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20 December 2024 Detection of high-speed railway driver fatigue across shifts scheduling
Yufan Chen, Lanxi Wei, Tong Wang, Yanmin Shen, Zhiqiang Sun, Chaozhe Jiang, Shupeng Han, Muhammad Junaid
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Proceedings Volume 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024); 134211X (2024) https://doi.org/10.1117/12.3054750
Event: Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 2024, Dalian, China
Abstract
Irregular shift scheduling is closely related to high-speed rail (HSR) driver fatigue, which affects driving performance and HSR operation safety. This paper attempts to identify the difference of driver fatigue between different shifts through the research conducted on an HSR simulator. A total of 28 time-domain, frequency-domain and nonlinear heart rate variability (HRV) features are extracted. Mann-Whitney U test is used to analyze whether HRV characteristics differ in different shifts. Five classifiers (CNN, LSTM, RNN, RF and SVM) are used for fatigue ternary classification. The results show that most of the HRV features are statistically different across different shifts, and the drivers may be more tired during morning shifts driving. RF and CNN perform best in the ternary classification of driver fatigue. The results of this study are helpful to understand the time-varying difference of driver fatigue and provide reference for optimizing driver scheduling.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Yufan Chen, Lanxi Wei, Tong Wang, Yanmin Shen, Zhiqiang Sun, Chaozhe Jiang, Shupeng Han, and Muhammad Junaid "Detection of high-speed railway driver fatigue across shifts scheduling", Proc. SPIE 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 134211X (20 December 2024); https://doi.org/10.1117/12.3054750
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KEYWORDS
Principal component analysis
Feature extraction
Statistical analysis
Transportation
Machine learning
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