Fatigue Driving Detection Methods based on Human-computer Interaction

Guanzheng An

doi:10.62051/4rdcbr36

Authors

Guanzheng An

DOI:

https://doi.org/10.62051/4rdcbr36

Keywords:

Fatigue Driving; Physiological Parameters; Visual Features; Multimodal Fusion.

Abstract

Drivers' fatigue driving detection is very important to traffic safety, and is closely related to the safety of human life and property. It is a key research topic for researchers. Effective fatigue identification technology can effectively reduce the traffic accidents caused by fatigue. This paper provides a systematic review of the detection methods for driver fatigue driving. The concept of driver fatigue and its necessity for detection are introduced, and the characteristics of fatigue driving behavior are described and classified. This paper summarizes several of the widely used public data sets for fatigue driving in detail, and analyzes the characteristics of each data set to compare its applicability and limitations, providing a valuable resource for subsequent research. Finally, the driver fatigue driving detection method based on facial features, physiological signal features, vehicle features and multiple features fusion is comprehensively analyzed. By comparing the advantages and disadvantages of various methods, this paper summarizes the problems and challenges faced by drivers in the field of fatigue driving detection, and prospects the future development direction.

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References

State Statistical Bureau. China Statistical Yearbook 2024. Beijing: China Statistics Press, 2024

Maclean A. W. Sleep and driving[M]. in, H.Dringenberg, Ed, London: Elsevier, 2019,

Min Jianliang, Cai Ming. Driving fatigue detection analysis based on the multiscale wavelet log-energy entropy of prefrontal EEG [J]. Chinese highway science Newspaper, 2020,33 (06): 182-189.

Pan Pingping. Research on the mental fatigue detection method based on the EEG signal [D]. Nanning: Guangxi University, 2014.

Wang Fei, Wu Shichao, Liu Shaolin, Zhang Yahui, Wei Ying. Driving fatigue detection based on deep transfer learning of EEG signals [J]. electron And Informatics, 2019,41 (09): 2264-2272.

Ma Y, Tian F, Zhao Q, et al. Design and application of mental fatigue detection system using non-contact ECG and BCG measurement[C]//2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018: 1508-1513.

Yue Y, Liu D, Fu S, et al. Heart rate and heart rate variability as classification features for mental fatigue using short-term PPG signals via smartphones instead of ECG recordings[C]//2021 13th International Conference on Communication Software and Networks (ICCSN). IEEE, 2021: 370-376.

Zeng J, Zhou Y, Yang Y, et al. Fatigue-Sensitivity Comparison of sEMG and A-Mode Ultrasound based Hand Gesture Recognition[J]. IEEE Journal of Biomedical and Health Informatics, 2021, 26(4): 1718-1725.

Li Zhao, Shu Zhibing. Design of a real-time early warning system for driver fatigue based on DSP [J]. Control Engineering, 2019,26 (01): 92-98.

But Xun Yang Zi. Research based on the embedded driver fatigue driving detection method [D]. Xi 'an: Chang' an University, 2020.

Wang Xiu, Zhou Fengxiao, Paul Liu, et al. Driver fatigue driving detection system based on the Dlib library [J]. Internet of Things Technology, 2021,11 (12): 26-29.

Yan Baozhong, Wang Chenyu, Wang Shuaishuai. Research on fatigue driving detection technology based on human eye characteristics [J]. Applied Technology, 2020,47 (01): 47-54.

Biswal A K, Singh D, Pattanayak B K, et al. IoT-based smart alert system for drowsy driver detection[J]. Wireless communications and mobile computing, 2021, 2021: 1-13.

Shi Jian, Wu Yuanpeng, Zhuo Bin, et al. Identification and analysis of the active safety factors of automobile drivers [J]. Journal of Shanghai Jiao Tong University, 2000,34 (4): 441-444

Xie Zhi. Study of driving fatigue model based on physiological signals [D]. Suzhou: Soochow University, 2017.

Li Lingling. Research and Realization of Driver Fatigue Detection Method Based on multi-visual information fusion [D]. Beijing: Beijing Industry The University, 2010.

Xu Weijing, Wang Di, Shan Chaoying. Research on fatigue driving detection technology based on video surveillance [C]. The 16th Shenyang Department Proceedings of annual Conference (Science, Industry, Agriculture and Medicine), 2019:405-408.

Ma Z P, Yao S W, Zhao J, et a1. Research on drowsy-driving monitoring and warning system based on multi-feature comprehensive evaluation[J]. IFAC Papers Online, 2018, 51(31): 784-789.

Dai Shiqi, Zeng Zhiyong. Deep learning-based fatigue driving detection algorithm [J]. Computer System Application, 2018,27 (7):113-120.