253 lines
11 KiB
Python
253 lines
11 KiB
Python
import os
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import math
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import numpy as np
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from common.params import Params
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from common.realtime import sec_since_boot, DT_MDL
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from common.numpy_fast import interp, clip
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from selfdrive.swaglog import cloudlog
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from selfdrive.controls.lib.lateral_mpc import libmpc_py
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from selfdrive.controls.lib.drive_helpers import MPC_COST_LAT, MPC_N, CAR_ROTATION_RADIUS
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from selfdrive.controls.lib.lane_planner import LanePlanner, TRAJECTORY_SIZE
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from selfdrive.config import Conversions as CV
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import cereal.messaging as messaging
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from cereal import log
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LaneChangeState = log.LateralPlan.LaneChangeState
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LaneChangeDirection = log.LateralPlan.LaneChangeDirection
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LOG_MPC = os.environ.get('LOG_MPC', False)
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LANE_CHANGE_SPEED_MIN = 45 * CV.MPH_TO_MS
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LANE_CHANGE_TIME_MAX = 10.
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# this corresponds to 80deg/s and 20deg/s steering angle in a toyota corolla
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MAX_CURVATURE_RATES = [0.03762194918267951, 0.003441203371932992]
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MAX_CURVATURE_RATE_SPEEDS = [0, 35]
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DESIRES = {
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LaneChangeDirection.none: {
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LaneChangeState.off: log.LateralPlan.Desire.none,
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LaneChangeState.preLaneChange: log.LateralPlan.Desire.none,
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LaneChangeState.laneChangeStarting: log.LateralPlan.Desire.none,
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LaneChangeState.laneChangeFinishing: log.LateralPlan.Desire.none,
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},
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LaneChangeDirection.left: {
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LaneChangeState.off: log.LateralPlan.Desire.none,
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LaneChangeState.preLaneChange: log.LateralPlan.Desire.none,
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LaneChangeState.laneChangeStarting: log.LateralPlan.Desire.laneChangeLeft,
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LaneChangeState.laneChangeFinishing: log.LateralPlan.Desire.laneChangeLeft,
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},
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LaneChangeDirection.right: {
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LaneChangeState.off: log.LateralPlan.Desire.none,
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LaneChangeState.preLaneChange: log.LateralPlan.Desire.none,
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LaneChangeState.laneChangeStarting: log.LateralPlan.Desire.laneChangeRight,
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LaneChangeState.laneChangeFinishing: log.LateralPlan.Desire.laneChangeRight,
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},
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}
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class LateralPlanner():
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def __init__(self, CP):
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self.LP = LanePlanner()
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self.last_cloudlog_t = 0
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self.steer_rate_cost = CP.steerRateCost
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self.setup_mpc()
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self.solution_invalid_cnt = 0
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self.use_lanelines = Params().get('EndToEndToggle') != b'1'
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self.lane_change_state = LaneChangeState.off
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self.lane_change_direction = LaneChangeDirection.none
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self.lane_change_timer = 0.0
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self.lane_change_ll_prob = 1.0
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self.prev_one_blinker = False
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self.desire = log.LateralPlan.Desire.none
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self.path_xyz = np.zeros((TRAJECTORY_SIZE,3))
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self.plan_yaw = np.zeros((TRAJECTORY_SIZE,))
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self.t_idxs = np.arange(TRAJECTORY_SIZE)
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self.y_pts = np.zeros(TRAJECTORY_SIZE)
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def setup_mpc(self):
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self.libmpc = libmpc_py.libmpc
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self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING, self.steer_rate_cost)
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self.mpc_solution = libmpc_py.ffi.new("log_t *")
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self.cur_state = libmpc_py.ffi.new("state_t *")
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self.cur_state[0].x = 0.0
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self.cur_state[0].y = 0.0
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self.cur_state[0].psi = 0.0
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self.cur_state[0].curvature = 0.0
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self.desired_curvature = 0.0
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self.safe_desired_curvature = 0.0
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self.desired_curvature_rate = 0.0
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self.safe_desired_curvature_rate = 0.0
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def update(self, sm, CP):
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v_ego = sm['carState'].vEgo
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active = sm['controlsState'].active
