Effects of Individual Differences on Measurements’ Drowsiness-Detection Performance

  • Yifan Sun Wuhan University of Technology, Intelligent Transportation Systems Research Centre http://orcid.org/0000-0002-1409-6520
  • Chaozhong Wu Wuhan University of Technology, Intelligent Transportation Systems Research Centre
  • Hui Zhang Wuhan University of Technology, Intelligent Transportation Systems Research Centre
  • Wenhui Chu Wuhan University of Technology, Intelligent Transportation Systems Research Centre
  • Yiying Xiao Wuhan University of Technology, Intelligent Transportation Systems Research Centre
  • Yijun Zhang Wuhan University of Technology, Intelligent Transportation Systems Research Centre
DOI: https://doi.org/10.7307/ptt.v33i4.3668
Keywords: traffic safety, drowsiness-detection models, non-intrusive measurements, naturalistic driving study, individual differences

Abstract

Individual differences (IDs) may reduce the detection-accuracy of drowsiness-driving by influencing measurements’ drowsiness-detection performance (MDDP). The purpose of this paper is to propose a model that can quantify the effects of IDs on MDDP and find measurements with less impact by IDs to build drowsiness-detection models. Through field experiments, drivers’ naturalistic driving data and subjective-drowsiness levels were collected, and drowsiness-related measurements were calculated using the double-layer sliding time window. In the model, MDDP was represented by |Z-statistics| of the Wilcoxon-test. First, the individual driver’s measurements were analysed by Wilcoxon-test. Next, drivers were combined in pairs, measurements of paired-driver combinations were analysed by Wilcoxon-test, and measurement’s IDs of paired-driver combinations were calculated. Finally, linear regression was used to fit the measurements’ IDs and changes of MDDP that equalled the individual driver’s |Z-statistics| minus the paired-driver combination’s |Z-statistics|, and the slope’s absolute value (|k|) indicated the effects of ID on the MDDP. As a result, |k| of the mean of the percentage of eyelid closure (MPECL) is the lowest (4.95), which illustrates MPECL is the least affected by IDs. The results contribute to the measurement selection of drowsiness-detection models considering IDs.

References

Doudou M, Bouabdallah A, Berge-Cherfaoui V. Driver Drowsiness Measurement Technologies: Current Research, Market Solutions, and Challenges. International Journal of Intelligent Transportation Systems Research. 2019: 1-23.

Anund A, Fors C, Ihlström J, Kecklund G. An on-road study of sleepiness in split shifts among city bus drivers. Accident Analysis & Prevention. 2018;114: 71-6.

Otmani S, Pebayle T, Roge J, Muzet A. Effect of driving duration and partial sleep deprivation on subsequent alertness and performance of car drivers. Physiology Behavior. 2005;84(5): 715-24.

Fu RR, Wang ST, Wang SW. Real-time Alarm Monitoring System for Detecting Driver Fatigue in Wireless Areas. Promet – Traffic&Transportation. 2017;(2): 165-74.

Li ZJ, et al. Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions. Sensors. 2017;17(3).

Liu CC, Hosking SG, Lenné MG. Predicting driver drowsiness using vehicle measures: Recent insights and future challenges. Journal of Safety Researchs. 2009;40(4): 239-45.

Wang XS, Chuan X. Driver drowsiness detection based on non-intrusive metrics considering individual specifics. Accident Analysis & Prevention. 2016;95: 350-7.

He QC, Li W, Fan XM, Fei ZM. Driver fatigue evaluation model with integration of multi-indicators based on dynamic Bayesian network IET Intelligent Transport Systems. 2015;(5): 547-54.

Al-Libawy H, Al-Ataby A, Al-Nuaimy W, Al-Taee MA. Modular design of fatigue detection in naturalistic driving environments. Accident Analysis & Prevention. 2018;120: 188-94.

Cheng Q, et al. Assessment of Driver Mental Fatigue Using Facial Landmarks. IEEE Access. 2019;7: 150423-34.

Wakita T, et al. Driver identification using driving behavior signals. IEICE Transactions on Information. 2006;89(3): 1188-94.

Thiffault P, Bergeron J. Fatigue and individual differences in monotonous simulated driving. Personality Individual Differences. 2003;34(1): 159-76.

Yan RH, Wu C, Wang YM. Exploration and evaluation of individual difference to driving fatigue for high-speed railway: A parametric SVM model based on multidimensional visual cue. IET Intelligent Transport Systems. 2018;12(6): 504-12.

Zhao XH, Zhang XJ, Rong J. Study of the Effects of Alcohol on Drivers and Driving Performance on Straight Road. Mathematical Problems in Engineering. 2014.

Ingre M, et al. Subjective sleepiness, simulated driving performance and blink duration: Examining individual differences. Journal of Sleep Research. 2006;15(1): 47-53.

Philip P, et al. Fatigue, sleep restriction and driving performance. Accident Analysis & Prevention. 2005;(3): 473-8.

Xu C, Wang XS, Chen XH. Evaluating Performance of Non-intrusive Indicators on Drowsy Driving Detection. Journal of Southwest Jiaotong University. 2014;49(4): 720-6.

Chu W, et al. Driver behavior model and its application in driver fatigue identification. China Safety Science Journal. 2018;28(06): 43-8.

You F, et al. A Real-time Driving Drowsiness Detection Algorithm With Individual Differences Consideration. IEEE Access. 2019;17: 179396-408.

Niu QN. Research on Driving Fatigue Detection Based on Hybrid Measures. PhD thesis. Jilin, Changchun: Jilin University; 2014.

Zhang H, et al. Qiu TZ. Sensitivity of lane position and steering angle measurements to driver fatigue. Transportation Research Record. 2016;2585(1): 67-76.

Sikander G, Anwar S. Driver fatigue detection systems: A review. IEEE Transactions on Intelligent Transportation Systems. 2018;20(6): 2339-52.

Jin LS, Li KY, Niu QN, Gao LL. A New Method for Detecting Driver Fatigue Using Steering Performance. Journal of Transport Information and Safety. 2014;32(5): 103-7.

Zhang XB, Cheng B, Feng RJ. Real-time detection of driver drowsiness based on steering performance. J Tsinghua Univ. 2010;7: 1072-6.

Kruskal WH. Historical notes on the Wilcoxon unpaired two-sample test. Journal of the American Statistical Association. 1957;52(279): 356-60.

Zhang D. A coefficient of determination for generalized linear models. The American Statistician. 2017;71(4): 310-6.

Published
2021-08-05
How to Cite
1.
Sun Y, Wu C, Zhang H, Chu W, Xiao Y, Zhang Y. Effects of Individual Differences on Measurements’ Drowsiness-Detection Performance. Promet [Internet]. 2021Aug.5 [cited 2024Dec.22];33(4):565-78. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/3668
Issue
Vol 33 No 4 (2021)
Section
Articles

Copyright (c) 2021 Yifan Sun, Chaozhong Wu, Hui Zhang, Wenhui Chu; Yiying Xiao, Yijun Zhang

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).