251 lines
10 KiB
Python
251 lines
10 KiB
Python
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import time
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import numpy as np
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from openpilot.common.realtime import DT_MDL
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from openpilot.common.swaglog import cloudlog
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from openpilot.selfdrive.controls.lib.lateral_mpc_lib.lat_mpc import LateralMpc
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from openpilot.selfdrive.controls.lib.lateral_mpc_lib.lat_mpc import N as LAT_MPC_N
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from openpilot.selfdrive.controls.lib.drive_helpers import CONTROL_N, MIN_SPEED, get_speed_error
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# from openpilot.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 openpilot.common.params import Params
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#from openpilot.selfdrive.controls.lib.lane_planner import LanePlanner
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from openpilot.selfdrive.controls.lib.lane_planner_2 import LanePlanner
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from collections import deque
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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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# 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.v_plan = np.zeros((TRAJECTORY_SIZE,))
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self.x_sol = np.zeros((TRAJECTORY_SIZE, 4), dtype=np.float32)
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self.v_ego = MIN_SPEED
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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.params = Params()
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self.latDebugText = ""
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# lane_mode
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self.LP = LanePlanner()
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self.readParams = 0
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self.lanelines_active = False
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self.lanelines_active_tmp = False
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self.useLaneLineSpeedApply = self.params.get_int("UseLaneLineSpeedApply")
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self.pathOffset = float(self.params.get_int("PathOffset")) * 0.01
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self.useLaneLineMode = False
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self.plan_a = np.zeros((TRAJECTORY_SIZE, ))
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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.d_path_w_lines_xyz = np.zeros((TRAJECTORY_SIZE, 3))
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self.lat_mpc = LateralMpc()
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self.reset_mpc(np.zeros(4))
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self.curve_speed = 0
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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, carrot):
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global LATERAL_ACCEL_COST, LATERAL_JERK_COST, STEERING_RATE_COST
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self.readParams -= 1
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if self.readParams <= 0:
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self.readParams = 100
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self.useLaneLineSpeedApply = self.params.get_int("UseLaneLineSpeedApply")
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self.pathOffset = float(self.params.get_int("PathOffset")) * 0.01
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self.lateralPathCost = self.params.get_float("LatMpcPathCost") * 0.01
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self.lateralMotionCost = self.params.get_float("LatMpcMotionCost") * 0.01
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LATERAL_ACCEL_COST = self.params.get_float("LatMpcAccelCost") * 0.01
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LATERAL_JERK_COST = self.params.get_float("LatMpcJerkCost") * 0.01
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STEERING_RATE_COST = self.params.get_float("LatMpcSteeringRateCost")
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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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self.curve_speed = sm['carrotMan'].vTurnSpeed
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# Parse model predictions
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md = sm['modelV2']
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laneless_only = False
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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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self.plan_a = np.array(md.acceleration.x)
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if md.velocity.x[-1] < md.velocity.x[0] * 0.7: # TODO: 모델이 감속을 요청하는 경우 속도테이블이 레인모드를 할수 없음. 속도테이블을 새로 만들어야함..
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laneless_only = True
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# Parse model predictions
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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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#self.DH.update(sm['carState'], md, sm['carControl'].latActive, lane_change_prob, sm)
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if self.useLaneLineSpeedApply == 0 or laneless_only:
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self.useLaneLineMode = False
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elif self.v_ego*3.6 >= self.useLaneLineSpeedApply + 2:
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self.useLaneLineMode = True
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elif self.v_ego*3.6 < self.useLaneLineSpeedApply - 2:
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self.useLaneLineMode = False
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# Turn off lanes during lane change
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#if self.DH.desire == log.Desire.laneChangeRight or self.DH.desire == log.Desire.laneChangeLeft:
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if md.meta.desire != log.Desire.none or carrot.atc_active:
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self.LP.lane_change_multiplier = 0.0 #md.meta.laneChangeProb
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else:
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self.LP.lane_change_multiplier = 1.0
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# lanelines calculation?
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self.LP.lanefull_mode = self.useLaneLineMode
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self.LP.lane_width_left = md.meta.laneWidthLeft
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self.LP.lane_width_right = md.meta.laneWidthRight
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self.LP.curvature = measured_curvature
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self.path_xyz, self.lanelines_active = self.LP.get_d_path(sm['carState'], self.v_ego, self.t_idxs, self.path_xyz, self.curve_speed)
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#if self.LP.lanefull_mode:
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# self.plan_yaw, self.plan_yaw_rate = self.LP.calculate_plan_yaw_and_yaw_rate(self.path_xyz)
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self.latDebugText = self.LP.debugText
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#self.lanelines_active = True if self.LP.d_prob > 0.3 and self.LP.lanefull_mode else False
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self.path_xyz[:, 1] += self.pathOffset
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self.lat_mpc.set_weights(self.lateralPathCost, self.lateralMotionCost,
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LATERAL_ACCEL_COST, LATERAL_JERK_COST,
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STEERING_RATE_COST)
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y_pts = self.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] = np.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 = time.monotonic()
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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, carrot):
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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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if not plan_send.valid:
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#print("lateralPlan_valid=", sm.valid)
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#print("lateralPlan_alive=", sm.alive)
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#print("lateralPlan_freq_ok=", sm.freq_ok)
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#print(sm.avg_freq)
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pass
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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.item() / 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.lanelines_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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lateralPlan.laneWidth = float(self.LP.lane_width)
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#plan_send.lateralPlan.dPathWLinesX = [float(x) for x in self.d_path_w_lines_xyz[:, 0]]
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#plan_send.lateralPlan.dPathWLinesY = [float(y) for y in self.d_path_w_lines_xyz[:, 1]]
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#lateralPlan.laneWidthLeft = float(self.DH.lane_width_left)
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#lateralPlan.laneWidthRight = float(self.DH.lane_width_right)
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lateralPlan.position.x = self.x_sol[:, 0].tolist()
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lateralPlan.position.y = self.x_sol[:, 1].tolist()
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lateralPlan.position.z = self.path_xyz[:, 2].tolist()
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#lateralPlan.distances = self.lat_mpc.x_sol[0:CONTROL_N, 0].tolist()
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self.x_sol = self.lat_mpc.x_sol
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debugText = (
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f"{'lanemode' if self.lanelines_active else 'laneless'} | " +
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f"{self.LP.lane_width_left:.1f}m | " +
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f"{self.LP.lane_width:.1f}m | " +
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f"{self.LP.lane_width_right:.1f}m | " +
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f"{f'offset={self.LP.offset_total * 100.0:.1f}cm turn={np.clip(self.curve_speed, -200, 200):.0f}km/h' if self.lanelines_active else ''}"
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)
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lateralPlan.latDebugText = debugText
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#lateralPlan.latDebugText = self.latDebugText
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#lateralPlan.laneWidthLeft = float(self.DH.lane_width_left)
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#lateralPlan.laneWidthRight = float(self.DH.lane_width_right)
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#lateralPlan.distanceToRoadEdgeLeft = float(self.DH.distance_to_road_edge_left)
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#lateralPlan.distanceToRoadEdgeRight = float(self.DH.distance_to_road_edge_right)
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pm.send('lateralPlan', plan_send)
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