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

Sensor fusion is crucial for an accurate and robust perception system on autonomous vehicles. Most existing datasets and perception solutions focus on fusing cameras and LiDAR. However, the collaboration between camera and radar is significantly under-exploited. Incorporating rich semantic information from the camera and reliable 3D information from the radar can achieve an efficient, cheap, and portable solution for 3D perception tasks. It can also be robust to different lighting or all-weather driving scenarios due to the capability of mmWave radars. In this paper, we introduce the CRUW3D dataset, including 66K synchronized and well-calibrated camera, radar, and LiDAR frames in various driving scenarios. Unlike other large-scale autonomous driving datasets, our radar data is in the format of radio frequency (RF) tensors that contain not only 3D location information but also spatio-temporal semantic information. This kind of radar format can enable machine learning models to generate more reliable object perception results after interacting and fusing the information or features between the camera and radar. We run several camera- and radar-based baseline methods for 3D object detection and multi-object tracking on our dataset. We hope the CRUW3D dataset will foster radar and multi-modal 3D perception research. CRUW3D is available at https://huggingface.co/datasets/uwipl/CRUW3D

In the domain of autonomous driving, the integration of multi-modal perception techniques based on data from diverse sensors has demonstrated substantial progress. Effectively surpassing the capabilities of state-of-the-art single-modality detectors through sensor fusion remains an active challenge. This work leverages the respective advantages of cameras in perspective view and radars in Bird’s Eye View (BEV) to greatly enhance overall detection and tracking performance. Our approach, Camera-Radar Associated Fusion Tracking Booster (CRAFTBooster) represents a pioneering effort to enhance radar-camera fusion in the tracking stage, contributing to improved 3D MOT accuracy. The superior experimental results on K-Radaar dataset, which exhibit 5-6% on IDF1 tracking performance gain, validate the potential of effective sensor fusion in advancing autonomous driving.