A holistic approach for modeling and verification of human driver behavior

Driver behavior has long been considered as particularly relevant for the development of automotive applications, especially that recently these applications are increasingly trying to adapt to the driver. However, drivers behave differently in the different traffic situations, hence the need of techniques to enable cars to learn from their drivers and create a model of his behavior. Actually, future generation of cars will be equipped with all sorts of sensing, computing and communication devices that will allow them to acquire all information about the state of the vehicle, the driver and the environment. And, hence make easier the driving behavior learning process. The present paper addresses the problem of modeling and learning the behavior of a driver in an intelligent car by presenting an approach for the construction and verification of a learned driving model. First, we propose a new way for modeling the driver-vehicle and environment, which consists of considering driver-vehicle as a rectangular hybrid input output automaton while representing contextual information about driving environment as conditions on the automaton variables. The construction of the model is ensured through a continuous monitoring of the driver-vehicle and environment system. The use of rectangular predicate states and environmental conditions will facilitate the verification of driving behavior. We then present a formal verification of properties of the constructed model expressed in Probabilistic Computational Tree Logic (PCTL) to assess its convenience to different traffic situations.