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汽车安全与节能学报

JASE ›› 2016, Vol. 07 ›› Issue (03): 279-284.DOI: 10.3969/j.issn.1674-8484.2016.03.005

• 汽车安全 • 上一篇    下一篇

基于方向盘转角近似熵与复杂度的驾驶人疲劳状态识别

李作进1，李仁杰2，李升波2，王文军2，成波2   

  1. 1. 重庆科技学院 电气与信息工程学院，重庆401331，中国；
    2. 清华大学 汽车安全与节能国家重点实验室，北京100084，中国
  • 收稿日期:2015-12-09 出版日期:2016-09-25 发布日期:2016-09-30
  • 作者简介:第一作者 / First author ： 李作进(1977—)，男( 汉)，重庆，副教授。E-mail: cqustlzj@sina.cn 第二作者 / Second author ： 李仁杰，博士研究生。E-mail: lirenaxe@gmail.com
  • 基金资助:

    汽车安全与节能国家重点实验室开放基金 (KF14212)

Driver fatigue recognition based on approximated entropy and complexity of steering wheel angle

LI Zuojin1, LI Renjie2, LI Shengbo2, WANG Wenjun2, CHENG Bo2   

  1. 1. School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing 401331,China; 2. State Key Lab of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China  
  • Received:2015-12-09 Online:2016-09-25 Published:2016-09-30

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摘要：

提出了一种基于实车工况下方向盘转角近似熵与复杂度的驾驶人疲劳状态识别方法。利用动态时间序列的非线性特征构造理论，计算短时方向盘转角时间序列的近似熵(ApEn) 与复杂度特征。以此非线性特征为输入，设计了硬阈值多级疲劳判别模型，构建了“二入二出”的疲劳等级映射规则，输出驾驶过程样本的疲劳预判状态。将近邻样本的疲劳预判状态进行比较，实现了驾驶人的三级疲劳状态识别。进行了实车实验。结果表明：本方法对三级疲劳状态识别的平均正确率达到84.6%；因而，本方法具有工程应用价值。

关键词: 汽车安全, 疲劳识别, 方向盘转角(SWA) , 近似熵(ApEn) , 复杂度

Abstract:

A novel approach was proposed to recognize driver fatigue status under real driving conditions based on approximate-entropy (ApEn) and complexity of steering wheel angle (SWA). This approach first calculated a non-linear ApEn and complexity of SWA in a short time sliding window based on structural theory of
dynamic time-series data analysis. Then a hard threshold based multi-level fatigue status detection model was designed to process the non-linear feature from first stage, and the pre-diagnosis of fatigue level was outputted from a two-input-two-output fatigue mapping rule based on observed samples. The pre-diagnosed fatigue statuses of the observed sample and its adjacent samples were compared to determine the three-level fatigue status of the observed sample. The experiment was set on real road driving environment. The results show that the approach performs an averaged accuracy of 84.6% for three-level fatigue detection on real road driving test. Therefore, the approach can be especially applied to several industrial applications.

Key words: automotive safety, fatigue recognition, steering wheel angle (SWA), approximate entropy (ApEn), complexity