Research on Enhanced Situation Awareness Model with DMI Visualization Cues for High-Speed Train Driving

Abstract

With the rapid increase of high-speed train driving automation, the human-computer interaction of high-speed train driving is changing. It is a major dilemma to maintain a well-interactive process between the driver and the driving system for the intelligent train operation. Nevertheless, the enhancement of the driver’s situation awareness (SA) is an effective way to promote the process of driver-driving system interaction. To achieve this purpose, we proposed an enhanced SA model based on the perceptual cycle model (PCM). The model helps to identify the high-speed train DMI interface elements that affect the different SA level of drivers and provides theoretical support for describing the SA change pattern of driving tasks. We proposed a new display mode (ESA mode) for enhancing driver SA according to this model, and conducted driving simulator experiments to explore the differences of SA change patterns between the ESA mode and the normal mode (NSA mode) for 16 subjects. The experimental results showed that the ESA mode successfully overcame the subjects’ SA recession in the NSA mode. The interaction with enhanced SA can help the driver remain vigilant and further improve the general driving performance during monotonous process of automated high-speed train driving.

Keywords:

• High-speed train driving
• SA enhancement
• Human in the loop
• Interface design
• Adaptive automation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China under Grant No.62203039 and Beijing Natural Science Foundation L201024.

Notes on contributors

Aobo Wang

Aobo Wang received the M.S. degree in industrial engineering from Beijing Jiaotong University, Beijing in 2020. He is currently pursuing the Ph.D. degree in mechanical engineering at Beijing Jiaotong University, Beijing, China. His current research interests include human-computer interaction, human–machine function allocation in railway transportation.

Beiyuan Guo

Beiyuan Guo received a B.S. and an M.S. degree from Chongqing University, Chongqing, China and a Ph.D. degree from Beijing Jiaotong University, China. He is currently a professor at State Key Laboratory of Rail Traffic Control and Safety at Beijing Jiaotong University.

Ziwang Yi

Ziwang Yi received the M.S. degree in transportation information engineering and control from Beijing Jiaotong University, Beijing in 2022. His current research interests include autonomous driving of High-speed trains, human-machine interaction in railway transportation.

Weining Fang

Weining Fang is a professor at State Key Laboratory of Rail Traffic Control and Safety at Beijing Jiaotong University. He received a B.S. in industrial design and an M.S. and Ph.D. degree in mechanical engineering from Chongqing University, China. He focus on interaction design and modeling for rail transit.