Introduction to Object Detection in Self-Driving Cars
Object detection is a core component of perception systems in autonomous vehicles, enabling the car to identify and locate objects in real-time from sensor data like cameras, LiDAR, and radar. This process is crucial for safe navigation, as it allows the vehicle to recognize obstacles, pedestrians, traffic signs, and other vehicles.
In self-driving cars, object detection goes beyond simple image recognition by not only classifying objects (e.g., "car" or "cyclist") but also determining their precise locations and sizes in the environment. This is typically represented using bounding boxes, which are rectangular enclosures around detected objects.
Why it matters: Without accurate object detection, a self-driving car cannot predict trajectories or make decisions, such as braking for a pedestrian. For instance, in urban environments, detecting a child darting into the road from camera feeds can prevent accidents.
Prerequisites like sensor calibration ensure that data from multiple sensors is aligned, providing a clean input for detection algorithms.