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

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.