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arXiv · 2506.00837

Improving Multi-Vehicle Perception Fusion with Millimeter-Wave Radar Assistance

Zhiqing Luo·Yi Wang·Yingying He·Wei Wang
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Abstract

Cooperative perception enables vehicles to share sensor readings and has become a new paradigm to improve driving safety, where the key enabling technology for realizing this vision is to real-time and accurately align and fuse the perceptions. Recent advances to align the views rely on high-density LiDAR data or fine-grained image feature representations, which however fail to meet the requirements of accuracy, real-time, and adaptability for autonomous driving. To this end, we present MMatch, a lightweight system that enables accurate and real-time perception fusion with mmWave radar point clouds. The key insight is that fine-grained spatial information provided by the radar present unique associations with all the vehicles even in two separate views. As a result, by capturing and understanding the unique local and global position of the targets in this association, we can quickly find out all the co-visible vehicles for view alignment. We implement MMatch on both the datasets collected from the CARLA platform and the real-world traffic with over 15,000 radar point cloud pairs. Experimental results show that MMatch achieves decimeter-level accuracy within 59ms, which significantly improves the reliability for autonomous driving.

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Zhiqing Luo, Yi Wang, Yingying He, Wei Wang. 2025-06-01. Improving Multi-Vehicle Perception Fusion with Millimeter-Wave Radar Assistance. https://arxiv.org/abs/2506.00837

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