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measured_curvature = sm['controlsState'].curvature
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md = sm['modelV2']
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self.LP.parse_model(sm['modelV2'])
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if len(md.position.x) == TRAJECTORY_SIZE and len(md.orientation.x) == TRAJECTORY_SIZE:
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self.path_xyz = np.column_stack([md.position.x, md.position.y, md.position.z])
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self.t_idxs = np.array(md.position.t)
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self.plan_yaw = list(md.orientation.z)
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# Lane change logic
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one_blinker = sm['carState'].leftBlinker != sm['carState'].rightBlinker
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below_lane_change_speed = v_ego < LANE_CHANGE_SPEED_MIN
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if sm['carState'].leftBlinker:
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self.lane_change_direction = LaneChangeDirection.left
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elif sm['carState'].rightBlinker:
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self.lane_change_direction = LaneChangeDirection.right
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if (not active) or (self.lane_change_timer > LANE_CHANGE_TIME_MAX):
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self.lane_change_state = LaneChangeState.off
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self.lane_change_direction = LaneChangeDirection.none
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else:
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torque_applied = sm['carState'].steeringPressed and \
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((sm['carState'].steeringTorque > 0 and self.lane_change_direction == LaneChangeDirection.left) or
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(sm['carState'].steeringTorque < 0 and self.lane_change_direction == LaneChangeDirection.right))
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blindspot_detected = ((sm['carState'].leftBlindspot and self.lane_change_direction == LaneChangeDirection.left) or
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(sm['carState'].rightBlindspot and self.lane_change_direction == LaneChangeDirection.right))
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lane_change_prob = self.LP.l_lane_change_prob + self.LP.r_lane_change_prob
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# State transitions
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# off
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if self.lane_change_state == LaneChangeState.off and one_blinker and not self.prev_one_blinker and not below_lane_change_speed:
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self.lane_change_state = LaneChangeState.preLaneChange
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self.lane_change_ll_prob = 1.0
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# pre
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elif self.lane_change_state == LaneChangeState.preLaneChange:
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if not one_blinker or below_lane_change_speed:
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self.lane_change_state = LaneChangeState.off
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elif torque_applied and not blindspot_detected:
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self.lane_change_state = LaneChangeState.laneChangeStarting
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# starting
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elif self.lane_change_state == LaneChangeState.laneChangeStarting:
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# fade out over .5s
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self.lane_change_ll_prob = max(self.lane_change_ll_prob - 2*DT_MDL, 0.0)
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# 98% certainty
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if lane_change_prob < 0.02 and self.lane_change_ll_prob < 0.01:
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self.lane_change_state = LaneChangeState.laneChangeFinishing
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# finishing
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elif self.lane_change_state == LaneChangeState.laneChangeFinishing:
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# fade in laneline over 1s
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self.lane_change_ll_prob = min(self.lane_change_ll_prob + DT_MDL, 1.0)
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if one_blinker and self.lane_change_ll_prob > 0.99:
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self.lane_change_state = LaneChangeState.preLaneChange
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elif self.lane_change_ll_prob > 0.99:
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self.lane_change_state = LaneChangeState.off
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if self.lane_change_state in [LaneChangeState.off, LaneChangeState.preLaneChange]:
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self.lane_change_timer = 0.0
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else:
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self.lane_change_timer += DT_MDL
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self.prev_one_blinker = one_blinker
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self.desire = DESIRES[self.lane_change_direction][self.lane_change_state]
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# Turn off lanes during lane change
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if self.desire == log.LateralPlan.Desire.laneChangeRight or self.desire == log.LateralPlan.Desire.laneChangeLeft:
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self.LP.lll_prob *= self.lane_change_ll_prob
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self.LP.rll_prob *= self.lane_change_ll_prob
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if self.use_lanelines:
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d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz)
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else:
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d_path_xyz = self.path_xyz
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y_pts = np.interp(v_ego * self.t_idxs[:MPC_N + 1], np.linalg.norm(d_path_xyz, axis=1), d_path_xyz[:,1])
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heading_pts = np.interp(v_ego * self.t_idxs[:MPC_N + 1], np.linalg.norm(self.path_xyz, axis=1), self.plan_yaw)
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self.y_pts = y_pts
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assert len(y_pts) == MPC_N + 1
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assert len(heading_pts) == MPC_N + 1
