This paper presents a methodology to control the trajectory of cooperative connected automated vehicles (CAVs) at roundabouts with a mixed fleet of CAVs and human-driven vehicles (HVs). We formulate an optimization program in a two-dimensional space for this purpose. A model predictive control-based solution technique is developed to optimize the trajectories of CAVs at discretized time steps based on the estimated driving behavior of HVs, while the actual behavior of HVs is controlled by a microscopic traffic simulator. At each time step, the location and speed of vehicles are collected, and a decomposition-based methodology optimizes CAV trajectories for a few time steps ahead of the system time. The optimization methodology has convexification, alternating direction method of multipliers, and cutting plane decomposition components to tackle the complexities of the problem. We tested the solution technique in a case study roundabout with different traffic demand flow rates and CAV market penetration rates. The results showed that increasing the CAV market penetration rate from 20% to 100% reduced total travel times by 2.8% to 35.8%. The analyses indicate that the presence of cooperative CAVs in roundabouts can lead to considerable improvements.

中文翻译：

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由自动和人工驾驶车辆混合组成的环形交叉路口轨迹控制

本文介绍了一种方法，用于控制环形交叉路口的协作联网自动驾驶车辆 (CAV) 的轨迹，其中包括 CAV 和人力驾驶车辆 (HV) 的混合车队。为此，我们在二维空间中制定了优化程序。开发了一种基于模型预测控制的解决方案技术，以基于 HV 的估计驾驶行为以离散时间步长优化 CAV 的轨迹，而 HV 的实际行为由微观交通模拟器控制。在每个时间步，收集车辆的位置和速度，并且基于分解的方法在系统时间之前的几个时间步优化 CAV 轨迹。优化方法有凸化，乘数交替方向法，和切割平面分解组件来解决问题的复杂性。我们在具有不同交通需求流率和 CAV 市场渗透率的案例研究环岛中测试了解决方案技术。结果表明，将 CAV 市场渗透率从 20% 提高到 100% 可将总出行时间减少 2.8% 至 35.8%。分析表明，环岛中协作 CAV 的存在可以带来相当大的改进。