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278 lines
13 KiB
Python
Executable File
278 lines
13 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 cereal.messaging as messaging
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from iqdbc.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, LongitudinalPlanSource
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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, DEFAULT_STOPPING_SPEED, get_accel_from_plan
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from openpilot.selfdrive.car.cruise import V_CRUISE_MAX, V_CRUISE_UNSET
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from openpilot.common.swaglog import cloudlog
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from openpilot.common.issue_debug import log_issue_limited
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from openpilot.iqpilot.selfdrive.controls.lib.longitudinal_planner import LongitudinalPlannerIQ
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A_CRUISE_MAX_VALS = [2.0, 1.6, 0.8, 0.6]
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A_CRUISE_MAX_BP = [0., 10.0, 25., 40.]
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A_CRUISE_MIN = -1.2
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J_CRUISE = 1.0
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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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MIN_ALLOW_THROTTLE_SPEED = 2.5
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LAUNCH_DISARM_SPEED = 2.0
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LAUNCH_COMMIT_T = 3.5
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LAUNCH_MOVING_SPEED = 1.2
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LAUNCH_MAX_ACCEL = 1.5
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E2E_CRUISE_CONVERGENCE_TAU = 15.0
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E2E_CRUISE_ACCEL_MAX = 0.5
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E2E_MODEL_SPEED_HORIZON = 5.0
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E2E_ACCEL_INTENT_BP = [-0.05, 0.05]
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E2E_MODEL_SPEED_INTENT_BP = [-0.5, 0.0]
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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_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 get_lead_distance(radarState):
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if radarState.leadOne.status and (not radarState.leadTwo.status or radarState.leadOne.dRel < radarState.leadTwo.dRel):
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return radarState.leadOne.dRel
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if radarState.leadTwo.status:
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return radarState.leadTwo.dRel
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return 0
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def get_cruise_accel(e2e, v_cruise, v_ego, a_cruise_prev, angle_steers, CP, dt, accel_coast, allow_throttle):
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max_accel = ACCEL_MAX if e2e else get_max_accel(v_ego)
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if not e2e:
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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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max_accel = min(max_accel, a_x_allowed)
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if not allow_throttle:
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clipped_accel_coast = max(accel_coast, ACCEL_MIN)
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coast_limit = np.interp(v_ego, [MIN_ALLOW_THROTTLE_SPEED, MIN_ALLOW_THROTTLE_SPEED*2], [max_accel, clipped_accel_coast])
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max_accel = min(max_accel, coast_limit)
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target_accel = np.clip(v_cruise - v_ego, A_CRUISE_MIN, max_accel)
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if not e2e:
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target_accel = float(np.clip(target_accel, a_cruise_prev - J_CRUISE * dt, a_cruise_prev + J_CRUISE * dt))
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cruise_should_stop = v_cruise == 0.0
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return target_accel, cruise_should_stop
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def get_e2e_accel(v_ego, v_cruise, model_v, a_target, should_stop):
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if should_stop or v_cruise <= v_ego or len(model_v) != len(T_IDXS_MPC):
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return a_target
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convergence_accel = min((v_cruise - v_ego) / E2E_CRUISE_CONVERGENCE_TAU, E2E_CRUISE_ACCEL_MAX)
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if convergence_accel <= a_target:
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return a_target
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# Only help the model converge to cruise when both its immediate action and
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# velocity trajectory show no active deceleration intent. The lead MPC and
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# cruise candidates remain hard upper bounds on the final acceleration.
