPE_TRIG or uncertainty >= UNCERT_MAG_TRIG)
|
||
|
||
if panic_bypass:
|
||
try:
|
||
cloudlog.error(f"LON_SLOPE; slope={uncert_slope:.3f}/s; uncertainty={uncertainty:.3f}; v_ego={v_ego:.2f}; v_rel={(v_ego - self.lead_one.vLead) if lead_one_active else 0.0:.2f}; lead_dist={self.lead_dist_f if self.lead_dist_f is not None else -1:.2f}; trigger=True")
|
||
except Exception:
|
||
pass
|
||
|
||
personality = get_longitudinal_personality(sm)
|
||
|
||
self.mpc.set_weights(sm['starpilotPlan'].accelerationJerk,
|
||
sm['starpilotPlan'].dangerJerk,
|
||
sm['starpilotPlan'].speedJerk,
|
||
prev_accel_constraint,
|
||
personality=personality,
|
||
v_ego=v_ego,
|
||
lead_dist=self.lead_dist_f if lead_one_active and self.lead_dist_f is not None else 50.0,
|
||
uncertainty=uncertainty,
|
||
panic_bypass=panic_bypass)
|
||
self.mpc.set_accel_limits(accel_limits_turns[0], accel_limits_turns[1])
|
||
self.mpc.set_cur_state(self.v_desired_filter.x, self.a_desired)
|
||
# After deciding the MPC mode via get_mpc_mode(), ensure MPC uses that mode when not mlsim
|
||
dec_mpc_mode = self.get_mpc_mode()
|
||
if not self.mlsim:
|
||
self.mpc.mode = dec_mpc_mode
|
||
self.mpc.update(sm['radarState'], v_cruise, x, v, a, j,
|
||
sm['starpilotPlan'].dangerFactor, effective_t_follow,
|
||
personality=personality, tracking_lead=lead_control_active)
|
||
|
||
self.a_desired_trajectory_full = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC, self.mpc.a_solution)
|
||
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)
|
||
self.j_desired_trajectory = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC[:-1], self.mpc.j_solution)
|
||
|
||
# TODO counter is only needed because radar is glitchy, remove once radar is gone
|
||
self.fcw = self.mpc.crash_cnt > 2 and not sm['carState'].standstill
|
||
if self.fcw:
|
||
cloudlog.info("FCW triggered")
|
||
|
||
# Safety checks for rubber-banding mitigation
|
||
max_jerk = np.max(np.abs(self.mpc.j_solution))
|
||
max_accel_change = np.max(np.abs(np.diff(self.mpc.a_solution)))
|
||
if max_jerk > 5.0: # m/s^3
|
||
cloudlog.warning(f"High jerk detected: {max_jerk:.2f} m/s^3")
|
||
if max_accel_change > 2.0: # m/s^2
|
||
cloudlog.warning(f"High acceleration change: {max_accel_change:.2f} m/s^2")
|
||
|
||
# Interpolate 0.05 seconds and save as starting point for next iteration
|
||
a_prev = self.a_desired
|
||
self.a_desired = float(np.interp(self.dt, CONTROL_N_T_IDX, self.a_desired_trajectory))
|
||
self.v_desired_filter.x = self.v_desired_filter.x + self.dt * (self.a_desired + a_prev) / 2.0
|
||
|
||
# Anticipatory pre-brake to avoid "coming in hot" when closing on a lead
|
||
if lead_one_active:
|
||
rel_v = max(0.0, v_ego - self.lead_one.vLead)
|
||
# dynamic time headway adds a small buffer when uncertainty is elevated
|
||
base_th = max(1.6, effective_t_follow)
|
||
th = base_th + 0.6 * max(0.0, uncertainty - 0.42)
|
||
desired_gap = th * v_ego
|
||
if (self.lead_dist_f is not None and self.lead_dist_f < desired_gap and rel_v > 0.5):
|
||
k_rel, k_unc = 0.04, 0.20
|
||
pre_brake = k_rel * rel_v + k_unc * max(0.0, uncertainty - 0.42)
|
||
pre_brake = min(pre_brake, 0.06)
|
||
self.a_desired = float(self.a_desired - pre_brake)
|
||
|
||
# Small deadzone around zero accel to kill micro-dithers
|
||
if -0.05 < self.a_desired < 0.05:
|
||
self.a_desired = 0.0
|
||
|
||
classic_model = bool(getattr(starpilot_toggles, "classic_model", False))
|
||
tinygrad_model = bool(getattr(starpilot_toggles, "tinygrad_model", False))
|
||
experimental_mlsim = bool(tinygrad_model and self.mlsim and self.mode != 'acc')
|
||
action_t = self.CP.longitudinalActuatorDelay + DT_MDL
|
||
|
||
if classic_model:
|
||
output_a_target, output_should_stop = get_accel_from_plan_classic(
|
||
self.CP, self.v_desired_trajectory, self.a_desired_trajectory, starpilot_toggles.vEgoStopping)
