Automated Driving Lab, Ohio State University, Columbus, OH 43210, USA.
Sensors (Basel). 2022 Oct 29;22(21):8317. doi: 10.3390/s22218317.
Ride-hailed shared autonomous vehicles (SAV) have emerged recently as an economically feasible way of introducing autonomous driving technologies while serving the mobility needs of under-served communities. There has also been corresponding research work on optimization of the operation of these SAVs. However, the current state-of-the-art research in this area treats very simple networks, neglecting the effect of a realistic other traffic representation, and is not useful for planning deployments of SAV service. In contrast, this paper utilizes a recent autonomous shuttle deployment site in Columbus, Ohio, as a basis for mobility studies and the optimization of SAV fleet deployment. Furthermore, this paper creates an SAV dispatcher based on reinforcement learning (RL) to minimize passenger wait time and to maximize the number of passengers served. The created taxi-dispatcher is then simulated in a realistic scenario while avoiding generalization or over-fitting to the area. It is found that an RL-aided taxi dispatcher algorithm can greatly improve the performance of a deployment of SAVs by increasing the overall number of trips completed and passengers served while decreasing the wait time for passengers.
打车共享自动驾驶车辆（SAV）最近作为一种经济可行的方式出现，引入自动驾驶技术，同时满足服务不足社区的交通需求。也有相应的优化这些 SAV 运营的研究工作。然而，该领域当前的最新研究对待非常简单的网络，忽略了现实中其他交通表示的影响，并且对于规划 SAV 服务的部署没有实际意义。相比之下，本文利用俄亥俄州哥伦布市最近的自动驾驶班车部署站点作为移动性研究和 SAV 车队部署优化的基础。此外，本文基于强化学习（RL）创建了一个 SAV 调度器，以最小化乘客等待时间并最大化服务的乘客数量。然后在现实场景中模拟创建的出租车调度器，避免泛化或过度拟合该区域。研究发现，借助 RL 的出租车调度算法可以通过增加完成的总行程和服务的乘客数量，同时减少乘客的等待时间，极大地提高 SAV 部署的性能。
