基于驾驶行为多元时间序列特征的愤怒驾驶状态检测

doi:10.13229/j.cnki.jdxbgxb201705014

吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (5): 1426-1435.doi: 10.13229/j.cnki.jdxbgxb201705014

基于驾驶行为多元时间序列特征的愤怒驾驶状态检测

万平^{1, 2, 3}, 吴超仲^{1, 2}, 林英姿³, 马晓凤^{1, 2}

1.武汉理工大学智能交通系统研究中心,武汉 430063;
2.水路公路交通安全控制与装备教育部工程技术研究中心,武汉 430063;
3. 东北大学智能人机系统实验室,美国波士顿02115

收稿日期:2016-06-22 出版日期:2017-09-20 发布日期:2017-09-20
通讯作者: 马晓凤(1981-),女,副研究员,博士.研究方向:驾驶行为与智能交通.E-mail:maxiaofeng@whut.edu.cn
作者简介:万平(1984-),男,博士研究生.研究方向:驾驶行为与安全辅助驾驶.E-mail:pingw04@163.com
基金资助:
国家自然科学基金项目(51775396,51178364,51108362); 美国国家科学基金项目(1333524); 国家留学基金管理委员会项目(201406950045)

Driving anger detection based on multivariate time series features of driving behavior

WAN Ping^{1, 2, 3}, WU Chao-zhong^{1, 2}, LIN Ying-zi³, MA Xiao-feng^{1, 2}

1.Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan 430063, China;
2.Engineering Research Center for Transportation Safety, Ministry of Education, Wuhan 430063, China;
3.Intelligent Human-Machine Systems Laboratory, Northeastern University, Boston 02115, USA

Received:2016-06-22 Online:2017-09-20 Published:2017-09-20

摘要/Abstract

摘要： 为了对“路怒症”进行有效干预,提出了一种基于驾驶行为的愤怒驾驶状态检测模型。在交通繁忙路段开展基于道路事件刺激的愤怒情绪诱导限时实验,获得驾驶人愤怒与中性情绪下的驾驶行为数据。运用分段线性表示方法拟合由方向盘转角与车辆横向位置组成的驾驶行为多元时间序列,并采用自底向上算法对该时间序列进行分段,提取各分段的斜率与时间间隔特征作为模型输入,建立基于支持向量机的愤怒驾驶状态检测模型。结果表明:模型的识别精度在10分段条件下达78.69%,较5分段、20分段分别高8.57%、4.85%。研究结果可为开发基于驾驶行为的愤怒情绪实时检测设备提供理论支持。

关键词: 交通运输系统工程, 愤怒驾驶检测, 多元时间序列, 驾驶行为, 分段线性表示

Abstract: To explore effective approaches of intervention on road rage, a driving anger detection model based on driving behavior is proposed. The driving behavior data in angry and neutral states were acquired by conducting timed experiments for driving anger induction in busy traffic sections. Piecewise Linear Representation (PLR) method was used to fit multivariate time series, which consists of steering wheel angle and vehicle lateral position, and a bottom-up algorithm was implemented to separate the multivariate time series. The slope and time interval of each segment were extracted as the input features of a Support Vector Machine (SVM) model, which was used to recognize the driving anger. The validation results show that the accuracy of the proposed model with ten segments is 78.69%, which is 8.57% and 4.85% higher than that with five segments and twenty segments, respectively. This study may provide reference for the design of real driving anger detection devices based on driving behavior.

Key words: engineering of communication and transportation system, driving anger detection, multivariate time series, driving behaviors, piecewise linear representation

中图分类号:

U491

万平, 吴超仲, 林英姿, 马晓凤. 基于驾驶行为多元时间序列特征的愤怒驾驶状态检测[J]. 吉林大学学报(工学版), 2017, 47(5): 1426-1435.

WAN Ping, WU Chao-zhong, LIN Ying-zi, MA Xiao-feng. Driving anger detection based on multivariate time series features of driving behavior[J]. 吉林大学学报(工学版), 2017, 47(5): 1426-1435.

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基于驾驶行为多元时间序列特征的愤怒驾驶状态检测

Driving anger detection based on multivariate time series features of driving behavior

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