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

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.