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self.libmpc.run_mpc(self.cur_state, self.mpc_solution,
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float(v_ego),
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CAR_ROTATION_RADIUS,
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list(y_pts),
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list(heading_pts))
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# init state for next
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self.cur_state.x = 0.0
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self.cur_state.y = 0.0
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self.cur_state.psi = 0.0
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self.cur_state.curvature = interp(DT_MDL, self.t_idxs[:MPC_N + 1], self.mpc_solution.curvature)
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# TODO this needs more thought, use .2s extra for now to estimate other delays
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delay = CP.steerActuatorDelay + .2
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current_curvature = self.mpc_solution.curvature[0]
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psi = interp(delay, self.t_idxs[:MPC_N + 1], self.mpc_solution.psi)
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next_curvature_rate = self.mpc_solution.curvature_rate[0]
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# MPC can plan to turn the wheel and turn back before t_delay. This means
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# in high delay cases some corrections never even get commanded. So just use
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# psi to calculate a simple linearization of desired curvature
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curvature_diff_from_psi = psi / (max(v_ego, 1e-1) * delay) - current_curvature
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next_curvature = current_curvature + 2 * curvature_diff_from_psi
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self.desired_curvature = next_curvature
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self.desired_curvature_rate = next_curvature_rate
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max_curvature_rate = interp(v_ego, MAX_CURVATURE_RATE_SPEEDS, MAX_CURVATURE_RATES)
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self.safe_desired_curvature_rate = clip(self.desired_curvature_rate,
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-max_curvature_rate,
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max_curvature_rate)
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self.safe_desired_curvature = clip(self.desired_curvature,
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self.safe_desired_curvature - max_curvature_rate/DT_MDL,
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self.safe_desired_curvature + max_curvature_rate/DT_MDL)
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# Check for infeasable MPC solution
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mpc_nans = any(math.isnan(x) for x in self.mpc_solution.curvature)
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t = sec_since_boot()
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if mpc_nans:
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self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING, CP.steerRateCost)
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self.cur_state.curvature = measured_curvature
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if t > self.last_cloudlog_t + 5.0:
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self.last_cloudlog_t = t
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cloudlog.warning("Lateral mpc - nan: True")
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if self.mpc_solution[0].cost > 20000. or mpc_nans: # TODO: find a better way to detect when MPC did not converge
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self.solution_invalid_cnt += 1
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else:
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self.solution_invalid_cnt = 0
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def publish(self, sm, pm):
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plan_solution_valid = self.solution_invalid_cnt < 2
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plan_send = messaging.new_message('lateralPlan')
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plan_send.valid = sm.all_alive_and_valid(service_list=['carState', 'controlsState', 'modelV2'])
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plan_send.lateralPlan.laneWidth = float(self.LP.lane_width)
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plan_send.lateralPlan.dPathPoints = [float(x) for x in self.y_pts]
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plan_send.lateralPlan.lProb = float(self.LP.lll_prob)
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plan_send.lateralPlan.rProb = float(self.LP.rll_prob)
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plan_send.lateralPlan.dProb = float(self.LP.d_prob)
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plan_send.lateralPlan.rawCurvature = float(self.desired_curvature)
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plan_send.lateralPlan.rawCurvatureRate = float(self.desired_curvature_rate)
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plan_send.lateralPlan.curvature = float(self.safe_desired_curvature)
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plan_send.lateralPlan.curvatureRate = float(self.safe_desired_curvature_rate)
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plan_send.lateralPlan.mpcSolutionValid = bool(plan_solution_valid)
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plan_send.lateralPlan.desire = self.desire
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plan_send.lateralPlan.laneChangeState = self.lane_change_state
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plan_send.lateralPlan.laneChangeDirection = self.lane_change_direction
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pm.send('lateralPlan', plan_send)
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if LOG_MPC:
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dat = messaging.new_message('liveMpc')
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dat.liveMpc.x = list(self.mpc_solution.x)
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dat.liveMpc.y = list(self.mpc_solution.y)
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dat.liveMpc.psi = list(self.mpc_solution.psi)
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dat.liveMpc.curvature = list(self.mpc_solution.curvature)
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dat.liveMpc.cost = self.mpc_solution.cost
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pm.send('liveMpc', dat)
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