This paper systematically investigates the development pathways for intelligent transformation of rail vehicles driven by artificial intelligence technologies. The study focuses on intelligent upgrading throughout the entire lifecycle of rail vehicles，comprehensively reviewing domestic and international research progress and engineering applications of key technologies across six dimensions：intelligent design，smart manufacturing，intelligent perception，autonomous driving，smart services，and intelligent maintenance. These technologies include generative models，digital twins，multi-source fusion perception，and deep learning-based control systems. The paper provides an in-depth analysis of core scientific challenges，such as data acquisition and governance，model reliability and generalization，edge-side usability，and systematic engineering. Furthermore，it identifies critical future research directions，including AI technologies for full lifecycle applications，autonomous and controllable computing power，and integrated manufacturing-maintenance algorithms. By examining the requirements for intelligent technology standardization and industrial ecosystem development，this study offers systematic solutions and strategic recommendations to advance the deep integration of “mechanical-electrical-information-intelligent” systems in China’s rail vehicle industry.
Peng et al. (Sun,) studied this question.