Train Tracking Interval Adjusting Strategy Based on Cooperative Perception for Train Autonomous Operation

With the continuous growth in passenger of high-speed railway, the existing line passing capacity (LPC) is unable to meet the increasing transportation demands. The train tracking interval (TTI) serves as an important parameter for evaluating LPC. The traditional train control system (TCS) considers the transmission latency as a fixed constant measured in the worst environment, which restricts the effectiveness of TTI optimization. In this article, an optimization strategy of TTI for train autonomous operation is proposed based on stochastic network calculus (SNC), aiming at enhancing LPC. Different with the existing TCS, SNC can calculate the transmission latency as a dynamic value, thereby more accurately reflecting the complexity and variability of the train operating environment and speed. This strategy not only guarantees the safety of train operations but also achieves smaller and more appropriate transmission latency. In addition, this article employs a moving block system for train autonomous operation and further decreases the TTI which is realized by train-to-train communication. Simulation studies were conducted to explore the relationship between transmission latency and the dynamic operational speed and environmental changes. The results demonstrate that the proposed method can enhance LPC by 2% to 9%. Meantime, the developed control algorithm can prove the effectiveness and availability of the TTI adjustment method.