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accel_intent = np.interp(a_target, E2E_ACCEL_INTENT_BP, [0.0, 1.0])
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model_speed = np.interp(E2E_MODEL_SPEED_HORIZON, T_IDXS_MPC, model_v)
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speed_intent = np.interp(model_speed - v_ego, E2E_MODEL_SPEED_INTENT_BP, [0.0, 1.0])
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return float(np.interp(min(accel_intent, speed_intent), [0.0, 1.0], [a_target, convergence_accel]))
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class LongitudinalPlanner(LongitudinalPlannerIQ):
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def __init__(self, CP, CP_IQ, init_v=0.0, init_a=0.0, dt=DT_MDL):
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self.CP = CP
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self.stopping_speed = CP_IQ.longitudinalStoppingSpeedOverride or DEFAULT_STOPPING_SPEED
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self.mpc = LongitudinalMpc(dt=dt)
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LongitudinalPlannerIQ.__init__(self, self.CP, CP_IQ, self.mpc)
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self.fcw = False
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self.dt = dt
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self.allow_throttle = True
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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.a_cruise = 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.launch_armed = 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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@staticmethod
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def parse_model(model_msg):
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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)
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v = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.velocity.x)
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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 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 update(self, sm):
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LongitudinalPlannerIQ.update(self, sm)
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if len(sm['carControl'].orientationNED) == 3:
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accel_coast = get_coast_accel(sm['carControl'].orientationNED[1])
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else:
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accel_coast = ACCEL_MAX
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v_ego = sm['carState'].vEgo
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v_cruise_kph = min(sm['carState'].vCruise, V_CRUISE_MAX)
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v_cruise = v_cruise_kph * CV.KPH_TO_MS
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if sm['controlsState'].forceDecel:
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v_cruise = 0.0
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long_control_off = sm['controlsState'].longControlState == LongCtrlState.off
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# Reset current state when not engaged, or user is controlling the speed
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reset_state = long_control_off if self.CP.openpilotLongitudinalControl else not sm['selfdriveState'].enabled
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# PCM cruise speed may be updated a few cycles later, check if initialized
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v_cruise_initialized = sm['carState'].vCruise != V_CRUISE_UNSET
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reset_state = reset_state or not v_cruise_initialized
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steer_angle_without_offset = sm['carState'].steeringAngleDeg - sm['liveParameters'].angleOffsetDeg
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if reset_state:
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self.v_desired_filter.x = v_ego
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self.a_desired = np.clip(sm['carState'].aEgo, ACCEL_MIN, ACCEL_MAX)
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# Prevent divergence, smooth in current v_ego
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self.v_desired_filter.x = max(0.0, self.v_desired_filter.update(v_ego))
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_, model_v, model_a, _, throttle_prob = self.parse_model(sm['modelV2'])
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# Don't clip at low speeds since throttle_prob doesn't account for creep
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self.allow_throttle = throttle_prob > ALLOW_THROTTLE_THRESHOLD or v_ego <= MIN_ALLOW_THROTTLE_SPEED
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# Get new v_cruise and a_desired from Smart Cruise Control and Speed Limit Assist
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v_cruise, self.a_desired = LongitudinalPlannerIQ.update_targets(self, sm, self.v_desired_filter.x, self.a_desired, v_cruise)
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if sm['controlsState'].forceDecel:
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v_cruise = 0.0
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personality = sm['selfdriveState'].personality
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self.mpc.set_weights(personality=personality)
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self.mpc.set_cur_state(self.v_desired_filter.x, self.a_desired)
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self.mpc.update(sm['modelV2'], sm['radarState'], personality=personality)
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self.v_desired_trajectory = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC, self.mpc.v_solution)
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self.a_desired_trajectory = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC, self.mpc.a_solution)
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self.j_desired_trajectory = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC[:-1], self.mpc.j_solution)
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# TODO counter is only needed because radar is glitchy, remove once radar is gone
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self.fcw = self.mpc.crash_cnt > 2 and not sm['carState'].standstill
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if self.fcw:
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cloudlog.info("FCW triggered")
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# Save starting point for next iteration
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a_prev = self.a_desired
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action_t = self.CP.longitudinalActuatorDelay + DT_MDL
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output_a_target_mpc, output_should_stop_mpc = get_accel_from_plan(self.v_desired_trajectory, self.a_desired_trajectory, CONTROL_N_T_IDX,
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action_t=action_t, stopping_speed=self.stopping_speed)
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output_a_target_e2e = sm['modelV2'].action.desiredAcceleration
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output_should_stop_e2e = sm['modelV2'].action.shouldStop
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output_a_target_e2e, output_should_stop_e2e = self.apply_e2e_stop_distance(sm, v_ego, output_a_target_e2e, output_should_stop_e2e)
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if self.is_e2e(sm):
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output_a_target_e2e = get_e2e_accel(v_ego, v_cruise, model_v, output_a_target_e2e, output_should_stop_e2e)
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if sm['carState'].standstill:
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self.launch_armed = True
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elif v_ego > LAUNCH_DISARM_SPEED:
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self.launch_armed = False
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if (self.launch_armed and self.is_e2e(sm) and not output_should_stop_e2e and
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np.interp(LAUNCH_COMMIT_T, T_IDXS_MPC, model_v) > LAUNCH_DISARM_SPEED):
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t_cut = min(float(T_IDXS_MPC[np.argmax(model_v > LAUNCH_MOVING_SPEED)]), LAUNCH_COMMIT_T)
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t_shifted = T_IDXS_MPC + t_cut
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v_shifted = np.interp(t_shifted, T_IDXS_MPC, model_v)
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a_shifted = np.interp(t_shifted, T_IDXS_MPC, model_a)
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a_launch = get_accel_from_plan(v_shifted, a_shifted, T_IDXS_MPC, action_t=action_t)[0]
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a_launch_max = np.interp(v_ego, [LAUNCH_MOVING_SPEED, LAUNCH_DISARM_SPEED], [LAUNCH_MAX_ACCEL, 0.])
