import os import math import numpy as np from common.params import Params from common.realtime import sec_since_boot, DT_MDL from common.numpy_fast import interp, clip 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 import cereal.messaging as messaging from cereal import log LaneChangeState = log.LateralPlan.LaneChangeState LaneChangeDirection = log.LateralPlan.LaneChangeDirection LOG_MPC = os.environ.get('LOG_MPC', False) LANE_CHANGE_SPEED_MIN = 30 * CV.MPH_TO_MS LANE_CHANGE_TIME_MAX = 10. # this corresponds to 80deg/s and 20deg/s steering angle in a toyota corolla MAX_CURVATURE_RATES = [0.03762194918267951, 0.003441203371932992] MAX_CURVATURE_RATE_SPEEDS = [0, 35] DESIRES = { LaneChangeDirection.none: { LaneChangeState.off: log.LateralPlan.Desire.none, LaneChangeState.preLaneChange: log.LateralPlan.Desire.none, LaneChangeState.laneChangeStarting: log.LateralPlan.Desire.none, LaneChangeState.laneChangeFinishing: log.LateralPlan.Desire.none, }, LaneChangeDirection.left: { LaneChangeState.off: log.LateralPlan.Desire.none, LaneChangeState.preLaneChange: log.LateralPlan.Desire.none, LaneChangeState.laneChangeStarting: log.LateralPlan.Desire.laneChangeLeft, LaneChangeState.laneChangeFinishing: log.LateralPlan.Desire.laneChangeLeft, }, LaneChangeDirection.right: { LaneChangeState.off: log.LateralPlan.Desire.none, LaneChangeState.preLaneChange: log.LateralPlan.Desire.none, LaneChangeState.laneChangeStarting: log.LateralPlan.Desire.laneChangeRight, LaneChangeState.laneChangeFinishing: log.LateralPlan.Desire.laneChangeRight, }, } class LateralPlanner(): 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.use_lanelines = not Params().get_bool('EndToEndToggle') 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.LateralPlan.Desire.none self.path_xyz = np.zeros((TRAJECTORY_SIZE,3)) self.path_xyz_stds = np.ones((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() 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].curvature = 0.0 self.desired_curvature = 0.0 self.safe_desired_curvature = 0.0 self.desired_curvature_rate = 0.0 self.safe_desired_curvature_rate = 0.0 def update(self, sm, CP): v_ego = sm['carState'].vEgo active = sm['controlsState'].active measured_curvature = sm['controlsState'].curvature 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) if len(md.orientation.xStd) == TRAJECTORY_SIZE: self.path_xyz_stds = np.column_stack([md.position.xStd, md.position.yStd, md.position.zStd]) # 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): 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.LateralPlan.Desire.laneChangeRight or self.desire == log.LateralPlan.Desire.laneChangeLeft: self.LP.lll_prob *= self.lane_change_ll_prob self.LP.rll_prob *= self.lane_change_ll_prob if self.use_lanelines: std_cost_mult = np.clip(abs(self.path_xyz[0,1]/self.path_xyz_stds[0,1]), 0.5, 5.0) d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz) else: std_cost_mult = 1.0 d_path_xyz = 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 assert len(y_pts) == MPC_N + 1 assert len(heading_pts) == MPC_N + 1 self.libmpc.set_weights(std_cost_mult*MPC_COST_LAT.PATH, 0.0, CP.steerRateCost) self.libmpc.run_mpc(self.cur_state, self.mpc_solution, float(v_ego), 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.curvature = interp(DT_MDL, self.t_idxs[:MPC_N + 1], self.mpc_solution.curvature) # TODO this needs more thought, use .2s extra for now to estimate other delays delay = CP.steerActuatorDelay + .2 current_curvature = self.mpc_solution.curvature[0] psi = interp(delay, self.t_idxs[:MPC_N + 1], self.mpc_solution.psi) next_curvature_rate = self.mpc_solution.curvature_rate[0] # MPC can plan to turn the wheel and turn back before t_delay. This means # in high delay cases some corrections never even get commanded. So just use # psi to calculate a simple linearization of desired curvature curvature_diff_from_psi = psi / (max(v_ego, 1e-1) * delay) - current_curvature next_curvature = current_curvature + 2 * curvature_diff_from_psi self.desired_curvature = next_curvature self.desired_curvature_rate = next_curvature_rate max_curvature_rate = interp(v_ego, MAX_CURVATURE_RATE_SPEEDS, MAX_CURVATURE_RATES) self.safe_desired_curvature_rate = clip(self.desired_curvature_rate, -max_curvature_rate, max_curvature_rate) self.safe_desired_curvature = clip(self.desired_curvature, self.safe_desired_curvature - max_curvature_rate/DT_MDL, self.safe_desired_curvature + max_curvature_rate/DT_MDL) # Check for infeasable MPC solution mpc_nans = any(math.isnan(x) for x in self.mpc_solution.curvature) t = sec_since_boot() if mpc_nans: self.libmpc.init() self.cur_state.curvature = measured_curvature 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('lateralPlan') plan_send.valid = sm.all_alive_and_valid(service_list=['carState', 'controlsState', 'modelV2']) plan_send.lateralPlan.laneWidth = float(self.LP.lane_width) plan_send.lateralPlan.dPathPoints = [float(x) for x in self.y_pts] plan_send.lateralPlan.lProb = float(self.LP.lll_prob) plan_send.lateralPlan.rProb = float(self.LP.rll_prob) plan_send.lateralPlan.dProb = float(self.LP.d_prob) plan_send.lateralPlan.rawCurvature = float(self.desired_curvature) plan_send.lateralPlan.rawCurvatureRate = float(self.desired_curvature_rate) plan_send.lateralPlan.curvature = float(self.safe_desired_curvature) plan_send.lateralPlan.curvatureRate = float(self.safe_desired_curvature_rate) plan_send.lateralPlan.mpcSolutionValid = bool(plan_solution_valid) plan_send.lateralPlan.desire = self.desire plan_send.lateralPlan.laneChangeState = self.lane_change_state plan_send.lateralPlan.laneChangeDirection = self.lane_change_direction pm.send('lateralPlan', plan_send) if LOG_MPC: dat = messaging.new_message('liveMpc') dat.liveMpc.x = list(self.mpc_solution.x) dat.liveMpc.y = list(self.mpc_solution.y) dat.liveMpc.psi = list(self.mpc_solution.psi) dat.liveMpc.curvature = list(self.mpc_solution.curvature) dat.liveMpc.cost = self.mpc_solution.cost pm.send('liveMpc', dat)