RAFDet: A Novel Camera-Radar Fusion Framework for Robust 3D Object Detection in Autonomous Driving

Abstract

Accurate and reliable 3D object detection is crucial for autonomous driving, normally achieved using camera-only or camera-LiDAR fusion methods based on BEV (Bird’s Eye View) perspective. However, visual perception through cameras alone faces significant challenges, such as ambiguous depth estimation and poor performance in low-light, while camera-LiDAR fusion, though robust, are expensive on deployment and have limitations in dust and fog weather conditions. To address these issues, we propose RAFDet (Radar-Assisted Fusion Detection), a novel multi-modality fusion framework integrating camera and radar data for enhanced 3D detection. RAFDet utilizes radar RF (Radio Frequency) images to enrich spatial details, boosting fusion effects, while radar point clouds provide precise depth information to rectify visual depth predictions. In addition, the Edge-Awareness Feature Enhancement mechanism compensates for the sparsity of radar points, further refining depth estimation and detection accuracy. Our extensive experiments demonstrate the superiority of RAFDet over current methods, highlighting its potential for autonomous driving systems. Key results show significant improvements in depth prediction and overall performance metrics, validating the effectiveness of our approach. Our data collection and testing in real traffic scenarios also reflects the robustness, accuracy, and cost-effectiveness of RAFDet for 3D object detection in autonomous driving.