Autonomous vehicles (AVs) must detect objects in advance for timely action to ensure driving safety. That said, AVs must accurately detect far-field objects while running at high speeds. Because a 60mph vehicle requires 60-meter stopping distance, AVs must detect far-field obstacles to avoid a potential collision into them. Interestingly, detecting far-field objects is also relevant for navigation in urban settings at modest speeds during precarious maneuvers, such as unprotected left turns where opposing traffic might be moving at 35mph, resulting in a relative speed of 70mph. These real-world scenarios motivate us to study the problem of far-field 3D object detection (Far3Det).
3D detection has been greatly advanced under AV research, largely owing to modern benchmarks that collect data using LiDAR (e.g., nuScenes, Waymo, and KITTI), which faithfully measures the 3D world and allows for precise localization in the 3D world. However, these benchmarks evaluate detections only up to a certain distance (i.e., within 50 meters from the ego-vehicle), presumably because near-field objects are more important that have an immediate impact on AVs’ motion plans. However, the aforementioned scenarios demonstrate that Far3Det, the 3D detection of objects beyond this distance (>= 50m), is also crucial.