#!/usr/bin/env python3 import math import numpy as np from common.numpy_fast import clip, interp from common.params import Params from cereal import log import cereal.messaging as messaging from common.conversions import Conversions as CV from common.filter_simple import FirstOrderFilter from common.realtime import DT_MDL from selfdrive.hybrid_modeld.constants import T_IDXS from selfdrive.controls.lib.longcontrol import LongCtrlState from selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import LongitudinalMpc, MIN_ACCEL, MAX_ACCEL, STOP_DISTANCE from selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import T_IDXS as T_IDXS_MPC from selfdrive.controls.lib.drive_helpers import V_CRUISE_MAX, CONTROL_N, get_speed_error from system.swaglog import cloudlog from selfdrive.controls.lib.vision_turn_controller import VisionTurnController LON_MPC_STEP = 0.2 # first step is 0.2s A_CRUISE_MIN = -1.2 A_CRUISE_MAX_VALS = [1.6, 1.2, 0.8, 0.6] A_CRUISE_MAX_BP = [0., 10.0, 25., 40.] # Lookup table for turns _A_TOTAL_MAX_V = [1.7, 3.2] _A_TOTAL_MAX_BP = [20., 40.] # d-e2e, from legacy_modeldata.h TRAJECTORY_SIZE = 33 _DP_E2E_LEAD_COUNT = 5 _DP_E2E_STOP_BP = [0., 10., 20., 30., 40., 50., 55.] _DP_E2E_STOP_DIST = [10, 30., 50., 70., 80., 90., 120.] _DP_E2E_STOP_COUNT = 3 _DP_E2E_SNG_COUNT = 3 _DP_E2E_SNG_ACC_COUNT = 5 _DP_E2E_SWAP_COUNT = 10 _DP_E2E_TF_COUNT = 5 def get_max_accel(v_ego): return interp(v_ego, A_CRUISE_MAX_BP, A_CRUISE_MAX_VALS) def limit_accel_in_turns(v_ego, angle_steers, a_target, CP): """ This function returns a limited long acceleration allowed, depending on the existing lateral acceleration this should avoid accelerating when losing the target in turns """ # FIXME: This function to calculate lateral accel is incorrect and should use the VehicleModel # The lookup table for turns should also be updated if we do this a_total_max = interp(v_ego, _A_TOTAL_MAX_BP, _A_TOTAL_MAX_V) a_y = v_ego ** 2 * angle_steers * CV.DEG_TO_RAD / (CP.steerRatio * CP.wheelbase) a_x_allowed = math.sqrt(max(a_total_max ** 2 - a_y ** 2, 0.)) return [a_target[0], min(a_target[1], a_x_allowed)] class LongitudinalPlanner: def __init__(self, CP, init_v=0.0, init_a=0.0): self.vision_turn_controller = VisionTurnController(CP) # conditional e2e self.dp_e2e_has_lead = False self.dp_e2e_lead_last = False self.dp_e2e_lead_count = 0 self.dp_e2e_sng = False self.dp_e2e_sng_count = 0 self.dp_e2e_standstill_last = False self.dp_e2e_swap_count = 0 self.dp_e2e_stop_count = 0 self.dp_e2e_tf_count = 0 self.CP = CP self.mpc = LongitudinalMpc() self.fcw = False self.a_desired = init_a self.v_desired_filter = FirstOrderFilter(init_v, 2.0, DT_MDL) self.v_model_error = 0.0 self.v_desired_trajectory = np.zeros(CONTROL_N) self.a_desired_trajectory = np.zeros(CONTROL_N) self.j_desired_trajectory = np.zeros(CONTROL_N) self.solverExecutionTime = 0.0 self.params = Params() self.param_read_counter = 0 self.read_param() self.personality = log.LongitudinalPersonality.standard def read_param(self): param_value = self.params.get('LongitudinalPersonality') if param_value is not None: self.personality = int(param_value) else: self.personality = log.LongitudinalPersonality.standard def _set_dp_e2e_mode(self, mode, force=False): reset_state = False