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. We introduce COOPERNAUT, an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving.

PDF

In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, June 2022.

@inproceedings{CVPR22-cui, 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}, booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month={June}, address={New Orleans, LA}, url="http://www.cs.utexas.edu/users/ai-lab?CVPR22-cui", year={2022} }