深度学习在自动驾驶中的应用

    import tensorflow as tf
    from tensorflow.keras import layers, models
    
    # 构建CNN模型
    model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.Flatten(),
    layers.Dense(64, activation='relu'),
    layers.Dense(10, activation='softmax')  # 假设有10个类别
    ])
    
    # 编译模型
    model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
    
    # 假设有一些训练数据
    # X_train, y_train = ... (加载数据)
    
    # 训练模型
    # model.fit(X_train, y_train, epochs=10)
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

    import numpy as np
    
    # 初始化Q表
    Q = np.zeros((10, 4))  # 假设有10个状态，4个动作
    
    # 定义奖励函数和转移概率（简化）
    def get_reward(state, action):
    # 根据状态和动作返回奖励
    return np.random.rand()  # 示例中随机返回奖励
    
    def get_next_state(state, action):
    # 根据状态和动作返回下一个状态
    return (state + action) % 10  # 示例中简单循环状态
    
    # Q-learning算法
    alpha = 0.1  # 学习率
    gamma = 0.9  # 折扣因子
    epsilon = 0.1  # 探索率
    
    for episode in range(1000):
    state = np.random.randint(10)  # 随机初始状态
    done = False
    
    while not done:
        if np.random.rand() < epsilon:
            action = np.random.randint(4)  # 探索
        else:
            action = np.argmax(Q[state, :])  # 利用
    
        next_state = get_next_state(state, action)
        reward = get_reward(state, action)
    
        # 更新Q表
        Q[state, action] = Q[state, action] + alpha * (reward + gamma * np.max(Q[next_state, :]) - Q[state, action])
    
        state = next_state
        # 假设某个条件结束episode，这里简化为固定步数
        if episode % 100 == 0:  # 仅为示例，实际应根据具体情况判断
            done = True
    
    print("Trained Q-table:")
    print(Q)
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

    import tensorflow as tf
    from tensorflow.keras import layers, models
    
    # 构建DNN模型
    model = models.Sequential([
    layers.Dense(64, activation='relu', input_shape=(10,)),  # 假设输入特征维度为10
    layers.Dense(64, activation='relu'),
    layers.Dense(3, activation='linear')  # 输出控制信号，如加速、转向、制动
    ])
    
    # 编译模型
    model.compile(optimizer='adam', loss='mse')
    
    # 假设有一些训练数据
    # X_train, y_train = ... (加载数据，X_train为输入特征，y_train为控制信号)
    
    # 训练模型
    # model.fit(X_train, y_train, epochs=100)
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

    # 假设测试集数据为X_test, y_test
    loss, accuracy = model.evaluate(X_test, y_test)
    print(f"Test Accuracy: {accuracy:.4f}")
    
    # 计算其他指标（如召回率、F1分数）需使用额外工具或自定义函数
    from sklearn.metrics import classification_report
    
    # 假设y_pred为模型预测结果
    # y_pred = model.predict(X_test).argmax(axis=1)
    # print(classification_report(y_test, y_pred))