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arXiv · 2508.16030

CoVeRaP: Cooperative Vehicular Perception through mmWave FMCW Radars

Jinyue Song·Hansol Ku·Jayneel Vora·Nelson Lee·Ahmad Kamari·Prasant Mohapatra·Parth Pathak
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Abstract

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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Jinyue Song, Hansol Ku, Jayneel Vora, Nelson Lee, Ahmad Kamari, Prasant Mohapatra, Parth Pathak. 2025-08-22. CoVeRaP: Cooperative Vehicular Perception through mmWave FMCW Radars. https://arxiv.org/abs/2508.16030

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