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Coopernaut: End-to-End Driving with Cooperative Perception for Networked Vehicles (2022)
Jiaxun Cui and Hang Qiu and Dian Chen and
Peter Stone
and Yuke Zhu
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.
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PDF
Citation:
In
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
, New Orleans, LA, June 2022.
Bibtex:
@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://nn.cs.utexas.edu/?CVPR22-cui", year={2022} }
People
Peter Stone
pstone [at] cs utexas edu
Areas of Interest
Autonomous Driving
Robotics