Dynamic speed trajectory generation and tracking control for autonomous driving of intelligent high-speed trains combining with deep learning and backstepping control methods. (October 2022) - Wang, Xi; Li, Shukai; Cao, Yuan; Xin, Tianpeng; Yang, Lixing
Abstract: The development of autonomous transportation systems has received increasing attention over the last decades. Different from existing automatic train control systems, the decision-making capability in the autonomous driving system enables a train to adapt to the complicated and dynamic circumstances. This paper in particular focuses on the decision-making problem for the autonomous driving of intelligent high-speed trains, and proposes a novel decision-making framework by combining the deep learning and backstepping control methods. By exploiting the deep learning methods, a speed trajectory generator is trained with the actual driving data, and dynamically calculates the reference speed trajectory according to the real-time driving condition. Then, a backstepping controller is designed to track the reference speed trajectory such that the separation, cohesion and alignment requirements for the autonomous driving of high-speed trains are achieved. Simulation experiments are implemented to illustrate the effectiveness of our methods.