Coopernaut: End-to-End Driving with Cooperative Perception for Networked Vehicles
Jiaxun Cui, Hang Qiu, Dian Chen, Peter Stone, Yuke Zhu
Abstract
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 Coopernaut,an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving. Our model encodes Li-DAR information into compact point-based representations that can be transmitted as messages between vehicles via realistic wireless channels. To evaluate our model, we develop Autocastsim,a network-augmented driving simulation framework with example accident-prone scenarios. Our experiments on Autocastsim suggest that our cooperative perception driving models lead to a 40% improvement in average success rate over egocentric driving mod-els in these challenging driving situations and a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5\times$</tex> smaller bandwidth requirement than prior work V2VNet. Cooper-nautand Autocastsim are available at https://ut-austin-rpl.github.io/Coopernaut/.