CoCam4D: Geometry-Aware Cooperative 4D Perception for Camera-Only Autonomous Driving
We propose CoCam4D, a Bayesian framework for collaborative perception that explicitly models geometric uncertainty.
ProofPaper ↗
Key points
- Autonomous vehicles often suffer from limited perception due to occlusions, blind spots, limited sensor range, and the complex nature of surrounding environments.
- Multi-agent collaborative perception (CP) addresses these challenges by allowing vehicles to share sensory information and reconstruct the scene cooperatively.
- To support real-world deployment, we introduce Dynamic Object Primitives (DOPs), a compact 35-byte representation designed for efficient C-V2X communication.
- Extensive experiments show that our proposed method consistently outperforms recent vision-only methods, achieving improvements of 11.48% on OPV2V+ and 10.62% on DAIR-V2X-C, demonstrating the potential of geometrically grounded collaborative perception for LiDAR-free autonomous driving.
Sources (1)
- [1]CoCam4D: Geometry-Aware Cooperative 4D Perception for Camera-Only Autonomous DrivingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 09:31 AM
We propose CoCam4D, a Bayesian framework for collaborative perception that explicitly models geometric uncertainty.
Autonomous vehicles often suffer from limited perception due to occlusions, blind spots, limited sensor range, and the complex nature of surrounding environments.
Extractive summary: sentences quoted from the sources.