|
||
elif tinygrad_model:
|
||
output_a_target_mpc, output_should_stop_mpc = get_accel_from_plan(
|
||
self.v_desired_trajectory, self.a_desired_trajectory,
|
||
action_t=action_t, vEgoStopping=starpilot_toggles.vEgoStopping)
|
||
output_a_target_e2e = sm['modelV2'].action.desiredAcceleration
|
||
output_should_stop_e2e = sm['modelV2'].action.shouldStop
|
||
|
||
if self.mode == 'acc' or self.generation == 'v9':
|
||
output_a_target = output_a_target_mpc
|
||
output_should_stop = output_should_stop_mpc
|
||
else:
|
||
output_a_target = min(output_a_target_mpc, output_a_target_e2e)
|
||
output_should_stop = output_should_stop_e2e or output_should_stop_mpc
|
||
else:
|
||
output_a_target, output_should_stop = get_accel_from_plan(
|
||
self.v_desired_trajectory, self.a_desired_trajectory,
|
||
action_t=action_t, vEgoStopping=starpilot_toggles.vEgoStopping)
|
||
|
||
output_accel_min = get_vehicle_min_accel(self.CP, v_ego) if experimental_mlsim else accel_limits_turns[0]
|
||
|
||
close_lead_caps = []
|
||
if lead_control_active:
|
||
for lead in (self.lead_one, self.lead_two):
|
||
cap = self.get_close_lead_brake_cap(lead, v_ego, output_accel_min)
|
||
if cap is not None:
|
||
close_lead_caps.append(cap)
|
||
approach_cap = self.get_vision_lead_approach_cap(lead, v_ego, output_accel_min, effective_t_follow)
|
||
if approach_cap is not None:
|
||
close_lead_caps.append(approach_cap)
|
||
if close_lead_caps:
|
||
close_lead_brake_cap = min(close_lead_caps)
|
||
self.a_desired = min(self.a_desired, close_lead_brake_cap)
|
||
output_a_target = min(output_a_target, close_lead_brake_cap)
|
||
|
||
if lead_control_active and sm['carState'].standstill:
|
||
moving_leads = [lead for lead in (self.lead_one, self.lead_two)
|
||
if lead.status and lead.vLead > 0.0 and lead.dRel >= STOP_DISTANCE - 0.5]
|
||
if moving_leads:
|
||
output_a_target = max(output_a_target, 0.3)
|
||
|
||
if lead_control_active and np.isfinite(v_cruise) and any(lead.status for lead in (self.lead_one, self.lead_two)):
|
||
# Keep follow/catchup behavior from pulling past the cruise target. Using the
|
||
# same action horizon as the planner preserves normal accel farther below set speed.
|
||
cruise_accel_cap = (v_cruise - v_ego + 0.01) / max(action_t, self.dt)
|
||
output_a_target = min(output_a_target, cruise_accel_cap)
|
||
|
||
output_accel_max = no_throttle_output_max if not self.allow_throttle else accel_limits_turns[1]
|
||
output_a_target = float(np.clip(output_a_target, output_accel_min, output_accel_max))
|
||
|
||
self.output_a_target = output_a_target
|
||
self.output_should_stop = bool(output_should_stop)
|
||
|
||
def publish(self, sm, pm):
|
||
plan_send = messaging.new_message('longitudinalPlan')
|
||
|
||
plan_send.valid = sm.all_checks(service_list=['carState', 'controlsState', 'selfdriveState', 'radarState'])
|
||
|
||
longitudinalPlan = plan_send.longitudinalPlan
|
||
longitudinalPlan.modelMonoTime = sm.logMonoTime['modelV2']
|
||
longitudinalPlan.processingDelay = (plan_send.logMonoTime / 1e9) - sm.logMonoTime['modelV2']
|
||
longitudinalPlan.solverExecutionTime = self.mpc.solve_time
|
||
|
||
longitudinalPlan.speeds = self.v_desired_trajectory.tolist()
|
||
longitudinalPlan.accels = self.a_desired_trajectory.tolist()
|
||
longitudinalPlan.jerks = self.j_desired_trajectory.tolist()
|
||
|
||
longitudinalPlan.hasLead = sm['radarState'].leadOne.status
|
||
longitudinalPlan.longitudinalPlanSource = self.mpc.source
|
||
longitudinalPlan.fcw = self.fcw
|
||
|
||
longitudinalPlan.aTarget = float(self.output_a_target)
|
||
longitudinalPlan.shouldStop = bool(self.output_should_stop) or sm['starpilotPlan'].forcingStopLength < 1
|
||
longitudinalPlan.allowBrake = True
|
||
longitudinalPlan.allowThrottle = bool(self.allow_throttle)
|
||
|
||
pm.send('longitudinalPlan', plan_send)
|