Toward Robust Collaborative Perception under Adverse Weather Conditions Via Dual-Branch Network

Abstract

Recent advances in collaborative perception systems have led to significant improvements in 3D object detection performance. While widely deployed LiDAR and camera systems often experience performance degradation under adverse weather conditions, weather-robust 4D radar offers a promising alternative to address this challenge. However, effectively fusing 4D radar measurements with degraded LiDAR data remains a critical challenge. In this work, we decompose the weather-induced degradation in LiDAR perception into feature attenuation requiring enhancement and feature contamination requiring suppression, based on the underlying physical interactions. Building upon this decomposition, we propose a dual-branch network to handle each degradation pattern in a specialized manner. One branch focuses on enhancement based on spatial and channel attention, guided by 4D radar cues. The other branch focuses on suppression based on intra-modal structural consistency and cross-modal consistency. To achieve adaptive branch integration, we propose a dynamic decision network to generate a decision weight map for each branch and capture the complex interaction between branches. To validate the effectiveness of our method, we conduct extensive experiments on V2X-R, the only publicly available collaborative LiDAR-4D radar dataset. Extensive experimental results demonstrate that our method achieves improvements of 3.65% and 10.80% in mAP@0.7 under fog and snow conditions, respectively, outperforming previous state-of-the-art approaches.