人工智能之自动驾驶技术体系

    # 示例：使用YOLOv5进行目标检测
    from yolov5 import YOLOv5
    
    # 加载预训练模型
    model = YOLOv5("yolov5s.pt")
    
    # 检测图像中的目标
    results = model.predict("road.jpg")
    results.show()  # 显示检测结果

    # 示例：使用U-Net进行语义分割
    import torch
    import torch.nn as nn
    
    class UNet(nn.Module):
    def __init__(self):
        super(UNet, self).__init__()
        # 定义U-Net结构
        self.encoder = nn.Sequential(...)
        self.decoder = nn.Sequential(...)
    
    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x
    
    # 训练模型
    model = UNet()
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
    
    # 伪代码：训练过程
    # for images, masks in train_loader:
    #     outputs = model(images)
    #     loss = criterion(outputs, masks)
    #     optimizer.zero_grad()
    #     loss.backward()
    #     optimizer.step()

    # 示例：使用卡尔曼滤波进行定位
    import numpy as np
    from filterpy.kalman import KalmanFilter
    
    # 定义卡尔曼滤波器
    kf = KalmanFilter(dim_x=2, dim_z=1)
    kf.x = np.array([0., 0.])  # 初始状态
    kf.F = np.array([[1., 1.], [0., 1.]])  # 状态转移矩阵
    kf.H = np.array([[1., 0.]])  # 观测矩阵
    kf.P *= 1000.  # 协方差矩阵
    kf.R = 5  # 观测噪声
    
    # 更新状态
    measurements = [1, 2, 3]
    for z in measurements:
    kf.predict()
    kf.update(z)
    print(f"估计位置：{kf.x}")

    # 示例：加载高精度地图
    import json
    
    # 加载地图数据
    with open('high_definition_map.json', 'r') as f:
    hd_map = json.load(f)
    
    # 提取车道线信息
    lanes = hd_map['lanes']
    for lane in lanes:
    print(f"车道ID：{lane['id']}, 车道类型：{lane['type']}")

    # 示例：使用A*算法进行路径规划
    import heapq
    
    def a_star(graph, start, goal):
    open_set = []
    heapq.heappush(open_set, (0, start))
    came_from = {}
    g_score = {node: float('inf') for node in graph}
    g_score[start] = 0
    
    while open_set:
        _, current = heapq.heappop(open_set)
        if current == goal:
            return reconstruct_path(came_from, current)
    
        for neighbor in graph[current]:
            tentative_g_score = g_score[current] + graph[current][neighbor]
            if tentative_g_score < g_score[neighbor]:
                came_from[neighbor] = current
                g_score[neighbor] = tentative_g_score
                heapq.heappush(open_set, (tentative_g_score, neighbor))
    
    return None
    
    # 测试A*算法
    graph = {
    'A': {'B': 1, 'C': 4},
    'B': {'A': 1, 'C': 2, 'D': 5},
    'C': {'A': 4, 'B': 2, 'D': 1},
    'D': {'B': 5, 'C': 1}
    }
    print(a_star(graph, 'A', 'D'))

    # 示例：行为决策逻辑
    def behavior_decision(vehicle_state, traffic_rules):
    if vehicle_state['speed'] > traffic_rules['speed_limit']:
        return "减速"
    elif vehicle_state['distance_to_obstacle'] < 10:
        return "刹车"
    else:
        return "保持速度"

    # 示例：使用PID控制器实现速度控制
    class PIDController:
    def __init__(self, kp, ki, kd):
        self.kp = kp
        self.ki = ki
        self.kd = kd
        self.prev_error = 0
        self.integral = 0
    
    def control(self, setpoint, measured_value):
        error = setpoint - measured_value
        self.integral += error
        derivative = error - self.prev_error
        output = self.kp * error + self.ki * self.integral + self.kd * derivative
        self.prev_error = error
        return output
    
    # 测试PID控制器
    pid = PIDController(kp=1.0, ki=0.1, kd=0.01)
    setpoint = 60  # 目标速度
    measured_value = 50  # 当前速度
    output = pid.control(setpoint, measured_value)
    print(f"控制输出：{output}")