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126 lines
5.3 KiB
126 lines
5.3 KiB
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 selfdrive.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.drive_helpers import CONTROL_N, MPC_COST_LAT, 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.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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class LateralPlanner:
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def __init__(self, CP, use_lanelines=True, wide_camera=False):
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self.use_lanelines = use_lanelines
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self.LP = LanePlanner(wide_camera)
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self.DH = DesireHelper()
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self.last_cloudlog_t = 0
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self.steer_rate_cost = CP.steerRateCost
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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.path_xyz_stds = np.ones((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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self.lat_mpc = LateralMpc()
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self.reset_mpc(np.zeros(4))
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def reset_mpc(self, 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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v_ego = sm['carState'].vEgo
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measured_curvature = sm['controlsState'].curvature
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# Parse model predictions
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md = sm['modelV2']
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self.LP.parse_model(md)
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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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if len(md.position.xStd) == TRAJECTORY_SIZE:
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self.path_xyz_stds = np.column_stack([md.position.xStd, md.position.yStd, md.position.zStd])
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# Lane change logic
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lane_change_prob = self.LP.l_lane_change_prob + self.LP.r_lane_change_prob
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self.DH.update(sm['carState'], sm['controlsState'].active, lane_change_prob)
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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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# Calculate final driving path and set MPC costs
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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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self.lat_mpc.set_weights(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING, self.steer_rate_cost)
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else:
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d_path_xyz = self.path_xyz
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# Heading cost is useful at low speed, otherwise end of plan can be off-heading
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heading_cost = interp(v_ego, [5.0, 10.0], [MPC_COST_LAT.HEADING, 0.15])
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self.lat_mpc.set_weights(MPC_COST_LAT.PATH, heading_cost, self.steer_rate_cost)
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y_pts = np.interp(v_ego * self.t_idxs[:LAT_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[:LAT_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) == LAT_MPC_N + 1
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assert len(heading_pts) == LAT_MPC_N + 1
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# self.x0[4] = v_ego
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p = np.array([v_ego, CAR_ROTATION_RADIUS])
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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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# init state for next
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# mpc.u_sol is the desired curvature rate given x0 curv state.
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# with x0[3] = measured_curvature, this would be the actual desired rate.
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# instead, interpolate x_sol so that x0[3] is the desired curvature 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
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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 > 20000. 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.laneWidth = float(self.LP.lane_width)
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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].tolist()
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lateralPlan.curvatureRates = [float(x) for x in self.lat_mpc.u_sol[0:CONTROL_N - 1]] + [0.0]
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lateralPlan.lProb = float(self.LP.lll_prob)
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lateralPlan.rProb = float(self.LP.rll_prob)
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lateralPlan.dProb = float(self.LP.d_prob)
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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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lateralPlan.desire = self.DH.desire
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lateralPlan.useLaneLines = self.use_lanelines
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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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