CoVeRaP: Cooperative Vehicular Perception through mmWave FMCW Radars
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918128919773184 |
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| author | Song, Jinyue Ku, Hansol Vora, Jayneel Lee, Nelson Kamari, Ahmad Mohapatra, Prasant Pathak, Parth |
| author_facet | Song, Jinyue Ku, Hansol Vora, Jayneel Lee, Nelson Kamari, Ahmad Mohapatra, Prasant Pathak, Parth |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16030 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CoVeRaP: Cooperative Vehicular Perception through mmWave FMCW Radars Song, Jinyue Ku, Hansol Vora, Jayneel Lee, Nelson Kamari, Ahmad Mohapatra, Prasant Pathak, Parth Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Networking and Internet Architecture 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. |
| title | CoVeRaP: Cooperative Vehicular Perception through mmWave FMCW Radars |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2508.16030 |