论文标题
基于分层模型的模型学习用于自动驾驶的计划
Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving
论文作者
论文摘要
我们演示了基于模型的生成对抗模仿学习（Mgail）在密集的城市自动驾驶任务中的首次大规模应用。我们使用层次模型来增强标准MGAIL，以使概括为任意目标路线，并使用与模拟交互式的闭环评估框架来衡量性能。我们训练从旧金山驾驶超过100,000英里的真正车辆收集的专家轨迹的政策，并展示了一项可通行的政策，即使在零照片的环境中，也可以坚固地导航，从而推广到具有从未在现实世界中从未发生过新颖目标的合成场景。我们还展示了将闭环MGAIL损失与开环行为克隆损失相结合的重要性，并展示了我们最好的政策方法是专家的表现。我们在平均和具有挑战性的场景中评估了我们的模仿模型，并在计划成功的轨迹之前展示了它如何用作有用。
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.
