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联合优化多车运动。然而，现有交叉口自主管理方法大多仅考虑全自动驾驶场景。实际中，混合交通（即自动驾驶车辆与人类驾驶车辆同时使用道路）将普遍存在。本文提出利用强化学习与基于图的场景表示实现协同多智能体规划。我们基于先前证明此类机器学习方法适用于全自动驾驶交通的研究进行扩展。针对混合交通场景，扩展后的场景表示考虑了人类驾驶员意图的不确定性。在基于仿真的评估中，我们通过噪声过程对测量不确定性进行建模，并使用真实数据调优参数。本文以增强型先到先得方案（混合交通管理基线）为基准评估所提方法。随着自动驾驶车辆占比增加，该学习型规划器可显著提升车辆通行效率并降低交互延迟。非自动驾驶车辆几乎同等受益。