Real-Time 3D Object Detection with Distance-Aware Hybrid Point Cloud Representation toward Long Range Detection

3D object detection is a crucial technology for enabling automated driving. Methods with LiDAR employing Bird’s-Eye View (BEV) representations of point cloud in a Cartesian coordinate system have become mainstream. This technology requires recognizing objects at long distances, exceeding 100 m. In autonomous driving scenarios, such as when monitoring oncoming lanes for a lane change across opposing traffic, long-range perception is essential. However, methods using BEV representations of point cloud face a significant challenge: the computational resources required increase enormously when attempting to recognize objects at long distances. To address this, we propose a novel approach that combines Cartesian and polar coordinate systems. This method efficiently enables perception of objects up to approximately 140 m away, all while maintaining high detection accuracy for small objects at short-range. Furthermore, with real-time operation in mind, we focused on accelerating our method and achieved an inference time of 83.30 ms per frame.