RaLiBEV: Radar and LiDAR BEV Fusion Learning for Anchor Box Free Object Detection Systems

Yanlong Yang, Jianan Liu, Tao Huang, Qing-Long Han, Gang Ma, Bing Zhu

Abstract: In autonomous driving, LiDAR and radar are crucial for environmental perception. LiDAR offers precise 3D spatial sensing information but struggles in adverse weather like fog. Conversely, radar signals can penetrate rain or mist due to their specific wavelength but are prone to noise disturbances. Recent state-of-the-art works reveal that the fusion of radar and LiDAR can lead to robust detection in adverse weather. Current approaches typically fuse features from various data sources using basic convolutional/transformer network architectures and employ straightforward label assignment strategies for object detection. However, these methods have two main limitations: they fail to adequately capture feature interactions and lack consistent regression constraints. In this paper, we propose a bird’s-eye view fusion learning-based anchor box-free object detection system. Our approach introduces a novel interactive transformer module for enhanced feature fusion and an advanced label assignment strategy for more consistent regression, addressing key limitations in existing methods. Specifically, experiments show that, our approach’s average precision ranks $1^{st}$ and significantly outperforms the state-of-the-art method by 13.1% and 19.0% at Intersection of Union (IoU) of 0.8 under “Clear+Foggy” training conditions for “Clear” and “Foggy” testing, respectively. Our code repository is available at: https://github.com/yyxr75/RaLiBEV.