期刊文献+

疲劳驾驶转向特征指标的个体差异敏感度分析 被引量:5

Sensitivities of Fatigue Driving Steering Features to Individual Difference
下载PDF
导出
摘要 个体差异是影响疲劳驾驶识别的重要因素.为研究基于转向行为的疲劳驾驶识别受个体差异的影响,本文对疲劳驾驶转向特征指标的个体差异敏感度进行分析.通过实车试验获得自然驾驶数据,对正常和疲劳状态下的指标进行Kruskal-Wallis(KW)检验,以H统计量表示指标有效性;以H统计量最大的单被试为基础逐一与其他被试组成双被试组合,采用线性模型拟合双被试组合的H统计量和指标个体差异度,以斜率绝对值表征指标个体差异敏感度.研究获得9个转向特征指标的个体差异敏感度,结果表明,敏感度越低,指标有效性受个体差异影响越小,其中方向盘转角标准差的个体差异敏感度最低为2.056.本研究可为转向特征指标的性能评估及疲劳驾驶识别模型的特征选择提供参考. Individual difference is important factor affecting identification of fatigue driving. For analyzing the effects of individual differences on fatigue driving identification based on steering driving behaviors, sensitivities of fatigue driving steering features to individual differences were analyzed. Naturalistic driving data was obtained by field driving experiments, features under normal and fatigue status were analyzed by Kruskal-Wallis (KW) test, H -statistics was used to indicate the effectiveness of features. Single participant with maximal H -statistics was used as basis to form double participants group with other participants one by one, H - statistics and individual difference degree for features of double participants group were fitted by linear model, and absolute value for slope was used to indicate sensitivities of features to individual differences. Sensitivities of nine steering features to individual differences is obtained, the lower sensitivity is, the less effectiveness of feature is affected by individual differences, and sensitivity for standard deviation of steering wheel angle to individual differences is the lowest whose value is 2.056. This study can offer references for evaluation of fatigue driving steering features' performances and feature selection for recognition model of fatigue driving.
作者 吴超仲 孙一帆 张晖 肖逸影 李徐义 WU Chao-zhong;SUN Yi-fan;ZHANG Hui;XIAO Yi-ying;LI Xv-yi(Intelligent Transportation Systems Research Center, Ministry of Education,Wuhan University of Technology,Wuhan 430063, China;Engineering Research Center for Transportation Safety, Ministry of Education,Wuhan University of Technology,Wuhan 430063, China)
出处 《交通运输系统工程与信息》 EI CSCD 北大核心 2019年第5期120-127,共8页 Journal of Transportation Systems Engineering and Information Technology
基金 国家重点研发计划项目(2017YFC0804802) 国家自然科学基金(61603282) 国家自然科学基金联合基金(u1624262)~~
关键词 智能交通 疲劳驾驶转向特征指标 敏感度分析 自然驾驶 个体差异 intelligent transportation steering features of fatigue driving sensitivity analysis naturalisticdriving individual difference
  • 相关文献

参考文献3

二级参考文献33

  • 1张承芬.眼底病学[M].北京:人民卫生出版社,2008:518.
  • 2KLAUER S G, DINGUS T A, NEALE V L, et al. The impact of driver inattention on near-crash/crash risk: an analysis using the 100-car naturalistic driving study data. Washington D. C.: National Highway Traffic Safety Administration, 2006.
  • 3公安部交通管理局. 中华人民共和国道路交通事故统计资料汇编2001—2008[R]. 北京:公安部交通管理局,2009.
  • 4LI W, HE Q C, FAN X M, et al. Evaluation of driver fatigue on two channels of EEG data[J]. Neuroscience Letters, 2012, 506(2): 235-239.
  • 5PATEL M, LAL S K L, KAVANAGH D, et al. Applying neural network analysis on heart rate variability data to assess driver fatigue[J]. Expert Systems with Applications, 2011, 38(6): 7235-7242.
  • 6LIU C C, HOSKING S G, LENN M G. Predicting driver drowsiness using vehicle measures: recent insights and future challenges[J]. Journal of Safety Research, 2009, 40(4): 239-245.
  • 7FORSMAN P M, VILA B J, SHORT R A, et al. Efficient driver drowsiness detection at moderate levels of drowsiness[J]. Accident Analysis and Prevention, 2013, 50: 341-350.
  • 8INGRE M, KERSTEDT T, PETERS B, et al. Subjective sleepiness, simulated driving performance and blink duration: examining individual differences[J]. Journal of Sleep Research, 2006, 15(1): 47-53.
  • 9JO J, LEE S J, PARK K R, et al. Detecting driver drowsiness using feature-level fusion and user-specific classification[J]. Expert Systems with Applications, 2014, 41(4): 1139-1152.
  • 10ZHU Z, JI Q. Real time and non-intrusive driver fatigue monitoring[C]//The 7th International IEEE Conference on Intelligent Transportation Systems Proceedings. Washington D. C.: IEEE, 2004: 657-662.

共引文献25

同被引文献43

引证文献5

二级引证文献14

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部