Papers › V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction
V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction
Tsun-Hsuan Wang, Sivabalan Manivasagam, Ming Liang, Bin Yang, Wenyuan Zeng, James Tu, Raquel Urtasun
In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the perception and motion forecasting performance of self-driving vehicles. By intelligently aggregating the information received from multiple nearby vehicles, we can observe the same scene from different viewpoints. This allows us to see through occlusions and detect actors at long range, where the observations are very sparse or non-existent. We also show that our approach of sending compressed deep feature map activations achieves high accuracy while satisfying communication bandwidth requirements.
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Code
Syntology Ran 2 of 2 code samples harvested from 1 repository linked to this paper; 0 have no recorded run. Of those that ran: 1 ran · our draft was wrong; 1 ran · fixture could not drive it.
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Code Syntology ran Syntology
2 samples harvested; 2 ran; 0 honoured the contract we drafted; 0 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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349015c9fdf7266b · report
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Object Detection | OPV2V | V2VNet (PointPillar backbone) | AP@0.7@CulverCity | 0.734 | #1 of 5 | Archive leaderboard | report |
| 3D Object Detection | OPV2V | V2VNet (PointPillar backbone) | AP@0.7@Default | 0.822 | #1 of 5 | Archive leaderboard | report |
| 3D Object Detection | V2X-SIM | V2VNet | mAOE | 0.349 | #4 of 5 | Archive leaderboard | report |
| 3D Object Detection | V2X-SIM | V2VNet | mAP | 21.4 | #4 of 5 | Archive leaderboard | report |
| 3D Object Detection | V2X-SIM | V2VNet | mASE | 0.255 | #4 of 5 | Archive leaderboard | report |
| 3D Object Detection | V2X-SIM | V2VNet | mATE | 0.768 | #4 of 5 | Archive leaderboard | report |
| 3D Object Detection | V2XSet | V2VNet | AP0.5 (Noisy) | 0.791 | #3 of 6 | Archive leaderboard | report |
| 3D Object Detection | V2XSet | V2VNet | AP0.5 (Perfect) | 0.845 | #3 of 6 | Archive leaderboard | report |
| 3D Object Detection | V2XSet | V2VNet | AP0.7 (Noisy) | 0.493 | #3 of 6 | Archive leaderboard | report |
| 3D Object Detection | V2XSet | V2VNet | AP0.7 (Perfect) | 0.677 | #3 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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