import os import math import numpy as np from common.realtime import sec_since_boot, DT_MDL from common.numpy_fast import interp from selfdrive.swaglog import cloudlog from selfdrive.controls.lib.lateral_mpc import libmpc_py from selfdrive.controls.lib.drive_helpers import MPC_COST_LAT, MPC_N, CAR_ROTATION_RADIUS from selfdrive.controls.lib.lane_planner import LanePlanner, TRAJECTORY_SIZE from selfdrive.config import Conversions as CV from common.params import Params import cereal.messaging as messaging from cereal import log LaneChangeState = log.PathPlan.LaneChangeState LaneChangeDirection = log.PathPlan.LaneChangeDirection LOG_MPC = os.environ.get('LOG_MPC', False) LANE_CHANGE_SPEED_MIN = 45 * CV.MPH_TO_MS LANE_CHANGE_TIME_MAX = 10. DESIRES = { LaneChangeDirection.none: { LaneChangeState.off: log.PathPlan.Desire.none, LaneChangeState.preLaneChange: log.PathPlan.Desire.none, LaneChangeState.laneChangeStarting: log.PathPlan.Desire.none, LaneChangeState.laneChangeFinishing: log.PathPlan.Desire.none, }, LaneChangeDirection.left: { LaneChangeState.off: log.PathPlan.Desire.none, LaneChangeState.preLaneChange: log.PathPlan.Desire.none, LaneChangeState.laneChangeStarting: log.PathPlan.Desire.laneChangeLeft, LaneChangeState.laneChangeFinishing: log.PathPlan.Desire.laneChangeLeft, }, LaneChangeDirection.right: { LaneChangeState.off: log.PathPlan.Desire.none, LaneChangeState.preLaneChange: log.PathPlan.Desire.none, LaneChangeState.laneChangeStarting: log.PathPlan.Desire.laneChangeRight, LaneChangeState.laneChangeFinishing: log.PathPlan.Desire.laneChangeRight, }, } class PathPlanner(): def __init__(self, CP): self.LP = LanePlanner() self.last_cloudlog_t = 0 self.steer_rate_cost = CP.steerRateCost self.setup_mpc() self.solution_invalid_cnt = 0 self.lane_change_enabled = Params().get('LaneChangeEnabled') == b'1' self.lane_change_state = LaneChangeState.off self.lane_change_direction = LaneChangeDirection.none self.lane_change_timer = 0.0 self.lane_change_ll_prob = 1.0 self.prev_one_blinker = False self.desire = log.PathPlan.Desire.none self.path_xyz = np.zeros((TRAJECTORY_SIZE,3)) self.plan_yaw = np.zeros((TRAJECTORY_SIZE,)) self.t_idxs = np.arange(TRAJECTORY_SIZE) self.y_pts = np.zeros(TRAJECTORY_SIZE) def setup_mpc(self): self.libmpc = libmpc_py.libmpc self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING, self.steer_rate_cost) self.mpc_solution = libmpc_py.ffi.new("log_t *") self.cur_state = libmpc_py.ffi.new("state_t *") self.cur_state[0].x = 0.0 self.cur_state[0].y = 0.0 self.cur_state[0].psi = 0.0 self.cur_state[0].tire_angle = 0.0 self.angle_steers_des = 0.0 self.angle_steers_des_mpc = 0.0 self.angle_steers_des_time = 0.0 def update(self, sm, CP, VM): v_ego = sm['carState'].vEgo active = sm['controlsState'].active steering_wheel_angle_offset_deg = sm['liveParameters'].angleOffset steering_wheel_angle_deg = sm['carState'].steeringAngle measured_tire_angle = -math.radians(steering_wheel_angle_deg - steering_wheel_angle_offset_deg) / VM.sR # Update vehicle model x = max(sm['liveParameters'].stiffnessFactor, 0.1) sr = max(sm['liveParameters'].steerRatio, 0.1) VM.update_params(x, sr) curvature_factor = VM.curvature_factor(v_ego) md = sm['modelV2'] self.LP.parse_model(sm['modelV2']) if