Physical AI and Autonomous Vehicles — Complete Guide to Self-Driving Technology

# Simplified AV perception pipeline (conceptual)
# Real systems are far more complex with specialized hardware

class AutonomousVehicleAI:
    def __init__(self):
        # Multiple AI models running in parallel at 30-60 Hz
        self.object_detector = load_model('yolo_v8_automotive')    # camera → objects
        self.lidar_segmenter = load_model('pointnet_v2')           # LiDAR → 3D objects
        self.lane_detector = load_model('lanedet_transformer')     # camera → lane lines
        self.behavior_predictor = load_model('trajectron_plus')    # objects → future positions
        self.motion_planner = load_model('nuplan_transformer')     # planning → trajectory

    def perception_step(self, camera_frames, lidar_points, radar_data):
        """Run at 30-60 Hz — must complete in <16ms for 60fps."""

        # 1. Detect objects in each camera view
        camera_objects = self.object_detector(camera_frames)

        # 2. Segment LiDAR point cloud into 3D bounding boxes
        lidar_objects = self.lidar_segmenter(lidar_points)

        # 3. Fuse camera + LiDAR + radar into unified world model
        world_model = sensor_fusion(camera_objects, lidar_objects, radar_data)

        # 4. Predict where each object will be in 5 seconds
        predictions = self.behavior_predictor(world_model, history=self.object_history)

        # 5. Plan a safe trajectory given predictions and route
        trajectory = self.motion_planner(
            world_model, predictions,
            route=self.current_route,
            constraints=['stay_in_lane', 'obey_speed_limit', 'avoid_collision']
        )

        return trajectory  # → steering, acceleration, braking commands