COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles
This addresses safety and efficiency issues for autonomous vehicles in dangerous driving situations, representing a novel method for a known bottleneck.
The paper tackles the problem of autonomous vehicle reliability by introducing COOPERNAUT, an end-to-end learning model for cooperative perception using vehicle-to-vehicle communications, which achieves a 40% improvement in average success rate over egocentric models in accident-prone scenarios and reduces bandwidth requirements by 5 times compared to prior work.
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/.