A Human-Machine Trust Evaluation Method for High-Speed Train Drivers Based on Multi-Modal Physiological Information

Abstract

With the development of intelligent transportation, it has become mainstream for drivers and automated systems to cooperate to complete train driving tasks. Human-machine trust has become one of the biggest challenges in achieving safe and effective human-machine cooperative driving. Accurate evaluation of human-machine trust is of great significance to calibrate human-machine trust, realize trust management, reduce safety accidents caused by trust bias, and achieve performance and safety goals. Based on typical driving scenarios of high-speed trains, this paper designs a train fault judgment experiment. By adjusting the machine’s reliability, the driver’s trust is cultivated to form their cognition of the machine. When the driver’s cognition is stable, data from the Trust in Automated (TIA) scale and modes of physiological information, including electrodermal activity (EDA), electrocardiograms (ECG), respiration (RSP), and functional near-infrared spectroscopy (fNIRS), are collected during the fault judgment experiment. Based on analysis of this multi-modal physiological information, a human-machine trust classification model for high-speed train drivers is proposed. The results show that when all four modes of physiological information are used as input, the random forest classification model is most accurate, reaching 93.14%. This indicates that the human-machine trust level of the driver can be accurately represented by physiological information, thus inputting the driver’s physiological information into the classification model outputs their level of human-machine trust. The human-machine trust classification model of high-speed train drivers built in this paper based on multi-modal physiological information establishes the corresponding relationship between physiological trust and human-machine trust level. Human-machine trust level is characterized by physiological trust monitoring, which provides support for the dynamic management of trust.

Keywords:

• High-speed trains
• human-machine trust
• multi-modal physiological information
• machine learning

Disclosure statement

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

Data availability statement

The data used to support the findings of this study are available from the corresponding author upon request.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

The authors acknowledge with gratitude National Natural Science Foundation of China [No. 72271091; No. 71974056], the Key Science and Technology Program of Henan Province [202102210159, 212102210396, 222102310369], Foundation of Henan Education Committee [21A580006, 22A580006], Scientific Research Foundation of the Higher Education Institutions of Henan Province [23A630040], Research Fund of Zhengzhou Railway Vocational & Technical College [2022KY009], and Beijing Everloyal Technology Co., Ltd. horizontal subject. This study would not have been possible without their financial support.

Notes on contributors

Huimin Li

Huimin Li, a professor in the Department of Construction Engineering and Management at North China University of Water Resources and Electric Power, mainly engaged in green urban infrastructure, human factors engineering.

Mengxuan Liang

Mengxuan Liang, graduate student, Department of Construction Engineering and Management, North China University of Water Resources and Electric Power, mainly engaged in water environment management, human factors engineering, etc.

Ke Niu

Ke Niu, associate professor at Zhengzhou Railway Vocational and Technical College. Primarily engaged in human factors engineering, the complexity of critical positions in automated railway systems, train driver safety.

Yaqiong Zhang

Yaqiong Zhang, lecturer of Zhengzhou Railway Vocational and Technical College. She is mainly engaged in risk management, human factors engineering, ppp projects and other research, has published relevant articles in journals such as discrete dynamics in nature and society.