import time import numpy as np from openpilot.common.realtime import DT_MDL from openpilot.common.swaglog import cloudlog from openpilot.selfdrive.controls.lib.lateral_mpc_lib.lat_mpc import LateralMpc from openpilot.selfdrive.controls.lib.lateral_mpc_lib.lat_mpc import N as LAT_MPC_N from openpilot.selfdrive.controls.lib.drive_helpers import CONTROL_N, MIN_SPEED, get_speed_error # from openpilot.selfdrive.controls.lib.desire_helper import DesireHelper import cereal.messaging as messaging from cereal import log from openpilot.common.params import Params #from openpilot.selfdrive.controls.lib.lane_planner import LanePlanner from openpilot.selfdrive.controls.lib.lane_planner_2 import LanePlanner from collections import deque TRAJECTORY_SIZE = 33 #CAMERA_OFFSET = 0.04 PATH_COST = 1.0 LATERAL_MOTION_COST = 0.11 LATERAL_ACCEL_COST = 0.0 LATERAL_JERK_COST = 0.04 # Extreme steering rate is unpleasant, even # when it does not cause bad jerk. # TODO this cost should be lowered when low # speed lateral control is stable on all cars STEERING_RATE_COST = 700.0 class LateralPlanner: def __init__(self, CP, debug=False): #self.DH = DesireHelper() # Vehicle model parameters used to calculate lateral movement of car self.factor1 = CP.wheelbase - CP.centerToFront self.factor2 = (CP.centerToFront * CP.mass) / (CP.wheelbase * CP.tireStiffnessRear) self.last_cloudlog_t = 0 self.solution_invalid_cnt = 0 self.path_xyz = np.zeros((TRAJECTORY_SIZE, 3)) self.velocity_xyz = np.zeros((TRAJECTORY_SIZE, 3)) self.v_plan = np.zeros((TRAJECTORY_SIZE,)) self.x_sol = np.zeros((TRAJECTORY_SIZE, 4), dtype=np.float32) self.v_ego = MIN_SPEED self.l_lane_change_prob = 0.0 self.r_lane_change_prob = 0.0 self.debug_mode = debug self.params = Params() self.latDebugText = "" # lane_mode self.LP = LanePlanner() self.readParams = 0 self.lanelines_active = False self.lanelines_active_tmp = False self.useLaneLineSpeedApply = self.params.get_int("UseLaneLineSpeed") self.pathOffset = float(self.params.get_int("PathOffset")) * 0.01 self.useLaneLineMode = False self.plan_a = np.zeros((TRAJECTORY_SIZE, )) self.plan_yaw = np.zeros((TRAJECTORY_SIZE,)) self.plan_yaw_rate = np.zeros((TRAJECTORY_SIZE,)) self.t_idxs = np.arange(TRAJECTORY_SIZE) self.y_pts = np.zeros((TRAJECTORY_SIZE,)) self.d_path_w_lines_xyz = np.zeros((TRAJECTORY_SIZE, 3)) self.lat_mpc = LateralMpc() self.reset_mpc(np.zeros(4)) self.curve_speed = 0 self.lanemode_possible_count = 0 self.laneless_only = True def reset_mpc(self, x0=None): if x0 is None: x0 = np.zeros(4) self.x0 = x0 self.lat_mpc.reset(x0=self.x0) def update(self, sm, carrot): global LATERAL_ACCEL_COST, LATERAL_JERK_COST, STEERING_RATE_COST self.readParams -= 1 if self.readParams <= 0: self.readParams = 100 self.useLaneLineSpeedApply = sm['carState'].useLaneLineSpeed self.pathOffset = float(self.params.get_int("PathOffset")) * 0.01 self.lateralPathCost = self.params.get_float("LatMpcPathCost") * 0.01 self.lateralMotionCost = self.params.get_float("LatMpcMotionCost") * 0.01 