Discrete Dynamics in Nature and Society
Volume 2012 (2012), Article ID 309415, 15 pages
http://dx.doi.org/10.1155/2012/309415
Research Article

Dynamic Recognition Model of Driver’s Propensity under Multilane Traffic Environments

Xiaoyuan Wang,1,2 Jin Liu,1 and Jinglei Zhang1

1School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255091, China
2Department of Civil and Environmental Engineering, School of Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA

Received 2 August 2012; Accepted 22 October 2012

Academic Editor: Wuhong Wang

Copyright © 2012 Xiaoyuan Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Driver’s propensity intends to change along with driving environment. In this paper, the situation factors (vehicle groups) that affect directly the driver’s affection among environment factors are considered under two-lane conditions. Then dynamic recognition model of driver’s propensity can be established in time-varying environment through Dynamic Bayesian Network (DBN). Physiology-psychology experiments and real vehicle tests are designed to collect characteristic data of driver’s propensity in different situations. Results show that the model is adaptable to realize the dynamic recognition of driver’s propensity type in multilane conditions, and it provides a theoretical basis for the realization of human-centered and personalized automobile active safety systems.