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. This not only provides a richer signal at training time but also allows more complex reasoning during inference. Learning how all vehicles drive helps predict their behavior at test time and can avoid collisions. We evaluate this system in closed-loop driving simulations. Our system outperforms all prior methods on the public CARLA Leaderboard by a wide margin, improving driving score by 25 and route completion rate by 24 points. Our method won the 2021 CARLA Autonomous Driving challenge. Code and data are available at https://github.com/dotchen/LAV.
翻译:在本文中,我们提出了一个系统,从不仅从自我车辆,而且从它所观察到的所有车辆收集到的经验中,来培训驾驶政策。这个系统使用其他代理人的行为来创造更多样化的驾驶方案,而不收集更多的数据。从其他车辆学习的主要困难是没有传感器信息。我们使用一套监督任务来学习一种中间代表,这种代表对控制车辆的观点是无法改变的。这不仅在培训时间提供了更丰富的信号,而且还允许在推断过程中进行更复杂的推理。学习所有车辆的驱动方式有助于预测它们在测试时间的行为,并避免碰撞。我们在闭路驾驶模拟中评估了这个系统。我们在CARLA主板上的系统比以往所有方法都大,将驾驶分数提高25分,完成路程率提高24点。我们的方法赢得了2021 CARLA自动驾驶的挑战。代码和数据见https://github.com/dotchen/LAV。