论文标题
探索人类驾驶模仿与交通模拟安全性之间的权衡
Exploring the trade off between human driving imitation and safety for traffic simulation
论文作者
论文摘要
对自动驾驶车辆性能的定量评估，交通模拟引起了很多兴趣。为了使模拟器成为有价值的测试工作台，要求对现场每个交通代理的驾驶策略动画，就像人类在保持最低安全保证的同时一样。从记录的人类驾驶数据或通过增强学习中学习交通代理的驾驶政策似乎是在不受控制的交叉点或回旋处中产生现实且高度互动的交通状况的有吸引力的解决方案。在这项工作中，我们表明，在学习驾驶政策时模仿人类驾驶与保持安全性之间存在权衡。我们通过比较应用于驾驶任务时的各种模仿学习和强化学习算法的性能来做到这一点。我们还提出了一种多物镜学习算法（MOPPO），该算法共同提高了两个目标。我们在从交互数据集中提取的高度互动驾驶场景上测试驾驶政策，以评估它们的表现如何。
Traffic simulation has gained a lot of interest for quantitative evaluation of self driving vehicles performance. In order for a simulator to be a valuable test bench, it is required that the driving policy animating each traffic agent in the scene acts as humans would do while maintaining minimal safety guarantees. Learning the driving policies of traffic agents from recorded human driving data or through reinforcement learning seems to be an attractive solution for the generation of realistic and highly interactive traffic situations in uncontrolled intersections or roundabouts. In this work, we show that a trade-off exists between imitating human driving and maintaining safety when learning driving policies. We do this by comparing how various Imitation learning and Reinforcement learning algorithms perform when applied to the driving task. We also propose a multi objective learning algorithm (MOPPO) that improves both objectives together. We test our driving policies on highly interactive driving scenarios extracted from INTERACTION Dataset to evaluate how human-like they behave.
