吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (3): 642-647.doi: 10.13229/j.cnki.jdxbgxb201403010

• 论文 • 上一篇    下一篇

不同道路线形下驾驶人认知分散状态监测

金立生1,牛清宁1,刘景华2,秦彦光1,吕欢欢1   

  1. 1. 吉林大学 交通学院,长春 130022;
    2. 郑州宇通客车股份有限公司,郑州 450000
  • 收稿日期:2013-04-08 出版日期:2014-03-01 发布日期:2014-03-01
  • 作者简介:金立生(1975),男,教授,博士生导师.研究方向:汽车安全与智能车辆导航技术.E-mail:jinls@jlu.edu.cn
  • 基金资助:
    教育部新世纪优秀人才基金项目(NCET-10-0435);高等学校博士学科点专项科研基金项目(20110061110036);吉林省人才开发基金项目(801121100417);吉林省科技厅重大项目 (20116017).

Driver cognitive distraction detection in different road lines

JIN Li-sheng1,NIU Qing-ning1,LIU Jing-hua2,QIN Yan-guang1,YU Huan-huan1   

  1. 1.College of Transportation, Jilin University, Changchun 130022, China;
    2.Zhengzhou Yutong Bus Co, Ltd, Zhengzhou 450000, China
  • Received:2013-04-08 Online:2014-03-01 Published:2014-03-01

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摘要： 通过驾驶模拟实验采集了不同驾驶人在不同道路线形下的驾驶行为参数,通过对参数的统计分析,确立了表征正常驾驶和认知分散状态下驾驶的特征参数组。利用提取的特征参数组作为支持向量机模型输入,建立了不同驾驶人在不同道路线形下的认知分散状态监测模型。实验结果表明,在不同道路线形下分别进行监测的准确度(直道88.58%,弯道81.25%)高于采用同一模型不区分道路线形直接进行监测的准确度74.17%。研究同时表明个人驾驶习惯对驾驶人意识监测结果有重要影响。

关键词: 交通运输安全工程, 认知分散, 驾驶行为, 支持向量机

Abstract: Through driving simulator experiments, the original performance data of different drivers on different road lines were collected, from which the characteristic parameters were extracted using statistical analysis. Then cognitive distraction detection models were developed based on the support vector machine in view of different road lines. The characteristic parameters were used as the input of the models. The experiment results show that, using the proposed models, the detection accuracy is 88.58% for straight road, and 81.25% for curve road. The performance of the proposed models is better than that of the universal model, using which the detection accuracy is only 74.17%. Experiment results also show that the cognitive distraction detection is also influenced by the driving styles of the individual drivers.

Key words: transportation safety engineering, cognitive distraction, driving performance, support vector machine

中图分类号: 

  • U492.8
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