# COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles Source: [COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:48:10.793Z Authors: Jiaxun Cui, Hang Qiu, Dian Chen, Peter Stone, Yuke Zhu Published: 2022-05-04T17:55:12.000Z ## 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 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 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 models in these challenging driving situations and a 5 times smaller bandwidth requirement than prior work V2VNet. COOPERNAUT and AUTOCASTSIM are available at https://ut-austin-rpl.github.io/Coopernaut/. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Open-loop trajectory planning on DAIR-V2X-Seq (val)](): Open-Loop Trajectory Planning; DAIR-V2X-Seq (val); L2 Error (1s): 2.69. ## Original sources and implementation [Original paper]() [PDF]()