Abstract
Connected automated driving has the potential to significantly improve urban traffic efficiency, e.g., by alleviating issues due to occlusion. Cooperative behavior planning can be employed to jointly optimize the motion of multiple vehicles. Most existing approaches to automatic intersection management, however, only consider fully automated traffic. In practice, mixed traffic, i.e., the simultaneous road usage by automated and human-driven vehicles, will be prevalent. The present work proposes to leverage reinforcement learning and a graph-based scene representation for cooperative multi-agent planning. We build upon our previous works that showed the applicability of such machine learning methods to fully automated traffic. The scene representation is extended for mixed traffic and considers uncertainty in the human drivers' intentions. In the simulation-based evaluation, we model measurement uncertainties through noise processes that are tuned using real-world data. The paper evaluates the proposed method against an enhanced first in - first out scheme, our baseline for mixed traffic management. With increasing share of automated vehicles, the learned planner significantly increases the vehicle throughput and reduces the delay due to interaction. Non-automated vehicles benefit virtually alike.
Abstract (translated)
联网自动驾驶有潜力 significantly 改善城市交通效率,例如通过减轻因遮挡等问题引起的问题。合作行为规划可以用于 jointly 优化 multiple 车辆的运动。但大多数现有的自动路口管理方法只考虑完全自动化交通。在实践中,混合交通,即自动化和人力驱动车辆同时使用的路面使用,将普遍存在。本研究提议利用强化学习和基于图的场景表示来进行合作多agent 规划。我们基于先前的工作,表明这些机器学习方法可以适用于完全自动化交通。场景表示扩展到了混合交通,并考虑了人类司机的意图不确定性。在基于模拟的评估中,我们通过使用实际数据调整的噪声过程来建模测量不确定性。论文评估了提出的方法与增强的先入先出计划,作为混合交通管理的基准。随着自动化车辆比例的增加,学习规划 significantly 增加了车辆的通过量和减少了因交互引起的延迟。非自动化车辆受益几乎相同。
URL
https://arxiv.org/abs/2301.12717
