Year: 2025
Type: article
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.
Source: Electronics
Authors: Ge Zhang, Zhangyu Song, Xiu-Li Li, Wenqing Li, Kuai Liang
Institution: University of Science and Technology Beijing
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4413816993