LATERAL_ACCEL_COST = self.params.get_float("LatMpcAccelCost") * 0.01 LATERAL_JERK_COST = self.params.get_float("LatMpcJerkCost") * 0.01 STEERING_RATE_COST = self.params.get_float("LatMpcSteeringRateCost") # clip speed , lateral planning is not possible at 0 speed measured_curvature = sm['controlsState'].curvature v_ego_car = max(sm['carState'].vEgo, MIN_SPEED) speed_kph = v_ego_car * 3.6 self.v_ego = v_ego_car self.curve_speed = sm['carrotMan'].vTurnSpeed # Parse model predictions md = sm['modelV2'] model_active = False if len(md.position.x) == TRAJECTORY_SIZE and len(md.orientation.x) == TRAJECTORY_SIZE: model_active = True 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 = np.array(md.orientation.z) self.plan_yaw_rate = np.array(md.orientationRate.z) self.velocity_xyz = np.column_stack([md.velocity.x, md.velocity.y, md.velocity.z]) car_speed = np.linalg.norm(self.velocity_xyz, axis=1) - get_speed_error(md, v_ego_car) self.v_plan = np.clip(car_speed, MIN_SPEED, np.inf) self.v_ego = self.v_plan[0] self.plan_a = np.array(md.acceleration.x) if md.velocity.x[-1] < md.velocity.x[0] * 0.7: # TODO: 모델이 감속을 요청하는 경우 속도테이블이 레인모드를 할수 없음. 속도테이블을 새로 만들어야함.. self.lanemode_possible_count = 0 self.laneless_only = True else: self.lanemode_possible_count += 1 if self.lanemode_possible_count > int(1/DT_MDL): self.laneless_only = False # Parse model predictions self.LP.parse_model(md) #lane_change_prob = self.LP.l_lane_change_prob + self.LP.r_lane_change_prob #self.DH.update(sm['carState'], md, sm['carControl'].latActive, lane_change_prob, sm) if self.useLaneLineSpeedApply == 0 or self.laneless_only: self.useLaneLineMode = False elif speed_kph >= self.useLaneLineSpeedApply + 2: self.useLaneLineMode = True elif speed_kph < self.useLaneLineSpeedApply - 2: self.useLaneLineMode = False # Turn off lanes during lane change #if self.DH.desire == log.Desire.laneChangeRight or self.DH.desire == log.Desire.laneChangeLeft: if md.meta.desire != log.Desire.none or carrot.atc_active: self.LP.lane_change_multiplier = 0.0 #md.meta.laneChangeProb else: self.LP.lane_change_multiplier = 1.0 # lanelines calculation? self.LP.lanefull_mode = self.useLaneLineMode self.LP.lane_width_left = md.meta.laneWidthLeft self.LP.lane_width_right = md.meta.laneWidthRight self.LP.curvature = measured_curvature self.path_xyz, self.lanelines_active = self.LP.get_d_path(sm['carState'], v_ego_car, self.t_idxs, self.path_xyz, self.curve_speed) if self.lanelines_active: self.plan_yaw, self.plan_yaw_rate = yaw_from_path_no_scipy( self.path_xyz, self.v_plan, smooth_window=5, clip_rate=2.0, align_first_yaw=None #md.orientation.z[0] # 초기 정렬 ) self.latDebugText = self.LP.debugText #self.lanelines_active = True if self.LP.d_prob > 0.3 and self.LP.lanefull_mode else False self.path_xyz[:, 1] += self.pathOffset self.lat_mpc.set_weights(self.lateralPathCost, self.lateralMotionCost, LATERAL_ACCEL_COST, LATERAL_JERK_COST, STEERING_RATE_COST) y_pts = self.path_xyz[:LAT_MPC_N+1, 1] heading_pts = self.plan_yaw[:LAT_MPC_N+1] yaw_rate_pts = self.plan_yaw_rate[:LAT_MPC_N+1] self.y_pts = y_pts assert len(y_pts) == LAT_MPC_N + 1 assert len(heading_pts) == LAT_MPC_N + 1 assert len(yaw_rate_pts) == LAT_MPC_N + 1 lateral_factor = np.clip(self.factor1 - (self.factor2 * self.v_plan**2), 0.0, np.inf) p = np.column_stack([self.v_plan, lateral_factor]) self.lat_mpc.run(self.x0, p, y_pts, heading_pts, yaw_rate_pts) # init state for next iteration # mpc.u_sol is the desired second derivative of psi given x0 curv state. # with x0[3] = measured_yaw_rate, this would be the actual desired yaw rate. # instead, interpolate x_sol so that x0[3] is the desired yaw rate for lat_control. self.x0[3] = np.interp(DT_MDL, self.t_idxs[:LAT_MPC_N + 1], self.lat_mpc.x_sol[:, 3]) # Check for infeasible MPC solution mpc_nans = np.isnan(self.lat_mpc.x_sol[:, 3]).any() t = time.monotonic() if mpc_nans or self.lat_mpc.solution_status != 0: self.reset_mpc() self.x0[3] = measured_curvature * self.v_ego if t > self.last_cloudlog_t + 5.0: self.last_cloudlog_t = t cloudlog.warning("Lateral mpc - nan: True") if self.lat_mpc.cost > 1e6 or mpc_nans: self.solution_invalid_cnt += 1 else: self.solution_invalid_cnt = 0 self.x_sol = self.lat_mpc.x_sol def publish(self, sm, pm, carrot): plan_solution_valid = self.solution_invalid_cnt < 2 plan_send = messaging.new_message('lateralPlan') plan_send.valid = sm.all_checks(service_list=['carState', 'controlsState', 'modelV2']) if not plan_send.valid: #print("lateralPlan_valid=", sm.valid) #print("lateralPlan_alive=", sm.alive) #print("lateralPlan_freq_ok=", sm.freq_ok) #print(sm.avg_freq) pass lateralPlan = plan_send.lateralPlan lateralPlan.modelMonoTime = sm.logMonoTime['modelV2'] lateralPlan.dPathPoints = self.y_pts.tolist() lateralPlan.psis = self.lat_mpc.x_sol[0:CONTROL_N, 2].tolist() lateralPlan.distances = self.lat_mpc.x_sol[0:CONTROL_N, 0].tolist() v_div = np.maximum(self.v_plan[:CONTROL_N], 6.0) if len(self.v_plan) == TRAJECTORY_SIZE: lateralPlan.curvatures = (self.lat_mpc.x_sol[0:CONTROL_N, 3] / v_div).tolist() else: lateralPlan.curvatures = (self.lat_mpc.x_sol[0:CONTROL_N, 3] / self.v_ego).tolist() v_div2 = max(self.v_ego, 6.0) lateralPlan.curvatureRates = [float(x.item() / v_div2) for x in self.lat_mpc.u_sol[0:CONTROL_N - 1]] + [0.0] lateralPlan.mpcSolutionValid = bool(plan_solution_valid) lateralPlan.solverExecutionTime = self.lat_mpc.solve_time if self.debug_mode: lateralPlan.solverCost = self.lat_mpc.cost lateralPlan.solverState = log.LateralPlan.SolverState.new_message() lateralPlan.solverState.x = self.lat_mpc.x_sol.tolist() lateralPlan.solverState.u = self.lat_mpc.u_sol.flatten().tolist() #lateralPlan.desire = self.DH.desire lateralPlan.useLaneLines = self.lanelines_active #lateralPlan.laneChangeState = self.DH.lane_change_state #lateralPlan.laneChangeDirection = self.DH.lane_change_direction lateralPlan.laneWidth = float(self.LP.lane_width) #plan_send.lateralPlan.dPathWLinesX = [float(x) for x in self.d_path_w_lines_xyz[:, 