翻译这段代码：# Find the lookahead distance look_ahead_distance = v + L/5.0 look_ahead_index = min_index for i in range(min_index, len(waypoints)): point = waypoints[i] dx = x - point[0] dy = y - point[1] dist = np.sqrt(dx**2 + dy**2) if (dist > look_ahead_distance): look_ahead_index = i break

# 寻找前方的距离
look_ahead_distance = v + L/5.0 # 计算前方距离
look_ahead_index = min_index # 将最小索引设为前方索引
for i in range(min_index, len(waypoints)): # 遍历所有路标
point = waypoints[i] # 获取当前路标
dx = x - point[0] # 计算 x 轴距离
dy = y - point[1] # 计算 y 轴距离
dist = np.sqrt(dx**2 + dy**2) # 计算距离
if (dist > look_ahead_distance): # 如果距离大于前方距离
look_ahead_index = i # 则将当前索引设为前方索引
break # 终止循环

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翻译这段代码： # Find the heading difference between the vehicle and the path x_lookahead = waypoints[look_ahead_index][0] y_lookahead = waypoints[look_ahead_index][1] heading = np.arctan2(y_lookahead - y, x_lookahead - x) yaw_diff_crosstrack = heading - yaw

# 找到车辆和路径之间的航向差异
x_lookahead = waypoints[look_ahead_index][0] # 获取前方目标点的x坐标
y_lookahead = waypoints[look_ahead_index][1] # 获取前方目标点的y坐标
heading = np.arctan2(y_lookahead - y, x_lookahead - x) # 计算车辆当前位置与前方目标点之间的航向
yaw_diff_crosstrack = heading - yaw # 计算航向差异，即车辆需要调整的航向偏差量

翻译这段代码： def stanley_controller_heading(self, yaw, waypoint): # Find the heading difference between the vehicle and the Dubins path x_lookahead = waypoint[0] y_lookahead = waypoint[1] return yaw_diff_heading

这段代码实现了一个名为"stanley_controller_heading"的函数，接受两个参数：yaw和waypoint。函数的作用是计算车辆当前方向与Dubins路径之间的方向差异。其中，x_lookahead和y_lookahead表示Dubins路径上的一个关键点，即车辆需要尽可能靠近的点。最后，函数返回方向差异的值yaw_diff_heading。