diff --git a/selfdrive/controls/lib/lane_planner_2.py b/selfdrive/controls/lib/lane_planner_2.py
index 1b127a1..d77c4e0 100644
--- a/selfdrive/controls/lib/lane_planner_2.py
+++ b/selfdrive/controls/lib/lane_planner_2.py
@@ -74,14 +74,6 @@ class LanePlanner:
 
     self.params = Params()
     self.camera_offset = self.params.get_int("CameraOffset") * 0.01
-    
-    # 障碍物绕行参数
-    # 绕行要“明显”，需要更快的响应；时间常数过大时偏移会被抹平
-    self.obstacle_avoidance_offset = FirstOrderFilter(0.0, 0.4, DT_MDL)
-    self.obstacle_offset_left = 0.0
-    self.obstacle_offset_right = 0.0
-    self.last_avoidance_time = 0.0  # 记录最后一次绕行时间
-    self.avoidance_cooldown = 2.0  # 绕行结束后的冷却时间（秒）
 
   def parse_model(self, md):
 
@@ -111,137 +103,7 @@ class LanePlanner:
       self.l_lane_change_prob = desire_state[log.Desire.laneChangeLeft]
       self.r_lane_change_prob = desire_state[log.Desire.laneChangeRight]
 
-  def calculate_obstacle_avoidance_offset(self, leads_left, leads_right, v_ego, lead_one=None):
-    """计算障碍物绕行偏移量，包含对向来车的避让优化"""
-
-    def _lead_field(lead, name, default=0.0):
-      # 既兼容 dict，又兼容 capnp/对象属性
-      if isinstance(lead, dict):
-        return lead.get(name, default)
-      return getattr(lead, name, default)
-
-    offset_left = 0.0
-    offset_right = 0.0
-    oncoming_detected = False
-
-    # -----------------------
-    # 1. 处理前方主目标（本车车道内障碍物）
-    # -----------------------
-    if lead_one is not None:
-      try:
-        d_rel = float(_lead_field(lead_one, 'dRel', 100.0))
-        v_lead = float(_lead_field(lead_one, 'vLead', 0.0))
-        d_path = float(_lead_field(lead_one, 'dPath', 10.0))
-
-        if 5.0 < d_rel < 50.0 and abs(d_path) < 1.5:
-          v_lead_kph = v_lead * 3.6
-
-          # 识别障碍物类型（行人/电动车/一般车辆）
-          if v_lead_kph < 6.0:
-            vulnerable_factor = 2.5
-          elif v_lead_kph < 25.0:
-            vulnerable_factor = 2.0
-          elif v_lead_kph < 40.0:
-            vulnerable_factor = 1.5
-          else:
-            vulnerable_factor = 1.0
-
-          distance_factor = np.interp(d_rel, [5.0, 30.0], [1.0, 0.3])
-
-          # 左侧障碍物 → 向右偏；右侧障碍物 → 向左偏
-          if d_path < -0.3:
-            offset_right = max(offset_right, 0.8 * distance_factor * vulnerable_factor)
-          elif d_path > 0.3:
-            offset_left = max(offset_left, 0.8 * distance_factor * vulnerable_factor)
-      except Exception:
-        pass
-
-    # -----------------------
-    # 2. 侧向障碍物 + 对向来车处理
-    #    left 列表代表车辆左侧一带，right 代表车辆右侧一带
-    # -----------------------
-
-    def _process_side_leads(leads, is_left_side):
-      nonlocal offset_left, offset_right, oncoming_detected
-
-      for lead in leads:
-        status = bool(_lead_field(lead, 'status', False))
-        if not status:
-          continue
-
-        d_rel = float(_lead_field(lead, 'dRel', 100.0))
-        v_lead = float(_lead_field(lead, 'vLead', 0.0))
-        v_rel = float(_lead_field(lead, 'vRel', 0.0))
-        d_path = float(abs(_lead_field(lead, 'dPath', 10.0)))
-
-        if d_rel >= 80.0 or d_path >= 4.0:
-          continue
-
-        v_lead_kph = v_lead * 3.6
-
-        # 基础类型权重：行人/电动车/普通车
-        if v_lead_kph < 6.0:
-          vulnerable_factor = 2.5
-          min_safe_distance = 2.0
-        elif v_lead_kph < 25.0:
-          vulnerable_factor = 2.0
-          min_safe_distance = 1.5
-        elif v_lead_kph < 40.0:
-          vulnerable_factor = 1.5
-          min_safe_distance = 1.2
-        else:
-          vulnerable_factor = 1.0
-          min_safe_distance = 1.0
-
-        # 静止/缓慢目标增强
-        if abs(v_rel) < 2.0 and v_lead_kph < 10.0:
-          vulnerable_factor *= 1.4
-
-        # 对向来车判定：相对速度为负且较大（更早触发），并且目标自身速度不低
-        is_oncoming = (v_rel < -3.0 and v_lead_kph > 20.0)
-        if is_oncoming:
-          # 对向车优先级再提升一些，并允许在更远距离就开始偏移
-          oncoming_detected = True
-          vulnerable_factor *= 1.8
-          distance_factor = np.interp(d_rel, [12.0, 90.0], [1.6, 0.35])
-        else:
-          distance_factor = np.interp(d_rel, [3.0, 40.0], [1.3, 0.2])
-
-        lateral_factor = np.interp(d_path, [0.3, 3.8], [1.2, 0.2])
-        if d_path < min_safe_distance:
-          lateral_factor *= 1.8
-
-        # 左侧列表 → 向右偏；右侧列表 → 向左偏
-        base_gain = 1.35 if is_oncoming else 1.0
-        avoidance_strength = base_gain * 0.95 * distance_factor * lateral_factor * vulnerable_factor
-        if is_left_side:
-          offset_right = max(offset_right, avoidance_strength)
-        else:
-          offset_left = max(offset_left, avoidance_strength)
-
-    if leads_left is not None:
-      _process_side_leads(leads_left, is_left_side=True)
-    if leads_right is not None:
-      _process_side_leads(leads_right, is_left_side=False)
-
-    # 计算最终偏移
-    final_offset = offset_left - offset_right
-    
-    # 速度自适应
-    # 对向场景下不要在高速被过度削弱，否则体感“不明显”
-    if oncoming_detected:
-      speed_factor = np.interp(v_ego * 3.6, [5, 30, 100], [1.8, 1.55, 1.0])
-    else:
-      speed_factor = np.interp(v_ego * 3.6, [5, 30, 80], [1.6, 1.3, 0.8])
-    final_offset *= speed_factor
-    
-    # 限制最大偏移量
-    max_offset = np.interp(v_ego * 3.6, [10, 60], [1.35, 0.9])
-    if oncoming_detected:
-      max_offset *= 1.35
-    return np.clip(final_offset, -max_offset, max_offset)
-
-  def get_d_path(self, CS, v_ego, path_t, path_xyz, curve_speed, leads_left=None, leads_right=None, lead_one=None):
+  def get_d_path(self, CS, v_ego, path_t, path_xyz, curve_speed):
     #if v_ego > 0.1:
     #  self.lane_width_updated_count = max(0, self.lane_width_updated_count - 1)
     # Reduce reliance on lanelines that are too far apart or
@@ -370,31 +232,6 @@ class LanePlanner:
     #  self.lane_width_left_filtered.x, self.lane_width, self.lane_width_right_filtered.x)
 
