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下面是一个使用Python实现的示例代码，用于处理协作车辆进入路口遮挡自车路径的情况。我们将分为几个步骤来完成这个任务：

1. 离线混合A*路径规划：使用混合A*算法规划自车从起点到终点的初始路径。
2. 障碍物处理：根据他车的停车点和车辆长宽确定障碍物的位置和大小。
3. 决策模型：判断是否需要进行路径重规划。
4. 在线路径重规划：如果需要重规划，使用 MoveToXY 方法重新规划路径。

import heapq
import numpy as np
import math# 定义节点类
class Node:def __init__(self, x, y, theta, g=0, h=0, parent=None):self.x = xself.y = yself.theta = thetaself.g = gself.h = hself.f = g + hself.parent = parentdef __lt__(self, other):return self.f < other.f# 定义混合A*路径规划器
def hybrid_astar(start, goal, obstacles, vehicle_length, vehicle_width, grid_resolution=0.1, theta_resolution=math.radians(10)):open_list = []closed_set = set()start_node = Node(start[0], start[1], start[2])heapq.heappush(open_list, start_node)while open_list:current_node = heapq.heappop(open_list)if (math.sqrt((current_node.x - goal[0])**2 + (current_node.y - goal[1])**2) < grid_resolution andabs(current_node.theta - goal[2]) < theta_resolution):path = []while current_node:path.append((current_node.x, current_node.y, current_node.theta))current_node = current_node.parentreturn path[::-1]closed_set.add((current_node.x, current_node.y, current_node.theta))# 生成子节点for steering_angle in [-math.radians(30), 0, math.radians(30)]:new_x = current_node.x + math.cos(current_node.theta) * grid_resolutionnew_y = current_node.y + math.sin(current_node.theta) * grid_resolutionnew_theta = current_node.theta + steering_anglenew_node = Node(new_x, new_y, new_theta,g=current_node.g + grid_resolution,h=math.sqrt((new_x - goal[0])**2 + (new_y - goal[1])**2),parent=current_node)if (new_x, new_y, new_theta) in closed_set:continue# 检查碰撞if not is_collision(new_node, obstacles, vehicle_length, vehicle_width):heapq.heappush(open_list, new_node)return None# 检查碰撞
def is_collision(node, obstacles, vehicle_length, vehicle_width):for obstacle in obstacles:ox, oy, ol, ow = obstacle# 简单的矩形碰撞检测min_x = ox - ol / 2max_x = ox + ol / 2min_y = oy - ow / 2max_y = oy + ow / 2if (min_x < node.x < max_x) and (min_y < node.y < max_y):return Truereturn False# 决策模型：判断是否需要重规划路径
def decision_model(path, obstacles):for point in path:for obstacle in obstacles:ox, oy, ol, ow = obstaclemin_x = ox - ol / 2max_x = ox + ol / 2min_y = oy - ow / 2max_y = oy + ow / 2if (min_x < point[0] < max_x) and (min_y < point[1] < max_y):return Truereturn False# 在线路径重规划（简化的MoveToXY）
def online_replanning(start, goal, obstacles, vehicle_length, vehicle_width):return hybrid_astar(start, goal, obstacles, vehicle_length, vehicle_width)# 示例参数
start = (0, 0, 0)  # 起点 (x, y, theta)
goal = (10, 10, 0)  # 终点 (x, y, theta)
obstacles = [(5, 5, 2, 2)]  # 障碍物 (x, y, length, width)
vehicle_length = 4
vehicle_width = 2# 离线混合A*路径规划
initial_path = hybrid_astar(start, goal, obstacles, vehicle_length, vehicle_width)
if initial_path:print("初始路径规划成功！")
else:print("初始路径规划失败！")# 决策是否需要重规划
if decision_model(initial_path, obstacles):print("需要重规划路径！")new_path = online_replanning(start, goal, obstacles, vehicle_length, vehicle_width)if new_path:print("重规划路径成功！")else:print("重规划路径失败！")
else:print("不需要重规划路径。")

代码说明：

1. Node类：定义了路径规划中的节点，包含坐标、角度、代价等信息。
2. hybrid_astar函数：实现了混合A*路径规划算法，用于离线规划初始路径。
3. is_collision函数：检查节点是否与障碍物发生碰撞。
4. decision_model函数：判断当前路径是否与障碍物冲突，决定是否需要重规划路径。
5. online_replanning函数：调用混合A*算法进行在线路径重规划。

注意事项：

• 此代码中的混合A*算法是一个简化版本，实际应用中可能需要更复杂的实现。
• MoveToXY 方法在代码中简化为再次调用混合A*算法进行路径重规划。
• 碰撞检测部分使用了简单的矩形碰撞检测，实际应用中可能需要更精确的碰撞检测方法。