Journal of South China University of Technology(Natural Science Edition)

• Traffic & Transportation Engineering • Previous Articles     Next Articles

Driving Mode Selection of Intelligent Vehicles Based on Risky Situation Identification

YAN Li-xin1,2,3 HUANG Zhen4 WU Chao-zhong1,2 QIN Ling-qiao3 ZHU Dun-yao1,2 RAN Bin3   

  1. 1.Intelligent Transportation Systems Center,Wuhan University of Technology,Wuhan 430063,Hubei,China; 2.National Engineering Research Center for Water Transport Safety ( WTSC) ,Wuhan University of Technology,Wuhan 430063,Hubei,China; 3.Department of Civil and Environmental Engineering,University of Wisconsin-Madison,Madison 53706, Wisconsin,USA; 4.School of Automation,Wuhan University of Technology,Wuhan 430070,Hubei,China
  • Received:2016-02-25 Online:2016-08-25 Published:2016-07-04
  • Contact: 黄珍( 1974-) ，女，副教授，主要从事智能交通系统、自动驾驶研究． E-mail:h-zhen@whut.edu.cn
  • About author:严利鑫( 1988-) ，男，博士生，主要从事智能车路关键技术、驾驶行为研究． E-mail: yanlixinits@126． com
  • Supported by:
    Supported by the National Key Technology Research and Development Program of the Ministry of Science and Technology of China( 2014BAG01B03) and the National Natural Science Foundation of China( 61104158)

PDF

399

Abstract: In the development process of intelligent vehicles,it is a necessary and important stage that manual driving and automatic driving jointly play their roles,of which one key problem is selecting an appropriate take-over time from manual driving to automatic driving when a risky situation occurs.In order to improve the driving safety,according to the data collected from a real vehicle test,driving modes are divided into safe driving,warning driving and automatic driving,based on both the driver’s report and the risky situation levels obtained by means of the K-means clustering.Then,by selecting six impact factors ( namely,the average of speed,the time to headway,the standard deviation of steering,the standard deviation of acceleration,the distance away from the lane and the driver's experience) as the feature vectors,a driving mode selection model of intelligent vehicles is constructed based on the sequential minimal optimization ( SMO) algorithm with the radial basis function ( RBF) .Moreover,the constructed model is compared with the algorithms of ID3,RBF network and SVM.The results show that the constructed model achieves an accuracy of up to 91. 7%，which is significantly superior to those of the other three algorithms.

Key words: intelligent vehicle, driving mode, K-Means clustering, sequential minimal optimization, traffic safety