CenterRadarNet: Joint 3D Object Detection and Tracking Framework Using 4D FMCW Radar
Jen‐Hao Cheng, Sheng-Yao Kuan, Hou-I Liu, Hugo Latapie, Gaowen Liu, Jenq–Neng Hwang
Abstract
Robust perception is a vital component for ensuring safe autonomous driving. Automotive radar (77 to 81 GHz) offering weather-resilient sensing provides a complementary capability to the vision-or LiDAR-based autonomous driving systems. Raw radio-frequency (RF) radar tensors contain rich spatiotemporal semantics besides 3D location information. Most previous methods take in 3D (Doppler-range-azimuth) RF radar tensors, allowing prediction of an object’s location, heading angle, and size in bird’s-eye-view (BEV). However, they lack the ability to simultaneously infer objects’ size, orientation, and identity in the 3D space. To overcome this limitation, we propose a joint architecture, called CenterRadarNet, designed to facilitate high-resolution representation learning from 4D (Doppler-range-azimuth-elevation) radar data for 3D object detection and re-identification (reID) tasks. Moreover, we build an online tracker utilizing the learned appearance embedding for re-ID. CenterRadarNet achieves the state-of-the-art result on the K-Radar 3D object detection benchmark. In addition, we present the first 3D object-tracking result on the K-Radar dataset. CenterRadarNet shows consistent, robust performance in diverse driving scenarios, emphasizing its wide applicability. Code is available at: https://github.com/Andy-Cheng/CenterRadarNet