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首页> 外文期刊>Promet-traffic & transportation >EFFECTS OF INDIVIDUAL DIFFERENCES ON MEASUREMENTS' DROWSINESS-DETECTION PERFORMANCE
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EFFECTS OF INDIVIDUAL DIFFERENCES ON MEASUREMENTS' DROWSINESS-DETECTION PERFORMANCE

机译:个体差异对测量的影响'嗜睡检测性能

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摘要

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 vertical bar Z-statistics vertical bar 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 vertical bar Z-statistics vertical bar minus the paired-driver combination's vertical bar Z-statistics vertical bar, and the slope's absolute value (vertical bar k vertical bar) indicated the effects of ID on the MDDP. As a result, vertical bar k vertical bar 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.
机译:单个差异(IDS)可以通过影响测量值的嗜睡 - 检测性能(MDDP)来降低蠕动驾驶的检测准确性。本文的目的是提出一个模型,可以量化ID在MDDP上的效果,并通过IDS的影响较少的测量来构建令人困难的检测模型。通过现场实验,收集了司机的自然主义驾驶数据和主观嗜可能嗜可能的测量,并且使用双层滑动时间窗口计算了嗜睡相关的测量。在该模型中,MDDP由Wilcoxon-Test的垂直条Z统计垂直条表示。首先,通过Wilcoxon-Test分析各个驾驶员的测量。接下来,司机成对合并,通过Wilcoxon-Test分析配对驾驶员组合的测量,并计算配对驾驶员组合的测量的ID。最后,使用线性回归来符合测量的ID和MDDP的变化,该MDDP等于各个驾驶员的垂直条Z统计垂直条减去配对驱动器组合的垂直条Z统计垂直栏,斜率的绝对值(垂直栏K垂直条)表示ID对MDDP的影响。结果,眼睑闭合百分比(MPECL)百分比的垂直条K垂直条是最低(4.95),其说明MPECL是受IDS最小的影响。结果有助于考虑IDS的嗜睡检测模型的测量选择。

著录项

  • 来源
    《Promet-traffic & transportation》 |2021年第4期|565-578|共14页
  • 作者单位

    Wuhan Univ Technol Intelligent Transportat Syst Res Ctr 1178 Heping Dadao Wuhan Hubei Peoples R China;

    Wuhan Univ Technol Intelligent Transportat Syst Res Ctr 1178 Heping Dadao Wuhan Hubei Peoples R China;

    Wuhan Univ Technol Intelligent Transportat Syst Res Ctr 1178 Heping Dadao Wuhan Hubei Peoples R China;

    Wuhan Univ Technol Intelligent Transportat Syst Res Ctr 1178 Heping Dadao Wuhan Hubei Peoples R China|Wuhan Univ Sci & Technol Sch Automobile & Traff Engn Special 1 Wuhan Hubei Peoples R China;

    Wuhan Univ Technol Intelligent Transportat Syst Res Ctr 1178 Heping Dadao Wuhan Hubei Peoples R China;

    Wuhan Univ Technol Intelligent Transportat Syst Res Ctr 1178 Heping Dadao Wuhan Hubei Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    traffic safety; drowsiness-detection models; non-intrusive measurements; naturalistic driving study; individual differences;

    机译:交通安全;嗜睡检测模型;非侵入性测量;自然主义驾驶研究;个人差异;

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