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output_a_target_e2e = max(output_a_target_e2e, min(a_launch, a_launch_max))
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e2e = self.is_e2e(sm)
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self.a_cruise, cruise_should_stop = get_cruise_accel(e2e, v_cruise, v_ego, self.a_cruise,
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steer_angle_without_offset, self.CP, self.dt,
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accel_coast, self.allow_throttle)
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candidates = [(output_a_target_mpc, self.mpc.source, output_should_stop_mpc),
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(self.a_cruise, LongitudinalPlanSource.cruise, cruise_should_stop)]
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if e2e:
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candidates.append((output_a_target_e2e, LongitudinalPlanSource.e2e, output_should_stop_e2e))
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output_a_target, self.mpc.source, _ = min(candidates, key=lambda c: c[0])
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self.output_should_stop = any(should_stop for _, _, should_stop in candidates)
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self.output_should_stop = self.output_should_stop or self.forcing_stop
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self.output_a_target = np.clip(output_a_target, ACCEL_MIN, ACCEL_MAX)
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self.a_desired = float(self.output_a_target)
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self.v_desired_filter.x = self.v_desired_filter.x + self.dt * (self.output_a_target + a_prev) / 2.0
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def publish(self, sm, pm):
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plan_send = messaging.new_message('longitudinalPlan')
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gate_services = ['carState', 'controlsState', 'selfdriveState', 'radarState']
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plan_send.valid = sm.all_checks(service_list=gate_services)
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if not plan_send.valid:
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log_issue_limited(
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"longitudinal_plan_invalid",
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"planner",
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f"longitudinalPlan invalid alive={ {s: sm.alive[s] for s in gate_services} } "
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f"freq_ok={ {s: sm.freq_ok[s] for s in gate_services} } valid={ {s: sm.valid[s] for s in gate_services} } "
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f"subchecks=({sm.all_alive(gate_services)},{sm.all_freq_ok(gate_services)},{sm.all_valid(gate_services)}) "
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f"recheck={sm.all_checks(service_list=gate_services)}",
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interval_sec=5.0,
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)
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longitudinalPlan = plan_send.longitudinalPlan
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longitudinalPlan.modelMonoTime = sm.logMonoTime['modelV2']
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longitudinalPlan.processingDelay = (plan_send.logMonoTime / 1e9) - sm.logMonoTime['modelV2']
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longitudinalPlan.solverExecutionTime = self.mpc.solve_time
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longitudinalPlan.speeds = self.v_desired_trajectory.tolist()
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longitudinalPlan.accels = self.a_desired_trajectory.tolist()
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longitudinalPlan.jerks = self.j_desired_trajectory.tolist()
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longitudinalPlan.hasLead = (sm['modelV2'].leadsV3[0].prob > 0.5) if self.mpc.new_lead_mpc else sm['radarState'].leadOne.status
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longitudinalPlan.leadDistance = get_lead_distance(sm['radarState'])
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longitudinalPlan.longitudinalPlanSource = self.mpc.source
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longitudinalPlan.fcw = self.fcw
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longitudinalPlan.leadTrajectoryX0 = self.mpc.lead_xv_0[:, 0].tolist()
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longitudinalPlan.leadTrajectoryV0 = self.mpc.lead_xv_0[:, 1].tolist()
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longitudinalPlan.leadTrajectoryX1 = self.mpc.lead_xv_1[:, 0].tolist()
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longitudinalPlan.leadTrajectoryV1 = self.mpc.lead_xv_1[:, 1].tolist()
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longitudinalPlan.aTarget = float(self.output_a_target)
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longitudinalPlan.shouldStop = bool(self.output_should_stop)
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longitudinalPlan.allowBrake = True
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longitudinalPlan.allowThrottle = bool(self.allow_throttle)
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pm.send('longitudinalPlan', plan_send)
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self.publish_longitudinal_plan_iq(sm, pm)
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