The management of mixed traffic that consists of robot vehicles (RVs) and human-driven vehicles (HVs) at complex intersections presents a multifaceted challenge. Traditional signal controls often struggle to adapt to dynamic traffic conditions and heterogeneous vehicle types. Recent advancements have turned to strategies based on reinforcement learning (RL), leveraging its model-free nature, real-time operation, and generalizability over different scenarios. We introduce a hierarchical RL framework to manage mixed traffic through precise longitudinal and lateral control of RVs. Our proposed hierarchical framework combines the state-of-the-art mixed traffic control algorithm as a high level decision maker to improve the performance and robustness of the whole system. Our experiments demonstrate that the framework can reduce the average waiting time by up to 54% compared to the state-of-the-art mixed traffic control method. When the RV penetration rate exceeds 60%, our technique consistently outperforms conventional traffic signal control programs in terms of the average waiting time for all vehicles at the intersection.

翻译：复杂交叉口中包含机器人车辆（RVs）和人类驾驶车辆（HVs）的混合交通管理是一项多面性挑战。传统信号控制往往难以适应动态交通状况和异质车辆类型。近年来的进展转向基于强化学习（RL）的策略，利用其无模型特性、实时操作能力和跨场景泛化能力。我们提出一种分层强化学习框架，通过对机器人车辆进行精确的纵向和横向控制来管理混合交通。该框架将当前最先进的混合交通控制算法作为高层决策者，以提升整个系统的性能与鲁棒性。实验表明，相比最先进的混合交通控制方法，该框架可将平均等待时间降低最多54%。当机器人车辆渗透率超过60%时，我们的技术在交叉口所有车辆的平均等待时间上始终优于传统交通信号控制程序。