mirror of
https://github.com/dragonpilot/dragonpilot.git
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212 lines
8.6 KiB
Python
212 lines
8.6 KiB
Python
import numpy as np
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from common.realtime import sec_since_boot, DT_MDL
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from common.numpy_fast import interp
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from system.swaglog import cloudlog
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from selfdrive.controls.lib.lateral_mpc_lib.lat_mpc import LateralMpc
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from selfdrive.controls.lib.lateral_mpc_lib.lat_mpc import N as LAT_MPC_N
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from selfdrive.controls.lib.drive_helpers import CONTROL_N, MIN_SPEED, get_speed_error
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from selfdrive.controls.lib.desire_helper import DesireHelper
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import cereal.messaging as messaging
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from cereal import log
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from selfdrive.controls.lib.lane_planner import LanePlanner
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from common.params import Params
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from selfdrive.controls.lib.road_edge_detector import RoadEdgeDetector
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TRAJECTORY_SIZE = 33
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CAMERA_OFFSET = 0.04
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PATH_COST = 1.0
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LATERAL_MOTION_COST = 0.11
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LATERAL_ACCEL_COST = 0.0
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LATERAL_JERK_COST = 0.04
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# Extreme steering rate is unpleasant, even
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# when it does not cause bad jerk.
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# TODO this cost should be lowered when low
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# speed lateral control is stable on all cars
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STEERING_RATE_COST = 700.0
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class LateralPlanner:
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def __init__(self, CP, debug=False):
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self.DH = DesireHelper()
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self.RED = RoadEdgeDetector()
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# dp - lanefull
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params = Params()
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self._dp_lat_lane_priority_mode = params.get_bool("dp_lat_lane_priority_mode")
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self.RED.set_enabled(params.get_bool("dp_lateral_road_edge_detection"))
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self._dp_lat_lane_priority_mode_active = False
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self._dp_lat_lane_priority_mode_active_prev = False
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self.LP = LanePlanner()
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# dp // mapd - for vision turn controller
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self._d_path_w_lines_xyz = np.zeros((TRAJECTORY_SIZE, 3))
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# Vehicle model parameters used to calculate lateral movement of car
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self.factor1 = CP.wheelbase - CP.centerToFront
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self.factor2 = (CP.centerToFront * CP.mass) / (CP.wheelbase * CP.tireStiffnessRear)
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self.last_cloudlog_t = 0
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self.solution_invalid_cnt = 0
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self.path_xyz = np.zeros((TRAJECTORY_SIZE, 3))
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self.velocity_xyz = np.zeros((TRAJECTORY_SIZE, 3))
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self.plan_yaw = np.zeros((TRAJECTORY_SIZE,))
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self.plan_yaw_rate = 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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self.v_plan = np.zeros((TRAJECTORY_SIZE,))
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self.v_ego = 0.0
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self.l_lane_change_prob = 0.0
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self.r_lane_change_prob = 0.0
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self.debug_mode = debug
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self.lat_mpc = LateralMpc()
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self.reset_mpc(np.zeros(4))
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def reset_mpc(self, x0=None):
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if x0 is None:
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x0 = np.zeros(4)
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self.x0 = x0
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self.lat_mpc.reset(x0=self.x0)
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def update(self, sm):
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# clip speed , lateral planning is not possible at 0 speed
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measured_curvature = sm['controlsState'].curvature
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v_ego_car = sm['carState'].vEgo
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# Parse model predictions
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md = 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 = np.array(md.orientation.z)
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self.plan_yaw_rate = np.array(md.orientationRate.z)
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self.velocity_xyz = np.column_stack([md.velocity.x, md.velocity.y, md.velocity.z])
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car_speed = np.linalg.norm(self.velocity_xyz, axis=1) - get_speed_error(md, v_ego_car)
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self.v_plan = np.clip(car_speed, MIN_SPEED, np.inf)
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self.v_ego = self.v_plan[0]
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# Lane change logic
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desire_state = md.meta.desireState
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if len(desire_state):
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self.l_lane_change_prob = desire_state[log.LateralPlan.Desire.laneChangeLeft]
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self.r_lane_change_prob = desire_state[log.LateralPlan.Desire.laneChangeRight]
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if self._dp_lat_lane_priority_mode:
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self.LP.parse_model(md)
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lane_change_prob = self.LP.l_lane_change_prob + self.LP.r_lane_change_prob
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else:
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lane_change_prob = self.l_lane_change_prob + self.r_lane_change_prob
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edge_detected_left, edge_detected_right = self.RED.get_road_edge_detected(md.roadEdgeStds, md.laneLineProbs, sm['carState'].leftBlinker, sm['carState'].rightBlinker)
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self.DH.update(sm['carState'], sm['carControl'].latActive, lane_change_prob, edge_detected_left, edge_detected_right)
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path_xyz = self._get_laneless_laneline_d_path_xyz() if self._dp_lat_lane_priority_mode else self.path_xyz
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self._d_path_w_lines_xyz = path_xyz
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self.lat_mpc.set_weights(PATH_COST, LATERAL_MOTION_COST,
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LATERAL_ACCEL_COST, LATERAL_JERK_COST,
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STEERING_RATE_COST)
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y_pts = path_xyz[:LAT_MPC_N+1, 1]
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heading_pts = self.plan_yaw[:LAT_MPC_N+1]
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yaw_rate_pts = self.plan_yaw_rate[:LAT_MPC_N+1]
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self.y_pts = y_pts
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assert len(y_pts) == LAT_MPC_N + 1
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assert len(heading_pts) == LAT_MPC_N + 1
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assert len(yaw_rate_pts) == LAT_MPC_N + 1
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lateral_factor = np.clip(self.factor1 - (self.factor2 * self.v_plan**2), 0.0, np.inf)
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p = np.column_stack([self.v_plan, lateral_factor])
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self.lat_mpc.run(self.x0,
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p,
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y_pts,
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heading_pts,
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yaw_rate_pts)
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# init state for next iteration
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# mpc.u_sol is the desired second derivative of psi given x0 curv state.
