4-D Radar Meets LiDAR and Camera: Cooperative Perception under Adverse Weather

Melih Yazgan, Iramm Hamdard, Qiyuan Wu, Svetlana Pavlitska, J. Marius Zöllner

Keywords: Cooperative Perception, Adverse Weather

TL;DR: Integrating 4D radar via Doppler-guided attention to achieve robust, all-weather collaborative perception for autonomous driving.

Abstract: Cooperative perception is critical for autonomous driving but remains highly fragile when cameras and LiDAR degrade in adverse weather. We address this critical limitation by elevating 4D imaging radar to a first-class modality within collaborative frameworks and introducing the first Doppler-guided spatial attention mechanism for multi-agent fusion. Our approach extends two representative backbones: (i) radar substitutes LiDAR to form a radar-camera pipeline, and (ii) radar complements LiDAR to form a LiDAR-radar pipeline. A Doppler-derived mask dynamically emphasizes moving objects while preserving static context, significantly enhancing robustness in cluttered and low-visibility scenes. To support comprehensive evaluation, we release radar-augmented benchmarks (OPV2V-R and Adver-City-R) featuring physics-based LiDAR degradation. Experiments demonstrate that substituting LiDAR with radar nearly doubles baseline detection accuracy in fog, while our Doppler-guided attention provides the essential refinement needed to achieve high precision. Furthermore, our LiDAR-radar fusion equipped with this attention mechanism achieves state-of-the-art robustness under heavy rain and fog. Additional validation on the real-world TruckScenes dataset confirms that our Doppler-guided radar modules transfer effectively beyond simulation, firmly establishing 4D radar as a primary modality for all-weather collaborative perception.

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Submission Number: 3