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Experimental study on driver’s mental load in hairpin curves of mountainous highway

    Ying Chen Affiliation
    ; Xiaohui Wang Affiliation
    ; Xiaobo Zhang Affiliation
    ; Haiyuan Chen Affiliation
    ; Zhigang Du Affiliation
    ; Jin Xu Affiliation

Abstract

In order to reveal the driving psychological characteristics and influencing factors of drivers under the hairpin curve section, 11 continuous hairpin curves on mountain roads were selected for natural driving test, and the on-board instruments were used to collect the driver’s ElectroCardioGraphy (ECG) under the natural driving habits. Analyse the overall heart rate characteristics, Heart Rate Increase (HRI), Heart Rate Variability (HRV) characteristics of drivers, as well as the relationship between heart rate change and the visual performance of curve corner and slop and curve environment. And compared with the general curve. The results show that: with 180° as the limit, the curve angle of the hairpin curve was divided into 3 types: greater, less or approximate. The 3 types of curve angle have different effects on the driver’s heart rate fluctuations. The overall heart rate distribution can be divided into 2 regions, in which the average heart rate of each driver at the curve, which curve angle ≈ 180°, was higher than the other 2 types of curves. The overall fluctuation range of heart rate in the middle of the curve is at the lowest level in the 3-stage curve segment area. Through the eigenvalue analysis of HRI, it can be seen that the drivers were more susceptible to the external environment when going downhill. When going uphill, the distribution range of the heart rate abnormality value was stable, but the sudden change was obvious. However, during the downhill direction, the overall adjacent heart rate varies greatly, but the abrupt change was small. Take the change trend of the HRI in the curve segment as an indicator, heart rate types were divided into 4 categories, continuous tension, relax gradually, relaxation-tension, and tension-relaxation. The 4 modes have a significant relationship with the difference of curve entrance environment. Compared with the modes shown in general curves, they focus on the modes with greater volatility, while the general curves focus on a more single growth trend.

Keyword : traffic engineering, hairpin curve, mountain road, natural driving test, driving mental load, heart rate variability

How to Cite
Chen, Y., Wang, X., Zhang, X., Chen, H., Du, Z., & Xu, J. (2023). Experimental study on driver’s mental load in hairpin curves of mountainous highway. Transport, 38(3), 127–138. https://doi.org/10.3846/transport.2023.19795
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Oct 18, 2023
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References

Eboli, L.; Guido, G.; Mazzulla, G.; Pungillo, G. 2017. Experimental relationships between operating speeds of successive road design elements in two-lane rural highways, Transport 32(2): 138–145. https://doi.org/10.3846/16484142.2015.1110831

Feng, D.; Chen, F.; Pan, X. 2017. Research on driver physiological load at the lowest point of city river-crossing tunnels, Transportation Research Procedia 25: 1494–1502. https://doi.org/10.1016/j.trpro.2017.05.178

Feng, Z.; Yang, M.; Zhang, W.; Du, Y.; Bai, H. 2018. Effect of longitudinal slope of urban underpass tunnels on drivers’ heart rate and speed: a study based on a real vehicle experiment, Tunnelling and Underground Space Technology 81: 525–533. https://doi.org/10.1016/j.tust.2018.08.032

Gromer, M.; Salb, D.; Walzer, T.; Martínez Madrid, N.; Seepold, R. 2019. ECG sensor for detection of driver’s drowsiness, Procedia Computer Science 159: 1938–1946. https://doi.org/10.1016/j.procs.2019.09.366

Hu, J. 2019. Research on the Psychological Load of Mountain City Interchange Based on Natural Driving. Chong Qing Jiaotong University, Chong Qing, China. (in Chinese).

Li, H.; Zhu, S.; Qi, C.; Yang, F.; Gao, M. 2015. Driver’s psychological and physiological reaction analysis on snow-covered road in North Forest region, Journal of Northeast Forestry University (5): 118–122. https://doi.org/10.13759/j.cnki.dlxb.20150522.006 (in Chinese).

Miller, E. E.; Boyle, L. N. 2015. Driver behavior in road tunnels: association with driver stress and performance, Transportation Research Record: Journal of the Transportation Research Board 2518: 60–67. https://doi.org/10.3141/2518-08

Patel, M.; Lal, S. K. L.; Kavanagh, D.; Rossiter, P. 2011. Applying neural network analysis on heart rate variability data to assess driver fatigue, Expert Systems with Applications 38(6): 7235–7242. https://doi.org/10.1016/j.eswa.2010.12.028

Peng, Z.; Rong, J.; Wu, Y.; Zhou, C.; Yuan, Y.; Shao, X. 2021. Exploring the different patterns for generation process of driving fatigue based on individual driving behavior parameters, Transportation Research Record: Journal of the Transportation Research Board 2675(8): 408–421. https://doi.org/10.1177/0361198121998351

Qiao, J.; Li, S.; Liu, W. L.; Liu, W. Y. 2020. Relationship between driver’s heart rate change and curved slope ratio of rebuilt and expanded roads and driving speed difference, Journal of Fuzhou University (Natural Science Edition) 48(1): 105–109. (in Chinese).

