RLRFusion: RCS-based LiDAR-Radar Fusion for 3D Object Detection

In the field of autonomous driving and intelligent transportation systems, 3D object detection plays a critical role in ensuring safe and efficient driving. Achieving reliable object detection relies on robust sensing technologies, where LiDAR provides accurate spatial perception, and radar offers extended detection range and speed information because of its longer wave-lengths. To capitalize on the strengths of both sensors, a novel RCS-based LiDAR-Radar fusion network, named RLRFusion, is proposed for 3D object detection in this paper. The network takes LiDAR and radar point clouds as inputs and processes them through dual bird's-eye view (BEV) feature extraction streams, followed by the BEV fusion and detection module to produce the detection results. In input-level fusion, a cross-modal pillar encoder is introduced to address the sparse radar data and its lack of height information. In feature-level fusion, an RCS-aware fusion encoder leverages the Radar Cross Section (RCS) distribution by mapping pillar features to their surroundings, enhancing object size estimation and mitigating the challenges faced by LiDAR in adverse weather conditions. Experimental results show that RLRFusion achieves competitive performance on the nuScenes dataset, with strong detection results even in rainy conditions. The source code of our method is available at: https://github.com/djZzgroupIRLRFusion.