用毫米波雷达点云实现多车感知实时精准融合
Improving Multi-Vehicle Perception Fusion with Millimeter-Wave Radar Assistance
- 利用毫米波雷达的细粒度空间信息建立车辆间关联
- 59毫秒内达到分米级精度，显著提升融合效率
- 适合需要实时高精度协同感知的自动驾驶场景
协同感知使车辆能够共享传感器数据，已成为提升行车安全的新范式，其实现核心在于实时准确地对齐与融合感知信息。当前视图对齐方法依赖高密度激光雷达数据或精细图像特征表示，但难以满足自动驾驶对精度、实时性和适应性的要求。为此，我们提出MMatch，一种轻量级系统，利用毫米波雷达点云实现精确且实时的感知融合。其关键洞察在于：雷达提供的细粒度空间信息能为所有车辆在不同视角下建立独特关联。通过捕捉目标在此关联中的局部与全局位置，可快速识别共可见车辆以完成视图对齐。我们在CARLA平台采集的数据集及真实交通环境中部署了MMatch，涵盖超过15,000对雷达点云。实验结果表明，MMatch可在59毫秒内实现分米级精度，显著提升自动驾驶系统的可靠性。
原文摘要 · Abstract (English)
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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