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

Xiaoyuan Wang | Zendy; Jin Liu | Zendy; Jinglei Zhang | Zendy

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

Author(s) -

Xiaoyuan Wang,

Jin Liu,

Jinglei Zhang

Publication year - 2012

Publication title -

discrete dynamics in nature and society

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.264

H-Index - 39

eISSN - 1607-887X

pISSN - 1026-0226

DOI - 10.1155/2012/309415

Subject(s) - dynamic bayesian network , computer science , realization (probability) , affection , bayesian probability , propensity score matching , human–computer interaction , artificial intelligence , psychology , social psychology , statistics , mathematics

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. Copyright © 2012 Xiaoyuan Wang et al.

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