A Generative Adversarial Network Based Learning Approach to the Autonomous Decision Making of High-Speed Trains

Nowadays, the autonomous driving transportation systems are at the heart of both academic and industry research for the distinguished advantages including increased network capacity, enhanced punctuality, greater flexibility and improved overall safety level. With the responsibility of transporting passengers in a safe, comfortable and efficient way, the decision making method plays a critical position in the autonomous driving of high-speed trains. Focusing on solving the autonomous decision making problem, this paper proposes a novel learning based framework by combining the deep learning technology with the distributed tracking control approach. To cope with the data insufficiency problem in training the deep learning network, a generative adversarial network (GAN) based data argumentation scheme is proposed to generate data samples that have the same distribution with actual data samples, and a hybrid learning network is constructed to predict the speed trajectory from the multi-attribute data with both temporal sequences and static features. Then, based on the model predictive control (MPC) scheme, a distributed tracking control model is formulated to minimize the tracking deviations and balance the performance of punctuality, energy-efficiency and riding comfort. Further, the dual decomposition technique is adopted to deal with the coupling constraints for the safe distance headway such that the separation for the autonomous driving of high-speed trains is achieved. Finally, simulation experiments based on actual scenarios of the Beijing-Shanghai high-speed railway are conducted to illustrate the effectiveness of our methods.

I. Introduction

Over the last decades, there has been great attentions dedicated to the research of autonomous vehicles. The rapid development of artificial intelligence (AI) and communication technologies has made it possible for autonomous vehicles to enter into a wide range of transportation applications [1]–[3]. The decision making system, which is responsible for enhancing operational safety, improving traffic capacity and alleviating driver fatigue, is one of the most crucial technologies for autonomous vehicles. For the high-speed railways, the high level of safety, punctuality, riding comfort and complex environment puts a strict requirement on the development of the decision making system for the autonomous driving of high-speed trains.