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.

翻译：网联自动驾驶有望显著提升城市交通效率，例如通过缓解遮挡问题。协同行为规划可用于共同优化多辆车的运动轨迹。然而，现有自动交叉口管理方法大多仅考虑全自动化交通。实际场景中，混合交通（即自动化车辆与人工驾驶车辆同时使用道路）将普遍存在。本研究提出利用强化学习和基于图的场景表征实现多智能体协同规划。我们基于先前证明此类机器学习方法可应用于全自动化交通的研究成果，将场景表征扩展至混合交通，并考虑人工驾驶者意图的不确定性。在基于仿真的评估中，我们通过采用真实世界数据校准的噪声过程来建模测量不确定性。本文将该方法与增强型先到先服务方案（混合交通管理基准方法）进行对比。随着自动化车辆比例增加，所学习的规划器显著提升车辆通行率并降低交互导致的延误。非自动化车辆同样受益。