A formal model of human driving behavior in vehicular networks
Afaf Bouhoute, Ismaïl Berrada, Mohamed El Kamili · 2014
Vehicular Ad-hoc Networks (VANET) are considered as a promising approach for building a variety of applications for Intelligent Transportation Systems (ITS). They are a kind of mobile networks that enables moving vehicles to exchange information about the driving environment. Although the amount of information disseminated through a VANET provides a great opportunity to enhance traffic safety, a study of the behavior of a human driver towards this information remains as an important axis to ensure safer roads. Driving behavior models are proposed by researchers as an important approach that allows a better understanding of human driving behavior. The main goal of this paper is to model and learn driver behavior in the presence of different type of traffic information. For this, we propose a new formal approach to construct a driving behavior model that will be adapted to an individual driver. To describe the model we define rectangular hybrid input output automata formalism which consists of an adaptation of a set of notions related to hybrid automata concept. Then for model construction, we propose an online passive learning based approach to construct the model according to the observed driving behavior. The constructed model may be useful to predict the driver behavior in the future, prevent unsafe situations and provide more comfort to the driver.