Learning from All Vehicles

In this paper, we present a system to train driving policies from experiences collected not just from the ego-vehicle, but all vehicles that it observes. This system uses the behaviors of other agents to create more diverse driving scenarios without collecting additional data. The main difficulty in learning from other vehicles is that there is no sensor information. We use a set of supervisory tasks to learn an intermediate representation that is invariant to the viewpoint of the controlling vehicle. 2022: Dian Chen, Philipp Krahenbuhl Ranked #1 on Autonomous Driving on CARLA Leaderboard https://arxiv.org/pdf/2203.11934v1.pdf