Automation and electrification are inevitable trends in the development of intelligent vehicles. It is envisioned that automated electric taxis (AETs) will play an important role in future transportation systems for serving personalized travel demands. To tackle the operational challenges caused by the high spatiotemporal heterogeneity of customer demands entails novel online strategy to intelligently manage AET fleet. This study proposes an advisor-student reinforcement learning framework to solve the online operations problem of AET fleet through which the taxis are intelligently assigned to serve demands, dispatched to zones with excessive future demands, and forced to get refueled at charging stations. Extensive numerical experiments illustrate the advantages of the proposed framework over myopic and nearest distance greedy strategies, especially when vehicle relocation is highly needed.

自动化和电气化是智能汽车发展的必然趋势。可以预见，自动电动出租车（AET）将在满足个性化旅行需求的未来运输系统中扮演重要角色。要解决因客户需求时空异构性高而引起的运营挑战，就需要采用新颖的在线策略来智能管理AET机队。这项研究提出了一个顾问学生强化学习框架，以解决AET车队的在线运营问题，通过该框架智能地分配出租车来满足需求，将其分配到未来需求过高的区域，并被迫在充电站加油。大量的数值实验说明了所提出的框架优于近视和最近距离贪婪策略的优势，特别是在非常需要车辆重新安置时。