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# with x0[3] = measured_yaw_rate, this would be the actual desired yaw rate.
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# instead, interpolate x_sol so that x0[3] is the desired yaw rate for lat_control.
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self.x0[3] = interp(DT_MDL, self.t_idxs[:LAT_MPC_N + 1], self.lat_mpc.x_sol[:, 3])
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# Check for infeasible MPC solution
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mpc_nans = np.isnan(self.lat_mpc.x_sol[:, 3]).any()
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t = sec_since_boot()
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if mpc_nans or self.lat_mpc.solution_status != 0:
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self.reset_mpc()
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self.x0[3] = measured_curvature * self.v_ego
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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.lat_mpc.cost > 1e6 or mpc_nans:
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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_checks(service_list=['carState', 'controlsState', 'modelV2'])
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lateralPlan = plan_send.lateralPlan
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lateralPlan.modelMonoTime = sm.logMonoTime['modelV2']
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lateralPlan.dPathPoints = self.y_pts.tolist()
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lateralPlan.psis = self.lat_mpc.x_sol[0:CONTROL_N, 2].tolist()
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lateralPlan.curvatures = (self.lat_mpc.x_sol[0:CONTROL_N, 3]/self.v_ego).tolist()
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lateralPlan.curvatureRates = [float(x/self.v_ego) for x in self.lat_mpc.u_sol[0:CONTROL_N - 1]] + [0.0]
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lateralPlan.mpcSolutionValid = bool(plan_solution_valid)
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lateralPlan.solverExecutionTime = self.lat_mpc.solve_time
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if self.debug_mode:
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lateralPlan.solverCost = self.lat_mpc.cost
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lateralPlan.solverState = log.LateralPlan.SolverState.new_message()
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lateralPlan.solverState.x = self.lat_mpc.x_sol.tolist()
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lateralPlan.solverState.u = self.lat_mpc.u_sol.flatten().tolist()
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lateralPlan.desire = self.DH.desire
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lateralPlan.useLaneLines = self._dp_lat_lane_priority_mode and self._dp_lat_lane_priority_mode_active
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lateralPlan.laneChangeState = self.DH.lane_change_state
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lateralPlan.laneChangeDirection = self.DH.lane_change_direction
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pm.send('lateralPlan', plan_send)
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# dp - extension
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plan_ext_send = messaging.new_message('lateralPlanExt')
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lateralPlanExt = plan_ext_send.lateralPlanExt
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lateralPlanExt.dPathWLinesX = [float(x) for x in self._d_path_w_lines_xyz[:, 0]]
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lateralPlanExt.dPathWLinesY = [float(y) for y in self._d_path_w_lines_xyz[:, 1]]
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pm.send('lateralPlanExt', plan_ext_send)
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def _get_laneless_laneline_d_path_xyz(self):
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if self._dp_lat_lane_priority_mode and self.LP is not None:
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# Turn off lanes during lane change
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if self.DH.desire == log.LateralPlan.Desire.laneChangeRight or self.DH.desire == log.LateralPlan.Desire.laneChangeLeft:
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self.LP.lll_prob *= self.DH.lane_change_ll_prob
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self.LP.rll_prob *= self.DH.lane_change_ll_prob
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# decide what mode should we use
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if (self.LP.lll_prob + self.LP.rll_prob)/2 < 0.3:
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self._dp_lat_lane_priority_mode_active = False
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if (self.LP.lll_prob + self.LP.rll_prob)/2 > 0.5:
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self._dp_lat_lane_priority_mode_active = True
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# perform reset mpc
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if self._dp_lat_lane_priority_mode_active != self._dp_lat_lane_priority_mode_active_prev:
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self.reset_mpc()
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self._dp_lat_lane_priority_mode_active_prev = self._dp_lat_lane_priority_mode_active
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# use default path if not active
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if not self._dp_lat_lane_priority_mode_active:
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return self.path_xyz
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# use lane planner path
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return self.LP.get_d_path(self.v_ego, self.t_idxs, self.path_xyz)
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else:
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return self.path_xyz
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