We demonstrate the first large-scale application of model-based generative
adversarial imitation learning (MGAIL) to the task of dense urban self-driving.
We augment standard MGAIL using a hierarchical model to enable generalization
to arbitrary goal routes, and measure performance using a closed-loop
evaluation framework with simulated interactive agents. We train policies from
expert trajectories collected from real vehicles driving over 100,000 miles in
San Francisco, and demonstrate a steerable policy that can navigate robustly
even in a zero-shot setting, generalizing to synthetic scenarios with novel
goals that never occurred in real-world driving. We also demonstrate the
importance of mixing closed-loop MGAIL losses with open-loop behavior cloning
losses, and show our best policy approaches the performance of the expert. We
evaluate our imitative model in both average and challenging scenarios, and
show how it can serve as a useful prior to plan successful trajectories.

使用 MGAIL 在密集城市自动驾驶方面进行了第一次大规模应用，通过使用分层模型和经过现实汽车采集的经验轨迹进行性能度量，并结合闭环 MGAIL 损失和开环行为克隆损失实现了稳健的导航策略。