len(md.position.x) == TRAJECTORY_SIZE and len(md.orientation.x) == TRAJECTORY_SIZE: self.path_xyz = np.column_stack([md.position.x, md.position.y, md.position.z]) self.t_idxs = np.array(md.position.t) self.plan_yaw = list(md.orientation.z) # Lane change logic one_blinker = sm['carState'].leftBlinker != sm['carState'].rightBlinker below_lane_change_speed = v_ego < LANE_CHANGE_SPEED_MIN if sm['carState'].leftBlinker: self.lane_change_direction = LaneChangeDirection.left elif sm['carState'].rightBlinker: self.lane_change_direction = LaneChangeDirection.right if (not active) or (self.lane_change_timer > LANE_CHANGE_TIME_MAX) or (not self.lane_change_enabled): self.lane_change_state = LaneChangeState.off self.lane_change_direction = LaneChangeDirection.none else: torque_applied = sm['carState'].steeringPressed and \ ((sm['carState'].steeringTorque > 0 and self.lane_change_direction == LaneChangeDirection.left) or (sm['carState'].steeringTorque < 0 and self.lane_change_direction == LaneChangeDirection.right)) blindspot_detected = ((sm['carState'].leftBlindspot and self.lane_change_direction == LaneChangeDirection.left) or (sm['carState'].rightBlindspot and self.lane_change_direction == LaneChangeDirection.right)) lane_change_prob = self.LP.l_lane_change_prob + self.LP.r_lane_change_prob # State transitions # off if self.lane_change_state == LaneChangeState.off and one_blinker and not self.prev_one_blinker and not below_lane_change_speed: self.lane_change_state = LaneChangeState.preLaneChange self.lane_change_ll_prob = 1.0 # pre elif self.lane_change_state == LaneChangeState.preLaneChange: if not one_blinker or below_lane_change_speed: self.lane_change_state = LaneChangeState.off elif torque_applied and not blindspot_detected: self.lane_change_state = LaneChangeState.laneChangeStarting # starting elif self.lane_change_state == LaneChangeState.laneChangeStarting: # fade out over .5s self.lane_change_ll_prob = max(self.lane_change_ll_prob - 2*DT_MDL, 0.0) # 98% certainty if lane_change_prob < 0.02 and self.lane_change_ll_prob < 0.01: self.lane_change_state = LaneChangeState.laneChangeFinishing # finishing elif self.lane_change_state == LaneChangeState.laneChangeFinishing: # fade in laneline over 1s self.lane_change_ll_prob = min(self.lane_change_ll_prob + DT_MDL, 1.0) if one_blinker and self.lane_change_ll_prob > 0.99: self.lane_change_state = LaneChangeState.preLaneChange elif self.lane_change_ll_prob > 0.99: self.lane_change_state = LaneChangeState.off if self.lane_change_state in [LaneChangeState.off, LaneChangeState.preLaneChange]: self.lane_change_timer = 0.0 else: self.lane_change_timer += DT_MDL self.prev_one_blinker = one_blinker self.desire = DESIRES[self.lane_change_direction][self.lane_change_state] # Turn off lanes during lane change if self.desire == log.PathPlan.Desire.laneChangeRight or self.desire == log.PathPlan.Desire.laneChangeLeft: self.LP.lll_prob *= self.lane_change_ll_prob self.LP.rll_prob *= self.lane_change_ll_prob d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz) y_pts = np.interp(v_ego * self.t_idxs[:MPC_N+1], np.linalg.norm(d_path_xyz, axis=1), d_path_xyz[:,1]) heading_pts = np.interp(v_ego * self.t_idxs[:MPC_N+1], np.linalg.norm(self.path_xyz, axis=1), self.plan_yaw) self.y_pts = y_pts v_ego_mpc = max(v_ego, 5.0) # avoid mpc roughness due to low speed assert len(y_pts) == MPC_N + 1 assert len(heading_pts) == MPC_N + 1 self.libmpc.run_mpc(self.cur_state, self.mpc_solution, float(v_ego_mpc), curvature_factor, CAR_ROTATION_RADIUS, list(y_pts), list(heading_pts)) # init state for next self.cur_state.x = 0.0 self.cur_state.y = 0.0 self.cur_state.psi = 0.0 self.cur_state.tire_angle = interp(DT_MDL, self.t_idxs[:MPC_N+1], self.mpc_solution.tire_angle) # TODO this needs more thought, use .2s extra for now to estimate other delays delay = CP.steerActuatorDelay + .2 next_tire_angle = interp(DT_MDL + delay, self.t_idxs[:MPC_N+1], self.mpc_solution.tire_angle) next_tire_angle_rate = self.mpc_solution.tire_angle_rate[0] # reset to current steer angle if not active or overriding if active: tire_angle_desired = next_tire_angle desired_tire_angle_rate = next_tire_angle_rate else: tire_angle_desired = measured_tire_angle desired_tire_angle_rate = 0.0 # negative sign, controls uses different convention self.desired_steering_wheel_angle_deg = -float(math.degrees(tire_angle_desired * VM.sR)) + steering_wheel_angle_offset_deg self.desired_steering_wheel_angle_rate_deg = -float(math.degrees(desired_tire_angle_rate * VM.sR)) # Check for infeasable MPC solution mpc_nans = any(math.isnan(x) for x in self.mpc_solution.tire_angle) t = sec_since_boot() if mpc_nans: self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING, CP.steerRateCost) self.cur_state.tire_angle = measured_tire_angle if t > self.last_cloudlog_t + 5.0: self.last_cloudlog_t = t cloudlog.warning("Lateral mpc - nan: True") if self.mpc_solution[0].cost > 20000. or mpc_nans: # TODO: find a better way to detect when MPC did not converge self.solution_invalid_cnt += 1 else: self.solution_invalid_cnt = 0 def publish(self, sm, pm): plan_solution_valid = self.solution_invalid_cnt < 2 plan_send = messaging.new_message('pathPlan') plan_send.valid = sm.all_alive_and_valid(service_list=['carState', 'controlsState', 'liveParameters', 'modelV2']) plan_send.pathPlan.laneWidth = float(self.LP.lane_width) plan_send.pathPlan.dPathPoints = [float(x) for x in self.y_pts] plan_send.pathPlan.lProb = float(self.LP.lll_prob) plan_send.pathPlan.rProb = float(self.LP.rll_prob) plan_send.pathPlan.dProb = float(self.LP.d_prob) plan_send.pathPlan.angleSteers = float(self.desired_steering_wheel_angle_deg) plan_send.pathPlan.rateSteers = float(self.desired_steering_wheel_angle_rate_deg) plan_send.pathPlan.angleOffset = float(sm['liveParameters'].angleOffsetAverage) plan_send.pathPlan.mpcSolutionValid = bool(plan_solution_valid) plan_send.pathPlan.paramsValid = bool(sm['liveParameters'].valid) plan_send.pathPlan.desire = self.desire plan_send.pathPlan.laneChangeState = self.lane_change_state plan_send.pathPlan.laneChangeDirection = self.lane_change_direction pm.send('pathPlan', plan_send) if LOG_MPC: dat = messaging.new_message('liveMpc') dat.liveMpc.x = list(self.mpc_solution[0].x) dat.liveMpc.y = list(self.mpc_solution[0].y) dat.liveMpc.psi = list(self.mpc_solution[0].psi) dat.liveMpc.tire_angle = list(self.mpc_solution[0].tire_angle) dat.liveMpc.cost = self.mpc_solution[0].cost pm.send('liveMpc', dat)