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定性推理生成器在驾驶疲劳检测中的应用

徐军莉，闵建亮，胡剑锋*，王 平   

  1. （江西科技学院 协同创新中心，南昌 330098，中国）
  • 收稿日期:2017-10-13 出版日期:2018-03-31 发布日期:2018-04-10
  • 通讯作者: 胡剑峰，教授。E-mail: huguess211@hotmail.com。
  • 作者简介:徐军莉(1977—)，女( 汉)，江西，副教授。E-mail: 244095158@qq.com。
  • 基金资助:

    国家自然科学基金(61762045) ；南昌市指导性科技计划项目(13, 洪科发计字[2016]96)。

Application of generator for uncertainty reasoning in fatigue driving inspection

XU Junli, MIN Jianliang, HU Jianfeng*, WANG Ping   

  1. (Center of Collaboration and Innovation, Jiangxi University of Technology, Nanchang 330098, China )
  • Received:2017-10-13 Online:2018-03-31 Published:2018-04-10

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摘要：

       疲劳驾驶是导致交通死亡事故的原因之一。为检测识别驾驶疲劳状态，该文鉴于人的眼动行为存在随机性及模糊性的特点，建立了基于“眼睛闭合时间比”(Per-clos) 和眨眼时间均值的二维云模型，根据这2 个眼动参数的云模型特征，构建了14 条定性规则。依据二维单规则生成器，构造了二维多规则定性推理生成器。对60 例数据，进行识别判断。结果表明：平均识别率达到73.98%。同组实验数据下，使用该方法比采用K 最近邻(KNN) 及支持向量机(SVM) 分类算法的检测率要高。随着训练样本人数增多，生成器的识别率预计可进一步提高。

关键词: 汽车安全, 疲劳驾驶, 眼动特征, 定性推理生成器, 二维云模型

Abstract:

To detect and identify driving fatigue state, which is one of the causes of a traffic death accident; a two-dimensional cloud model was established based on the “percentage of eyelid closure over the pupil over time” (Per-clos) and blink time mean in view of the randomness and fuzziness of eye movement. Fourteen
pieces of qualitative rules were constructed based on the cloud model features of the two parameters. A generator on 2-D and multi-rules was constructed for uncertainty reasoning in fatigue detection based on a 2-D single rule generator to recognize and detect the fatigue state of an experimental data of 60 samples. The analysis results show that the average recognition rate is 73.98%. Therefore, the method has higher detection rate than classification algorithm KNN (k-nearest neighbor) and SVM (support vector machine) under the same experimental data. The recognition rate of the generator can be improved when the number of training samples increases.

Key words: automobile safety, fatigue driving, eye movement characteristics, generator for uncertainty reasoning, 2-D cloud model