用毫米波雷达实现多车协同感知，显著提升目标检测精度。
CoVeRaP: Cooperative Vehicular Perception through mmWave FMCW Radars
- 设计多车雷达-视觉-GPS同步数据集，支持协同感知研究。
- 融合强度信息的中层融合使检测精度在高重叠率下提升9倍。
- 适合自动驾驶、智能交通系统研究者参考使用。
汽车级FMCW雷达在雨天和强光下仍具可靠性，但其稀疏且噪声大的点云限制了三维目标检测性能。为此，我们发布CoVeRaP，一个包含21,000帧的协作感知数据集，对多辆车辆在不同驾驶行为下的雷达、相机和GPS流进行时间对齐。基于该数据集，提出统一的协作感知框架，支持中层与晚层融合。其基础网络采用多分支PointNet式编码器，并引入自注意力机制，融合空间、多普勒和强度特征至共享隐空间，解码器生成3D边界框与每点深度置信度。实验表明，加入强度编码的中层融合在IoU=0.9时，平均精度提升高达9倍，且持续优于单车基线。CoVeRaP建立了首个可复现的多车毫米波雷达感知基准，证明低成本雷达共享能显著提升检测鲁棒性。数据集与代码已公开，以促进后续研究。
原文摘要 · Abstract (English)
Automotive FMCW radars remain reliable in rain and glare, yet their sparse, noisy point clouds constrain 3-D object detection. We therefore release CoVeRaP, a 21 k-frame cooperative dataset that time-aligns radar, camera, and GPS streams from multiple vehicles across diverse manoeuvres. Built on this data, we propose a unified cooperative-perception framework with middle- and late-fusion options. Its baseline network employs a multi-branch PointNet-style encoder enhanced with self-attention to fuse spatial, Doppler, and intensity cues into a common latent space, which a decoder converts into 3-D bounding boxes and per-point depth confidence. Experiments show that middle fusion with intensity encoding boosts mean Average Precision by up to 9x at IoU 0.9 and consistently outperforms single-vehicle baselines. CoVeRaP thus establishes the first reproducible benchmark for multi-vehicle FMCW-radar perception and demonstrates that affordable radar sharing markedly improves detection robustness. Dataset and code are publicly available to encourage further research.
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