     adjustLaneTime = self.params.get_float("LatMpcInputOffset") * 0.01 # 0.06
-    
-    # 计算障碍物绕行偏移（在车道线处理之前）
-    obstacle_offset = 0.0
-    has_obstacle = False
-    try:
-      if leads_left is not None and leads_right is not None:
-        obstacle_offset = self.calculate_obstacle_avoidance_offset(leads_left, leads_right, v_ego, lead_one)
-        
-        # 检测是否有需要绕行的障碍物
-        if abs(obstacle_offset) > 0.05:
-          has_obstacle = True
-          self.last_avoidance_time = 0.0  # 重置计时器
-        else:
-          self.last_avoidance_time += DT_MDL
-        
-        # 如果障碍物消失，平滑回归原车道
-        if not has_obstacle and self.last_avoidance_time < self.avoidance_cooldown:
-          # 在冷却期内，逐渐减小绕行偏移
-          decay_factor = 1.0 - (self.last_avoidance_time / self.avoidance_cooldown)
-          obstacle_offset = self.obstacle_avoidance_offset.x * decay_factor
-        
-        self.obstacle_avoidance_offset.update(obstacle_offset)
-    except Exception:
-      pass
-    
     laneline_active = False
     self.d_prob_count = self.d_prob_count + 1 if self.d_prob > 0.3 else 0
     if self.lanefull_mode and self.d_prob_count > int(1 / DT_MDL):
@@ -409,13 +246,10 @@ class LanePlanner:
           lane_path_y_interp = np.interp(path_t * (1.0 + adjustLaneTime), self.ll_t[safe_idxs], lane_path_y[safe_idxs])
           path_xyz[:,1] = self.d_prob * lane_path_y_interp + (1.0 - self.d_prob) * path_xyz[:,1]
 
-    # 应用障碍物绕行偏移（优先级高于车道线）
-    if abs(self.obstacle_avoidance_offset.x) > 0.03:
-      path_xyz[:,1] += self.obstacle_avoidance_offset.x
 
     path_xyz[:, 1] += (self.camera_offset + self.lane_offset_filtered.x)
 
-    self.offset_total = self.lane_offset_filtered.x + self.obstacle_avoidance_offset.x
+    self.offset_total = self.lane_offset_filtered.x
 
     return path_xyz, laneline_active
 
diff --git a/selfdrive/controls/lib/lateral_planner.py b/selfdrive/controls/lib/lateral_planner.py
index 115ec64..52fbf2f 100644
--- a/selfdrive/controls/lib/lateral_planner.py
+++ b/selfdrive/controls/lib/lateral_planner.py
@@ -1,370 +1,347 @@
-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.bypass_lat_offset = 0.0
-    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
-
-    # Bypass lateral assist (no new model): when a close slow lead exists and
-    # lane-change intent is active, add a small temporary lateral offset to help
-    # the vehicle commit to bypass trajectory earlier.
-    lead = sm['radarState'].leadOne
-    lane_change_active = md.meta.desire != log.Desire.none or carrot.desireState > 0.7
-    lead_slow_close = lead.status and lead.dRel < 45.0 and (self.v_ego - lead.vLead) > 1.0 and self.v_ego < (50.0 / 3.6)
-
-    if lane_change_active and lead_slow_close:
-      # choose offset direction from current model desire state (left/right)
-      if md.meta.desire == log.Desire.laneChangeLeft:
-        target_bypass_offset = 0.28
-      elif md.meta.desire == log.Desire.laneChangeRight:
-        target_bypass_offset = -0.28
-      else:
-        target_bypass_offset = 0.0
-    else:
-      target_bypass_offset = 0.0
-
-    # smooth offset transitions to avoid lateral jerk
-    alpha = np.clip(DT_MDL / 0.5, 0.0, 1.0)
-    self.bypass_lat_offset += alpha * (target_bypass_offset - self.bypass_lat_offset)
-
-    self.path_xyz[:, 1] += (self.pathOffset + self.bypass_lat_offset)
-
-    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)
+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)