Qin, P.; Wang, M.; Chen, Z.; Yan, G.; Yan, T.; Han, C.; Bao, Y.; Wang, X. 2021. Characteristics of driver fatigue and fatigue-relieving effect of special light belt in extra-long highway tunnel: a real-road driving study, Tunnelling and Underground Space Technology 114: 103990. https://doi.org/10.1016/J.TUST.2021.103990

Russo, F.; Biancardo, S. A.; Busiello, M. 2016. Operating speed as a key factor in studying the driver behaviour in a rural context, Transport 31(2): 260–270. https://doi.org/10.3846/16484142.2016.1193054

Sang, Y.; Li, J. 2012. Research on Beijing bus driver psychology fatigue evaluation, Procedia Engineering 43: 443–448. https://doi.org/10.1016/j.proeng.2012.08.076

Wadhwa, A.; Roy, S. S. 2020. Driver drowsiness detection using heart rate and behavior methods: a study, in K. C. Lee, S. S. Roy, P. Samui, V. Kumar (Eds.). Data Analytics in Biomedical Engineering and Healthcare, 163–177. https://doi.org/10.1016/B978-0-12-819314-3.00011-2

Wang, J. Z.; Alli, S. 2020. Safety risk assessment of plateau highway corner value based on driver’s physiological load, Science Technology and Engineering (7): 2939–2943. (in Chinese).

Wolkow, A. P.; Rajaratnam, S. M. W.; Wilkinson, V.; Shee, D.; Baker, A.; Lillington, T.; Roest, P.; Marx, B.; Chew, C.; Tucker, A.; Haque, S.; Schaefer, A.; Howard, M. E. 2020. The impact of heart rate-based drowsiness monitoring on adverse driving events in heavy vehicle drivers under naturalistic conditions, Sleep Health 6(3): 366–373. https://doi.org/10.1016/j.sleh.2020.03.005

Wu, Y.; Zhao, X.; Rong, J.; Ma, J. 2013. Effects of chevron alignment signs on driver eye movements, driving performance, and stress, Transportation Research Record: Journal of the Transportation Research Board 2365: 10–16. https://doi.org/10.3141/2365-02

Xiao, D. Q.; Shen, Z. W.; Xu, X.C. 2017. Investigating the impact of greenery on the driver’s psychology at a freeway tunnel portal, in Civil, Architecture and Environmental Engineering: Proceedings of the International Conference ICCAE, 4–6 November 2016, Taipei, Taiwan, 1: 159–166. https://doi.org/10.1201/9781315226187-27

Xu, J.; Liu, X.-M.; Hu, J. 2020. Analysis of driver mental load on helical ramps and helical bridges based on naturalistic driving data, Journal of Transportation Systems Engineering and Information Technology (3): 212–218. https://doi.org/10.16097/j.cnki.1009-6744.2020.03.032 (in Chinese).

Zeng, C.; Wang, W.; Chen, C.; Zhang, C.; Cheng, B. 2020. Sex differences in time-domain and frequency-domain heart rate variability measures of fatigued drivers, International Journal of Environmental Research and Public Health 17(22): 8499. https://doi.org/10.3390/ijerph17228499

Zhang, J.; Liu, H.; Chen, J.; Tian, Z.; Wang, Z. 2015. On the relationship between the drivers’ heart-beating rate and the curve radius of long and steep slop, Journal of Safety and Environment (4): 140–143. (in Chinese).

Zhao, L.; Zhang, M. L. 2012. Influence of radius of horizontal curve on driver’s psychology and physiology on the two-lane mountain highway, Applied Mechanics and Materials 226–228: 2335–2339. https://doi.org/10.4028/www.scientific.net/AMM.226-228.2335

Zhao, T.; Qi, C.; Zhu, S.; Gao, M.; Wang, Y. 2016. Study on influence of complexity of highway alignment on driver’s HRV, China Safety Science Journal (2): 6–12. https://doi.org/10.16265/j.cnki.issn1003-3033.2016.02.002 (in Chinese).

Zhu, R. 2020. A review of research on driving fatigue detection based on physiological signals, Chinese Journal of Ergonomics 26(4): 82–86. (in Chinese).