Collaborative Train and Edge Computing in Edge Intelligence Based Train Autonomous Operation Control Systems

火车 准时 强化学习 计算机科学 GSM演进的增强数据速率 边缘计算 计算 智能交通系统 计算卸载 人工智能 分布式计算 实时计算 工程类 土木工程 地图学 算法 运输工程 地理
作者
Li Zhu,Taiyuan Gong,Siyu Wei,F. Richard Yu
出处
期刊:IEEE Transactions on Intelligent Transportation Systems [Institute of Electrical and Electronics Engineers]
卷期号:25 (9): 11991-12004 被引量:2
标识
DOI:10.1109/tits.2024.3382747
摘要

Train autonomous circumambulate systems (TACS) are a new-generation train control systems. They are characterized by autonomous travel path planning, autonomous protection, and autonomous train operation adjustment. One crucial problem in TACS is real-time communication and computation of autonomous train control systems. Trains need to obtain the real-time state of all the other trains and derive real-time intelligent control commands in TACS. With high capacity and reliable 5G technologies, edge intelligence (EI) can perform complex computing tasks offloaded from trains with little delay. In this paper, we develop a collaborative train and edge computing framework for TACS to provide real-time communication and computation service for train control. To reduce the tracking deviations and ensure the train operation punctuality, ride comfort, and energy-saving ability, we adopt the model predictive control (MPC) algorithm to optimize the autonomous train control process. To cope with the limited onboard computing power, we propose a meta reinforcement learning (MRL) based collaborative computing method to solve the computation offloading problem. Compared with the existing RL-based offloading policy that requires sufficient data samples for training, MRL can rapidly adapt to different computation offloading environments, which is exceptionally suited for the urban rail transit system where different rail lines have different operating environments, and we do not have enough data to finish a regular reinforcement learning and training task. Experimental results illustrate that the proposed framework can provide TACS with reliable and real-time computing services. The train operational efficiency can be significantly improved with our proposed collaborative computing train control algorithm.
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