• DOI:10.48550/arXiv.2205.02222
• Corpus ID: 248512704

COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles

@article{Cui2022COOPERNAUTED,
  title={COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles},
  author={Jiaxun Cui and Hang Qiu and Dian Chen and Peter Stone and Yuke Zhu},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.02222}
}

• Jiaxun Cui, Hang Qiu, +2 authors Yuke Zhu
• Published 4 May 2022
• Computer Science
• ArXiv

Optical sensors and learning algorithms for autonomous vehicles have dramatically advanced in the past few years. Nonetheless, the reliability of today’s autonomous vehicles is hindered by the limited line-of-sight sensing capability and the brittleness of data-driven methods in handling extreme situations. With recent developments of telecommunication technologies, cooperative perception with vehicle-to-vehicle communications has become a promising paradigm to enhance autonomous driving in dangerous or emergency situations. We introduce C OOPERNAUT , an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving. Our model encodes LiDAR information into compact point-based representations that can be transmitted as messages between vehicles via realistic wireless channels. To evaluate our model, we develop A UTO C AST S IM , a network-augmented driving simulation framework with example accident-prone scenarios. Our experiments on A UTO C AST S IM suggest that our cooperative perception driving models lead to a 40% improve-ment in average success rate over egocentric driving models in these challenging driving situations and a 5 × smaller bandwidth requirement than prior work V2VNet. C OOPER NAUT and A UTO C AST S IM are available at https:// ut-austin-rpl.github.io/Coopernaut/ .