4D Radar Meets LiDAR and Camera: Cooperative Perception under Adverse Weather
For autonomous driving systems, this work provides a practical solution to maintain cooperative perception performance under adverse weather conditions where cameras and LiDAR fail.
This paper integrates 4D imaging radar into cooperative perception for autonomous driving to address sensor degradation under adverse weather. The proposed Doppler-guided spatial attention mechanism achieves significant robustness gains in fog and rain, with radar effectively replacing degraded LiDAR.
Cooperative perception is important for autonomous driving but remains fragile when cameras and LiDAR degrade in adverse weather. We address this challenge by integrating 4D imaging radar as a weather-robust modality into collaborative perception and introducing a Doppler-guided spatial attention mechanism for multi-agent fusion. Our approach extends two representative backbones: a radar-camera pipeline where radar substitutes LiDAR, and a LiDAR-radar pipeline where radar complements LiDAR. To support evaluation, we release radar-augmented benchmarks, OPV2V-R and Adver-City-R, with physics-based LiDAR degradation. Experiments show strong robustness gains in fog and rain, including substantial improvements when radar replaces degraded LiDAR. Additional validation on MAN TruckScenes demonstrates transfer beyond simulation. Overall, our results highlight 4D imaging radar as a robust modality for all-weather collaborative perception. Dataset and code are available at: https://url.fzi.de/SlimComm.