0]] #plan_send.lateralPlan.dPathWLinesY = [float(y) for y in self.d_path_w_lines_xyz[:, 1]] #lateralPlan.laneWidthLeft = float(self.DH.lane_width_left) #lateralPlan.laneWidthRight = float(self.DH.lane_width_right) lateralPlan.position.x = self.x_sol[:, 0].tolist() lateralPlan.position.y = self.x_sol[:, 1].tolist() lateralPlan.position.z = self.path_xyz[:, 2].tolist() #lateralPlan.distances = self.lat_mpc.x_sol[0:CONTROL_N, 0].tolist() self.x_sol = self.lat_mpc.x_sol debugText = ( f"{'lanemode' if self.lanelines_active else 'laneless'} | " + f"{self.LP.lane_width_left:.1f}m | " + f"{self.LP.lane_width:.1f}m | " + f"{self.LP.lane_width_right:.1f}m | " + 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 ''}" ) lateralPlan.latDebugText = debugText #lateralPlan.latDebugText = self.latDebugText #lateralPlan.laneWidthLeft = float(self.DH.lane_width_left) #lateralPlan.laneWidthRight = float(self.DH.lane_width_right) #lateralPlan.distanceToRoadEdgeLeft = float(self.DH.distance_to_road_edge_left) #lateralPlan.distanceToRoadEdgeRight = float(self.DH.distance_to_road_edge_right) pm.send('lateralPlan', plan_send) def smooth_moving_avg(arr, window=5): if window < 2: return arr if window % 2 == 0: window += 1 pad = window // 2 arr_pad = np.pad(arr, (pad, pad), mode='edge') kernel = np.ones(window) / window return np.convolve(arr_pad, kernel, mode='same')[pad:-pad] def yaw_from_path_no_scipy(path_xyz, v_plan, smooth_window=5, clip_rate=2.0, align_first_yaw=None): v0 = float(np.asarray(v_plan)[0]) if len(v_plan) else 0.0 # 저속(≤6 m/s)에서는 창을 크게 if v0 <= 6.0: smooth_window = max(smooth_window, 9) # 9~11 권장 N = path_xyz.shape[0] x = path_xyz[:, 0].astype(float) y = path_xyz[:, 1].astype(float) if N < 5: return np.zeros(N, np.float32), np.zeros(N, np.float32) # 1) s(호길이) 계산 dx = np.diff(x) dy = np.diff(y) ds_seg = np.sqrt(dx*dx + dy*dy) ds_seg[ds_seg < 0.05] = 0.05 s = np.zeros(N, float) s[1:] = np.cumsum(ds_seg) if s[-1] < 0.5: # 총 호길이 < 0.5m면 미분 결과 의미가 약함 return np.zeros(N, np.float32), np.zeros(N, np.float32) # 2) smoothing (이동평균) x_smooth = smooth_moving_avg(x, smooth_window) y_smooth = smooth_moving_avg(y, smooth_window) # 3) 1·2차 도함수(s축 미분) dx_ds = np.gradient(x_smooth, s) dy_ds = np.gradient(y_smooth, s) d2x_ds2 = np.gradient(dx_ds, s) d2y_ds2 = np.gradient(dy_ds, s) # 4) yaw = atan2(dy/ds, dx/ds) yaw = np.unwrap(np.arctan2(dy_ds, dx_ds)) # 5) 곡률 kappa = ... denom = (dx_ds*dx_ds + dy_ds*dy_ds)**1.5 denom[denom < 1e-9] = 1e-9 kappa = (dx_ds * d2y_ds2 - dy_ds * d2x_ds2) / denom # 6) yaw_rate = kappa * v v = np.asarray(v_plan, float) yaw_rate = kappa * v if v0 <= 6.0: # 이동평균으로 미세 요동 감쇄(창 5~7) yaw_rate = smooth_moving_avg(yaw_rate, window=7) # 7) 초기 yaw 정렬 (선택) if align_first_yaw is not None: bias = yaw[0] - float(align_first_yaw) yaw = yaw - bias # 8) 안정화 yaw = np.where(np.isfinite(yaw), yaw, 0.0) yaw_rate = np.where(np.isfinite(yaw_rate), yaw_rate, 0.0) yaw_rate = np.clip(yaw_rate, -abs(clip_rate), abs(clip_rate)) return yaw.astype(np.float32), yaw_rate.astype(np.float32)