Cooperative maneuver planning promises to significantly improve traffic efficiency at unsignalized intersections by leveraging connected automated vehicles. Previous works on this topic have been mostly developed for completely automated traffic in a simple simulated environment. In contrast, our previously introduced planning approaches are specifically designed to handle real-world mixed traffic. The two methods are based on multi-scenario prediction and graph-based reinforcement learning, respectively. This is the first study to perform evaluations in a novel mixed traffic simulation framework as well as real-world drives with prototype connected automated vehicles in public traffic. The simulation features the same connected automated driving software stack as deployed on one of the automated vehicles. Our quantitative evaluations show that cooperative maneuver planning achieves a substantial reduction in crossing times and the number of stops. In a realistic environment with few automated vehicles, there are noticeable efficiency gains with only slightly increasing criticality metrics.

翻译：协同机动规划有望通过利用联网自动驾驶车辆，显著提升无信号交叉口的交通效率。该领域的先前研究大多针对简单模拟环境中的完全自动化交通场景。相比之下，我们先前提出的规划方法专门设计用于处理真实世界的混合交通流。这两种方法分别基于多场景预测和图强化学习。本研究首次在新型混合交通仿真框架以及真实世界的公共交通环境中，使用原型联网自动驾驶车辆进行了评估。该仿真框架采用了与其中一辆自动驾驶车辆上部署的完全相同的联网自动驾驶软件栈。定量评估结果表明，协同机动规划能显著缩短通过时间并减少停车次数。在自动驾驶车辆较少的现实环境中，该方法在关键性指标仅有轻微上升的同时，实现了显著的效率提升。