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Electronics
Volume 14
Issue 17
10.3390/electronics14173469
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Research on Driving Fatigue Assessment Based on Physiological and Behavioral Data

by
Ge Zhang
*,
Zhangyu Song
,
XiuLi Li
,
Wenqing Li
and
Kuai Liang
School of Future Cities, University of Science and Technology Beijing, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Electronics 2025, 14(17), 3469; https://doi.org/10.3390/electronics14173469
Submission received: 15 July 2025 / Revised: 27 August 2025 / Accepted: 28 August 2025 / Published: 29 August 2025
(This article belongs to the Special Issue Techniques and Applications of Multimodal Data Fusion)
Versions Notes

Abstract

Driving fatigue is a crucial factor affecting road traffic safety. Accurately assessing the driver’s fatigue status is critical for accident prevention. This paper explores the assessment methods of driving fatigue under different conditions based on multimodal physiological and behavioral data. Physiological data such as heart rate, brainwave, electromyography, and pupil diameter were collected through experiments, as well as behavioral data such as posture changes, vehicle acceleration, and throttle usage. The results show that physiological and behavioral indicators have significant sensitivity to driving fatigue, and the fusion of multimodal data can effectively improve the accuracy of fatigue detection. Based on this, a comprehensive driving fatigue assessment model was constructed, and its applicability and reliability in different driving scenarios were verified. This study provides a theoretical basis for the development and application of driver fatigue monitoring systems, helping to achieve real-time fatigue warnings and protections, thereby improving driving safety.
Keywords: driving fatigue; physiological data; behavioral data; multimodal assessment; fatigue monitoring driving fatigue; physiological data; behavioral data; multimodal assessment; fatigue monitoring

Share and Cite

MDPI and ACS Style

Zhang, G.; Song, Z.; Li, X.; Li, W.; Liang, K. Research on Driving Fatigue Assessment Based on Physiological and Behavioral Data. Electronics 2025, 14, 3469. https://doi.org/10.3390/electronics14173469

AMA Style

Zhang G, Song Z, Li X, Li W, Liang K. Research on Driving Fatigue Assessment Based on Physiological and Behavioral Data. Electronics. 2025; 14(17):3469. https://doi.org/10.3390/electronics14173469

Chicago/Turabian Style

Zhang, Ge, Zhangyu Song, XiuLi Li, Wenqing Li, and Kuai Liang. 2025. "Research on Driving Fatigue Assessment Based on Physiological and Behavioral Data" Electronics 14, no. 17: 3469. https://doi.org/10.3390/electronics14173469

APA Style

Zhang, G., Song, Z., Li, X., Li, W., & Liang, K. (2025). Research on Driving Fatigue Assessment Based on Physiological and Behavioral Data. Electronics, 14(17), 3469. https://doi.org/10.3390/electronics14173469

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