|Table of Contents|

[1] Chen Fei, Xu Shuang, Li Cunxiao, Zhu Wanxiao, et al. Comprehensive evaluation method for plateau driving fatigue based on psychophysiological indicators [J]. Journal of Southeast University (English Edition), 2024, 40 (4): 355-362. [doi:10.3969/j.issn.1003-7985.2024.04.004]
Copy

Comprehensive evaluation method for plateau driving fatigue based on psychophysiological indicators()
Share:

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
40
Issue:
2024 4
Page:
355-362
Research Field:
Traffic and Transportation Engineering
Publishing date:
2024-12-03

Info

Title:
Comprehensive evaluation method for plateau driving fatigue based on psychophysiological indicators
Author(s):
Chen Fei1 Xu Shuang1 Li Cunxiao1 Zhu Wanxiao1 Bo Wu2
1School of Transportation, Southeast University, Nanjing 211189, China
2School of Engineering, Tibet University, Lhasa 850000, China
Keywords:
plateau area driving fatigue driving simulation psychophysiological indicators
PACS:
U492.8
DOI:
10.3969/j.issn.1003-7985.2024.04.004
Abstract:
To investigate the effects of plateau environments on driving fatigue, heart rate and electroencephalogram(EEG)signals were chosen as indicators to characterize driving fatigue. The study analyzed the variation in these indicators as drivers transitioned into fatigued stages. By examining the sample entropy of EEG signals and the heart rate variation coefficient, a complex indicator of driving fatigue(CIDF)was established using principal component analysis to overcome the limitations of single-indicator methods. According to the CIDF values, the driving fatigue states in plateau areas were subdivided into three categories, including alertness, mild fatigue, and severe fatigue, by cluster analysis. Optimal binning determined thresholds for different driving fatigue states, which were validated through variance analysis. The results indicate that the CIDF values effectively distinguish the driving fatigue states of drivers in plateau areas. The CIDF thresholds for the alertness and the mild fatigue states are 0.34 and 0.50, respectively. A CIDF value greater than 0.50 indicates that the driver is in a severe fatigue state.

References:

[1] Horne J A, Reyner L A. Sleep related vehicle accidents[J]. BMJ, 1995, 310(6979): 565-567. DOI: 10.1136/bmj.310.6979.565.
[2] Zeng C, Wang W J, Li Y, et al. Nonlinear heart rate variability features of drivers in fatigue state considering gender factor[J]. Journal of Southeast University(Natural Science Edition), 2019, 49(3):595-602. DOI:10.3969/j.issn.1001-0505.2019.03.027. (in Chinese)
[3] Fang Z W, Lin Z S, Wang J X, et al. Shared differential steering control considering fatigue characteristics of driver[J]. Journal of Southeast University(Natural Science Edition), 2022, 52(5):1012-1022. DOI:10.3969/j.issn.1001-0505.2022.05.022. (in Chinese)
[4] Ji S Q, Eli I. Analysis of the influence on driver’s characteristics in plateau area of road traffic safety facilities[J]. Technology & Economy in Areas of Communications, 2014, 16(5): 5-9.(in Chinese)
[5] Wang Q, Yu X H, Hu X L, et al. Analysis on 1894 cases of road traffic injuries in the Qinghai-Tibet Plateau[J]. Chinese Journal of Trauma, 2004, 20(3):136-138.(in Chinese)
[6] Lal S K L, Craig A. A critical review of the psychophysiology of driver fatigue[J]. Biological Psychology, 2001, 55(3): 173-194. DOI: 10.1016/S0301-0511(00)00085-5.
[7] Ting P H, Hwang J R, Doong J L, et al. Driver fatigue and highway driving: A simulator study[J]. Physiology & Behavior, 2008, 94(3): 448-453. DOI: 10.1016/j.physbeh.2008.02.015.
[8] Oron-Gilad T, Ronen A. Road characteristics and driver fatigue: A simulator study[J]. Traffic Injury Prevention, 2007, 8(3): 281-289. DOI: 10.1080/15389580701354318.
[9] Wang H T, Wu C, Li T, et al. Driving fatigue classification based on fusion entropy analysis combining EOG and EEG[J]. IEEE Access, 2019, 7: 61975-61986. DOI: 10.1109/ACCESS.2019.2915533.
[10] Zhang C, Wang H, Fu R R. Automated detection of driver fatigue based on entropy and complexity measures[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(1): 168-177. DOI: 10.1109/TITS.2013.2275192.
[11] Lenné M G, Triggs T J, Redman J R. Time of day variations in driving performance[J]. Accident Analysis & Prevention, 1997, 29(4): 431-437. DOI: 10.1016/S0001-4575(97)00022-5.
[12] Ma Y L, Chen B, Li R H, et al. Driving fatigue detection from EEG using a modified PCANet method[J]. Computational Intelligence and Neuroscience, 2019, 2019: 4721863. DOI: 10.1155/2019/4721863.
[13] Chen W Y, Sawaragi T, Horiguchi Y. Measurement of driver’s mental workload in partial autonomous driving[C]//14th IFAC Symposium on Analysis, Design, and Evaluation of Human-Machine Systems. Tallinn, Estonia, 2019: 347-352.
[14] Lal S K L, Craig A, Boord P, et al. Development of an algorithm for an EEG-based driver fatigue countermeasure[J]. Journal of Safety Research, 2003, 34(3): 321-328. DOI: 10.1016/s0022-4375(03)00027-6.
[15] Schier M A. Changes in EEG alpha power during simulated driving: A demonstration[J]. International Journal of Psychophysiology, 2000, 37(2): 155-162. DOI: 10.1016/S0167-8760(00)00079-9.
[16] Hu F. Research on key alignment indicators of two-lane highway in plateau based on drivers’ physiological characteristics[D]. Nanjing: Southeast University, 2020.(in Chinese)
[17] Li T B. Study of driving fatigue in plateau highway based on drivers’ psychological and physiological reaction[D]. Urumqi: Xinjiang Agricultural University, 2015.(in Chinese)

Memo

Memo:
Biography: Chen Fei(1967—), male, doctor, professor, cf@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No. 51768063, 51868068).
Citation: Chen Fei, Xu Shuang, Li Cunxiao, et al. Comprehensive evaluation method for plateau driving fatigue based on psychophysiological indicators[J].Journal of Southeast University(English Edition), 2024, 40(4):355-362.DOI:10.3969/j.issn.1003-7985.2024.04.004.
Last Update: 2024-12-20