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.

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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 2024Nov.21];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

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