Introduction to Object Detection in Autonomous Vehicles
Object detection is a critical component of perception systems in autonomous vehicles (AVs), enabling the vehicle to identify and locate objects such as pedestrians, vehicles, and traffic signs in real-time from sensor data. Building on prerequisites like LiDAR principles and camera/radar fundamentals, this knowledge point focuses on applying advanced algorithms to process this data for safe navigation.
In AVs, sensors like cameras provide RGB images, LiDAR generates 3D point clouds, and radar offers velocity information. Object detection algorithms analyze this multi-modal data to output bounding boxes around detected objects, along with class labels and confidence scores.
Key Challenges:
- Handling varying lighting, weather, and occlusions in camera data.
- Processing sparse point clouds from LiDAR for 3D detection.
- Achieving real-time performance (e.g., >30 FPS) on embedded hardware.
For example, detecting a pedestrian in a camera feed involves identifying their position to predict collision risks, directly informing path planning.
Basic formulation: Given an input image $I$, the detector outputs tuples $(b_x, b_y, b_w, b_h, c, p)$, where $(b_x, b_y, b_w, b_h)$ is the bounding box, $c$ is the class, and $p$ is the confidence.