if force: self.dp_e2e_swap_count = 0 if self.mpc.mode != mode: reset_state = True self.mpc.mode = mode return reset_state # prevent switching in a short period of time. if self.mpc.mode == mode: self.dp_e2e_swap_count = 0 else: self.dp_e2e_swap_count += 1 if self.dp_e2e_swap_count >= _DP_E2E_SWAP_COUNT: self.mpc.mode = mode reset_state = True return reset_state def conditional_e2e(self, sm): if not sm['controlsState'].experimentalMode: return self._set_dp_e2e_mode('acc', True) v_ego_kph = sm['carState'].vEgo * 3.6 standstill = sm['carState'].standstill # lead detection with buffer lead = sm['radarState'].leadOne lead_dist = lead.dRel # make sure it see lead enough time if lead.status != self.dp_e2e_lead_last: self.dp_e2e_lead_count = 0 else: self.dp_e2e_lead_count += 1 if self.dp_e2e_lead_count >= _DP_E2E_LEAD_COUNT: self.dp_e2e_has_lead = lead.status self.dp_e2e_lead_last = lead.status # when standstill, always e2e if standstill: self.dp_e2e_sng_count = 0 self.dp_e2e_sng = False return self._set_dp_e2e_mode('blended') if self.dp_e2e_standstill_last and not standstill: self.dp_e2e_sng = True # when sng, we e2e for 0.5 secs if self.dp_e2e_sng: self.dp_e2e_sng_count += 1 if self.dp_e2e_sng_count > _DP_E2E_SNG_COUNT: if self.dp_e2e_sng_count > _DP_E2E_SNG_ACC_COUNT: self.dp_e2e_sng = False return self._set_dp_e2e_mode('acc', True) return self._set_dp_e2e_mode('blended') # when we see a lead # if sm['dragonConf'].dpE2EConditionalVoacc and self.dp_e2e_has_lead: if self.CP.radarUnavailable and self.dp_e2e_has_lead: # drive above conditional speed and lead is too close if lead_dist <= v_ego_kph * self.mpc.t_follow * interp(v_ego_kph, [50., 60., 80., 85, 90.], [1.25, 1.20, 1.10, 1.05, 1.]) / 3.6: self.dp_e2e_tf_count += 1 else: self.dp_e2e_tf_count = 0 if self.dp_e2e_tf_count > _DP_E2E_TF_COUNT: return self._set_dp_e2e_mode('blended', True) # stop sign detection md = sm['modelV2'] if abs(sm['carState'].steeringAngleDeg) <= 60 and len(md.orientation.x) == len(md.position.x) == TRAJECTORY_SIZE: if md.position.x[TRAJECTORY_SIZE - 1] < interp(v_ego_kph, _DP_E2E_STOP_BP, _DP_E2E_STOP_DIST): self.dp_e2e_stop_count += 1 else: self.dp_e2e_stop_count = 0 else: self.dp_e2e_stop_count = 0 if self.dp_e2e_stop_count >= _DP_E2E_STOP_COUNT: return self._set_dp_e2e_mode('blended', True) return self._set_dp_e2e_mode('acc') @staticmethod def parse_model(model_msg, model_error): if (len(model_msg.position.x) == 33 and len(model_msg.velocity.x) == 33 and len(model_msg.acceleration.x) == 33): x = np.interp(T_IDXS_MPC, T_IDXS, model_msg.position.x) - model_error * T_IDXS_MPC v = np.interp(T_IDXS_MPC, T_IDXS, model_msg.velocity.x) - model_error a = np.interp(T_IDXS_MPC, T_IDXS, model_msg.acceleration.x) j = np.zeros(len(T_IDXS_MPC)) else: x = np.zeros(len(T_IDXS_MPC)) v = np.zeros(len(T_IDXS_MPC)) a = np.zeros(len(T_IDXS_MPC)) j = np.zeros(len(T_IDXS_MPC)) return x, v, a, j def update(self, sm): if self.param_read_counter % 50 == 0: self.read_param() self.param_read_counter += 1 # self.mpc.mode = 'blended' if sm['controlsState'].experimentalMode else 'acc' dp_reset_state = self.conditional_e2e(sm) v_ego = sm['carState'].vEgo v_cruise_kph = sm['controlsState'].vCruise v_cruise_kph = min(v_cruise_kph, V_CRUISE_MAX) v_cruise = v_cruise_kph * CV.KPH_TO_MS 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['controlsState'].enabled # 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 = [A_CRUISE_MIN, get_max_accel(v_ego)] accel_limits_turns = limit_accel_in_turns(v_ego, sm['carState'].steeringAngleDeg, accel_limits, self.CP) else: accel_limits = [MIN_ACCEL, MAX_ACCEL] accel_limits_turns = [MIN_ACCEL, MAX_ACCEL] if reset_state or dp_reset_state: self.v_desired_filter.x = v_ego # Clip aEgo to cruise limits to prevent large accelerations when becoming active self.a_desired = clip(sm['carState'].aEgo, accel_limits[0], accel_limits[1]) # Prevent divergence, smooth in current v_ego self.v_desired_filter.x = max(0.0, self.v_desired_filter.update(v_ego)) # Compute model v_ego error self.v_model_error = get_speed_error(sm['modelV2'], v_ego) # rick - vision turn controller from move-fast team # https://github.com/move-fast/openpilot/blob/develop/selfdrive/controls/lib/vision_turn_controller.py self.vision_turn_controller.update(not reset_state, self.v_desired_filter.x, self.a_desired, v_cruise, sm) if self.vision_turn_controller.is_active: if min(v_cruise, self.vision_turn_controller.v_turn) == self.vision_turn_controller.v_turn: self.a_desired = self.vision_turn_controller.a_target v_cruise = self.vision_turn_controller.v_turn 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) self.mpc.set_weights(prev_accel_constraint, personality=self.personality) 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) x, v, a, j = self.parse_model(sm['modelV2'], self.v_model_error) # dynamic stopping distance ONLY when on radarUnavailable vehicles (e.g. Toyota C-HR, VW) stop_distance = STOP_DISTANCE if not self.CP.radarUnavailable else interp(sm['carState'].vEgo, [0., 2.78, 5.55, 22.], [3.7, 4., 5, STOP_DISTANCE]) self.mpc.update(sm['radarState'], v_cruise, x, v, a, j, personality=self.personality, stop_distance=stop_distance) self.v_desired_trajectory_full = np.interp(T_IDXS, T_IDXS_MPC, self.mpc.v_solution) self.a_desired_trajectory_full = np.interp(T_IDXS, T_IDXS_MPC, self.mpc.a_solution) self.v_desired_trajectory = self.v_desired_trajectory_full[:CONTROL_N] self.a_desired_trajectory = self.a_desired_trajectory_full[:CONTROL_N] self.j_desired_trajectory = np.interp(T_IDXS[:CONTROL_N], 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") self._set_dp_e2e_mode('blended', True) # Interpolate 0.05 seconds and save as starting point for next iteration a_prev = self.a_desired self.a_desired = float(interp(DT_MDL, T_IDXS[:CONTROL_N], self.a_desired_trajectory)) self.v_desired_filter.x = self.v_desired_filter.x + DT_MDL * (self.a_desired + a_prev) / 2.0 def publish(self, sm, pm): plan_send = messaging.new_message('longitudinalPlan') plan_send.valid = sm.all_checks(service_list=['carState', 'controlsState']) longitudinalPlan = plan_send.longitudinalPlan longitudinalPlan.modelMonoTime = sm.logMonoTime['modelV2'] longitudinalPlan.processingDelay = (plan_send.logMonoTime / 1e9) - sm.logMonoTime['modelV2'] 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.longitudinalValid = self.mpc.mode == 'acc' longitudinalPlan.solverExecutionTime = self.mpc.solve_time longitudinalPlan.personality = self.personality pm.send('longitudinalPlan', plan_send)