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

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

WAN Ping1, 2, 3, WU Chao-zhong1, 2, LIN Ying-zi3, MA Xiao-feng1, 2   

  1. 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

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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

CLC Number: 

  • U491
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