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174 lines
7.1 KiB
174 lines
7.1 KiB
#!/usr/bin/env python3
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import math
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import numpy as np
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from common.numpy_fast import clip, interp
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from common.params import Params
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from cereal import log
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import cereal.messaging as messaging
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from common.conversions import Conversions as CV
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from common.filter_simple import FirstOrderFilter
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from common.realtime import DT_MDL
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from selfdrive.modeld.constants import T_IDXS
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from selfdrive.controls.lib.longcontrol import LongCtrlState
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from selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import LongitudinalMpc, MIN_ACCEL, MAX_ACCEL
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from selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import T_IDXS as T_IDXS_MPC
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from selfdrive.controls.lib.drive_helpers import V_CRUISE_MAX, CONTROL_N, get_speed_error
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from system.swaglog import cloudlog
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LON_MPC_STEP = 0.2 # first step is 0.2s
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A_CRUISE_MIN = -1.2
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A_CRUISE_MAX_VALS = [1.6, 1.2, 0.8, 0.6]
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A_CRUISE_MAX_BP = [0., 10.0, 25., 40.]
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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 interp(v_ego, A_CRUISE_MAX_BP, A_CRUISE_MAX_VALS)
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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 = 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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class LongitudinalPlanner:
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def __init__(self, CP, init_v=0.0, init_a=0.0):
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self.CP = CP
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self.mpc = LongitudinalMpc()
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self.fcw = False
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self.a_desired = init_a
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self.v_desired_filter = FirstOrderFilter(init_v, 2.0, DT_MDL)
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self.v_model_error = 0.0
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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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self.params = Params()
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self.param_read_counter = 0
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self.read_param()
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self.personality = log.LongitudinalPersonality.standard
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def read_param(self):
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try:
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self.personality = int(self.params.get('LongitudinalPersonality'))
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except (ValueError, TypeError):
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self.personality = log.LongitudinalPersonality.standard
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@staticmethod
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def parse_model(model_msg, model_error):
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if (len(model_msg.position.x) == 33 and
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len(model_msg.velocity.x) == 33 and
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len(model_msg.acceleration.x) == 33):
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x = np.interp(T_IDXS_MPC, T_IDXS, model_msg.position.x) - model_error * T_IDXS_MPC
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v = np.interp(T_IDXS_MPC, T_IDXS, model_msg.velocity.x) - model_error
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a = np.interp(T_IDXS_MPC, 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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return x, v, a, j
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def update(self, sm):
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if self.param_read_counter % 50 == 0:
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self.read_param()
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self.param_read_counter += 1
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self.mpc.mode = 'blended' if sm['controlsState'].experimentalMode else 'acc'
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v_ego = sm['carState'].vEgo
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v_cruise_kph = sm['controlsState'].vCruise
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v_cruise_kph = min(v_cruise_kph, V_CRUISE_MAX)
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v_cruise = v_cruise_kph * CV.KPH_TO_MS
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long_control_off = sm['controlsState'].longControlState == LongCtrlState.off
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force_slow_decel = sm['controlsState'].forceDecel
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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['controlsState'].enabled
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# No change cost when user is controlling the speed, or when standstill
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prev_accel_constraint = not (reset_state or sm['carState'].standstill)
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if self.mpc.mode == 'acc':
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accel_limits = [A_CRUISE_MIN, get_max_accel(v_ego)]
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accel_limits_turns = limit_accel_in_turns(v_ego, sm['carState'].steeringAngleDeg, accel_limits, self.CP)
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else:
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accel_limits = [MIN_ACCEL, MAX_ACCEL]
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accel_limits_turns = [MIN_ACCEL, MAX_ACCEL]
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if reset_state:
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self.v_desired_filter.x = v_ego
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# Clip aEgo to cruise limits to prevent large accelerations when becoming active
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self.a_desired = clip(sm['carState'].aEgo, accel_limits[0], accel_limits[1])
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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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# Compute model v_ego error
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self.v_model_error = get_speed_error(sm['modelV2'], v_ego)
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if force_slow_decel:
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v_cruise = 0.0
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# clip limits, cannot init MPC outside of bounds
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accel_limits_turns[0] = min(accel_limits_turns[0], self.a_desired + 0.05)
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accel_limits_turns[1] = max(accel_limits_turns[1], self.a_desired - 0.05)
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self.mpc.set_weights(prev_accel_constraint, personality=self.personality)
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self.mpc.set_accel_limits(accel_limits_turns[0], accel_limits_turns[1])
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self.mpc.set_cur_state(self.v_desired_filter.x, self.a_desired)
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x, v, a, j = self.parse_model(sm['modelV2'], self.v_model_error)
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self.mpc.update(sm['radarState'], v_cruise, x, v, a, j, personality=self.personality)
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self.v_desired_trajectory_full = np.interp(T_IDXS, T_IDXS_MPC, self.mpc.v_solution)
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self.a_desired_trajectory_full = np.interp(T_IDXS, T_IDXS_MPC, self.mpc.a_solution)
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self.v_desired_trajectory = self.v_desired_trajectory_full[:CONTROL_N]
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self.a_desired_trajectory = self.a_desired_trajectory_full[:CONTROL_N]
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self.j_desired_trajectory = np.interp(T_IDXS[:CONTROL_N], 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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# Interpolate 0.05 seconds and save as starting point for next iteration
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a_prev = self.a_desired
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self.a_desired = float(interp(DT_MDL, T_IDXS[:CONTROL_N], self.a_desired_trajectory))
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self.v_desired_filter.x = self.v_desired_filter.x + DT_MDL * (self.a_desired + 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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plan_send.valid = sm.all_checks(service_list=['carState', 'controlsState'])
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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.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['radarState'].leadOne.status
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longitudinalPlan.longitudinalPlanSource = self.mpc.source
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longitudinalPlan.fcw = self.fcw
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longitudinalPlan.solverExecutionTime = self.mpc.solve_time
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longitudinalPlan.personality = self.personality
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pm.send('longitudinalPlan', plan_send)
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