Analysis of Research for Fatigue Driving Detection in Complex Scenarios

Sirui Min

doi:10.47297/taposatWSP2633-456916.20250607

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Analysis of Research for Fatigue Driving Detection in Complex Scenarios

Volume 6, Issue 7

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Analysis of Research for Fatigue Driving Detection in Complex Scenarios
Analysis of Research for Fatigue Driving Detection in Complex Scenarios
Chang'an Dublin International Transportation College, Chang'an University, Xi'an, Shaanxi, 710000, China
Vol. 6, Issue 7, Pages: 79-81(2025)
Published：31 July 2025
DOI：10.47297/taposatWSP2633-456916.20250607
Accepted：

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Min Sirui.Analysis of Research for Fatigue Driving Detection in Complex Scenarios[J].Theory and Practice of Science and Technology,2025,06(07):79-81.
DOI：

Min Sirui.Analysis of Research for Fatigue Driving Detection in Complex Scenarios[J].Theory and Practice of Science and Technology,2025,06(07):79-81. DOI： 10.47297/taposatWSP2633-456916.20250607.

摘要

Abstract

Fatigued driving is a major threat to road traffic safety

and its identification and warning become more challenging in complex environments such as at night

in adverse weather conditions

in mountainous areas

and in urban congestion. This paper examines how complex environmental factors influence fatigue detection and the current research in detection using multimodal perception technology. This includes methods based on physiological signals

as well as techniques for extracting facial landmarks and blink parameters under low-light conditions or facial occlusion. This paper further explores multimodal fusion methods and their application in improving the detection accuracy of environmental factors. Finally

addressing the shortcomings of current research in algorithm robustness

scene generalization capability

and real-road validation

the paper summarizes the challenges faced and outlines future directions aiming to provide references for in-depth research and intelligent prevention and control practices regarding fatigue driving in complex environments.

关键词

Keywords

references

Cheng, W.X., Yan, G.H., Chang, W.W. et al. Based on channel weighted multimodal feature fusion for EEG fatigue driving detection. Journal of Zhejiang University (Engineering Science), P. 1 – 10 . 2025.

Sun, J., Tang, X., Xu, Y.N. et al. Detection method of highway fatigue driving behavior by fusing machine vision and high-precision positioning. Transportation Research, 2023, 9(6), 78–87+118.

Sun, Z.C., Miao, Y.N., Jeon, J.Y. et al. Facial feature fusion convolutional neural network for driver fatigue detection. Engineering Applications of Artificial Intelligence, P. 126(PC). 2023.

Gao, Q.H., Xie, K., He, Z.F. et al. Fatigue driving detection based on multimodal feature fusion in complex environment. Electronic Measurement Technology, 2023, 46(6), 106–115.

Li, G. & Chung, W.Y. Electroencephalogram-Based Approaches for Driver Drowsiness Detection and Management: A Review. Sensors, 2022, 22(3), 1100–1100.

Hu, X.Z. & Huang, B.Y. Fatigue driving detection method based on facial feature analysis. Science Technology and Engineering, 2021, 21(4), 1629–1636.

Zhan, H.M. Anti fatigue driving system based on facial feature recognition technology in complex driving environment. Electronic Test, 2016, (17), 84–85.

Chen, S.L., Lei, L. & Zhao, Y.X.Research on fatigue driving detection technology based on behavioral characteristics. Journal of Chengdu University (Natural Science Edition), 2014, 33(1), 37–40.

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