AI人工智能在自动驾驶领域的实践案例

    摄像头 → 2D目标检测 → 3D空间估计
    LiDAR → 点云分割 → 3D物体识别
    雷达 → 速度检测 → 物体追踪
    超声波 → 近距离检测 → 泊车辅助

    import torch
    import torch.nn as nn
    import torchvision.models as models
    
    class PerceptionNetwork(nn.Module):
    def __init__(self, num_classes=10):
        super(PerceptionNetwork, self).__init__()
        # 使用ResNet作为骨干网络
        self.backbone = models.resnet50(pretrained=True)
        # 修改最后一层
        in_features = self.backbone.fc.in_features
        self.backbone.fc = nn.Linear(in_features, 512)
        
        # 检测头
        self.detection_head = nn.Sequential(
            nn.Linear(512, 256),
            nn.ReLU(),
            nn.Linear(256, num_classes*5)  # 每个类别预测5个参数(x,y,w,h,score)
        )
        
        # 分割头
        self.segmentation_head = nn.Sequential(
            nn.Conv2d(2048, 1024, kernel_size=3, padding=1),
            nn.BatchNorm2d(1024),
            nn.ReLU(),
            nn.Conv2d(1024, num_classes, kernel_size=1)
        )
    
    def forward(self, x):
        features = self.backbone(x)
        detection = self.detection_head(features)
        segmentation = self.segmentation_head(features)
        return detection, segmentation

    import numpy as np
    from filterpy.kalman import KalmanFilter
    
    class SensorFusion:
    def __init__(self):
        self.kf = KalmanFilter(dim_x=6, dim_z=3)
        # 状态转移矩阵 (假设匀速运动)
        self.kf.F = np.array([[1,0,0,1,0,0],
                             [0,1,0,0,1,0],
                             [0,0,1,0,0,1],
                             [0,0,0,1,0,0],
                             [0,0,0,0,1,0],
                             [0,0,0,0,0,1]])
        
        # 观测矩阵 (只能观测位置)
        self.kf.H = np.array([[1,0,0,0,0,0],
                             [0,1,0,0,0,0],
                             [0,0,1,0,0,0]])
        
        # 初始化协方差矩阵
        self.kf.P *= 1000.
        
        # 过程噪声
        self.kf.Q = np.eye(6) * 0.01
        
    def update(self, camera_data, lidar_data, radar_data):
        # 传感器数据预处理
        camera_pos = self._process_camera(camera_data)
        lidar_pos = self._process_lidar(lidar_data)
        radar_pos = self._process_radar(radar_data)
        
        # 预测步骤
        self.kf.predict()
        
        # 更新步骤 (使用加权平均)
        fused_pos = (camera_pos * 0.4 + lidar_pos * 0.4 + radar_pos * 0.2)
        self.kf.update(fused_pos)
        
        return self.kf.x[:3]  # 返回融合后的位置估计

    # 创建conda环境
    conda create -n autonomous python=3.8
    conda activate autonomous
    
    # 安装核心库
    pip install torch torchvision torchaudio
    pip install opencv-python numpy pandas
    pip install matplotlib scipy filterpy
    pip install jupyterlab  # 用于交互式开发

    import carla
    import numpy as np
    import cv2
    
    class AutonomousAgent:
    def __init__(self, world, vehicle):
        self.world = world
        self.vehicle = vehicle
        self.camera = None
        self.lidar = None
        self.setup_sensors()
        
    def setup_sensors(self):
        # 设置摄像头
        camera_bp = self.world.get_blueprint_library().find('sensor.camera.rgb')
        camera_bp.set_attribute('image_size_x', '800')
        camera_bp.set_attribute('image_size_y', '600')
        camera_transform = carla.Transform(carla.Location(x=1.5, z=2.4))
        self.camera = self.world.spawn_actor(camera_bp, camera_transform, attach_to=self.vehicle)
        self.camera.listen(self.process_camera)
        
        # 设置LiDAR
        lidar_bp = self.world.get_blueprint_library().find('sensor.lidar.ray_cast')
        lidar_bp.set_attribute('channels', '32')
        lidar_bp.set_attribute('points_per_second', '56000')
        lidar_transform = carla.Transform(carla.Location(x=0, z=2.5))
        self.lidar = self.world.spawn_actor(lidar_bp, lidar_transform, attach_to=self.vehicle)
        self.lidar.listen(self.process_lidar)
    
    def process_camera(self, image):
        # 转换图像数据
        img = np.array(image.raw_data)
        img = img.reshape((600, 800, 4))
        img = img[:, :, :3]  # 去除alpha通道
        
        # 目标检测处理
        # 这里可以插入3.1节的感知网络
        cv2.imshow('Camera View', img)
        cv2.waitKey(1)
    
    def process_lidar(self, point_cloud):
        # 转换点云数据
        points = np.frombuffer(point_cloud.raw_data, dtype=np.dtype('f4'))
        points = np.reshape(points, (int(points.shape[0]/4), 4))
        
        # 点云处理
        # 这里可以插入点云分割算法
        print(f"Received {points.shape[0]} LiDAR points")
    
    def run_step(self):
        # 获取车辆状态
        velocity = self.vehicle.get_velocity()
        speed = np.sqrt(velocity.x**2 + velocity.y**2 + velocity.z**2) * 3.6  # km/h
        
        # 简单控制逻辑
        control = carla.VehicleControl()
        if speed < 50:  # 如果速度低于50km/h
            control.throttle = 0.7
        else:
            control.throttle = 0.0
            control.brake = 0.1
        
        # 保持车道逻辑
        waypoints = self.world.get_map().get_waypoint(self.vehicle.get_location())
        next_waypoint = waypoints.next(10.0)[0]  # 获取前方10米的路点
        current_transform = self.vehicle.get_transform()
        wp_transform = next_waypoint.transform
        
        # 计算转向角度
        v_begin = current_transform.location
        v_end = v_begin + carla.Location(x=10 * np.cos(current_transform.rotation.yaw * np.pi/180),
                                         y=10 * np.sin(current_transform.rotation.yaw * np.pi/180))
        v_vec = np.array([v_end.x - v_begin.x, v_end.y - v_begin.y, 0.0])
        w_vec = np.array([wp_transform.location.x - v_begin.x,
                          wp_transform.location.y - v_begin.y,
                          0.0])
        angle = np.math.atan2(np.linalg.norm(np.cross(v_vec, w_vec)), np.dot(v_vec, w_vec))
        angle *= 180 / np.pi
        
        if angle > 1.0:  # 如果偏离车道
            control.steer = np.clip(angle * 0.01, -1.0, 1.0)
        
        return control