Abstract
The deployment of Autonomous Vehicles (AVs) poses considerable challenges and unique opportunities for the design and management of future urban road infrastructure. In light of this disruptive transformation, the Right-Of-Way (ROW) composition of road space has the potential to be renewed. Design approaches and intelligent control models have been proposed to address this problem, but we lack an operational framework that can dynamically generate ROW plans for AVs and pedestrians in response to real-time demand. Based on microscopic traffic simulation, this study explores Reinforcement Learning (RL) methods for evolving ROW compositions. We implement a centralised paradigm and a distributive learning paradigm to separately perform the dynamic control on several road network configurations. Experimental results indicate that the algorithms have the potential to improve traffic flow efficiency and allocate more space for pedestrians. Furthermore, the distributive learning algorithm outperforms its centralised counterpart regarding computational cost (49.55\%), benchmark rewards (25.35\%), best cumulative rewards (24.58\%), optimal actions (13.49\%) and rate of convergence. This novel road management technique could potentially contribute to the flow-adaptive and active mobility-friendly streets in the AVs era.
Abstract (translated)
无人驾驶汽车(AVs)的部署为未来城市道路基础设施的设计和管理提出了巨大的挑战和独特的机会。考虑到这种破坏性变革,道路空间的 Right-Of-Way (ROW) 组成有潜力得到更新。为此,提出了设计方法和智慧控制模型来解决这个问题,但缺乏能够根据实时需求动态生成 AVs 和行人 ROW 计划的 operational 框架。基于微观交通模拟,本研究探索了演化 ROW 组成的强化学习(RL)方法。我们分别实现了集中范式和分散学习范式,对多个道路网络配置进行了动态控制。实验结果表明,算法有潜力提高交通流量效率和分配更多空间给行人。此外,分散学习算法在计算成本(49.55%)、基准奖励(25.35%)、最佳累积奖励(24.58%)、最优行动(13.49%)和收敛速度等方面表现优异。这一创新的道路管理技术可能有助于在 AVs 时代的行人流动自适应和主动移动友好的街道。
URL
https://arxiv.org/abs/2303.12289
