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

TL;DR

This paper introduces MMatch, a lightweight system that leverages millimeter-wave radar data to enable accurate, real-time perception fusion among vehicles, significantly enhancing autonomous driving safety and reliability.

Contribution

MMatch is the first system to utilize mmWave radar point clouds for fast, precise multi-vehicle perception fusion, addressing limitations of previous high-density LiDAR and image-based methods.

Findings

Achieves decimeter-level accuracy within 59ms

Effective in both simulated and real-world datasets

Improves perception reliability for autonomous driving

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.