A Foresighted Driver Model derived from Integral Expected Risk

Current efforts in Advanced Driver Assistant Systems and Autonomous Driving research target at making the vehicles more intelligent, in terms of understanding what is going on and selecting the most appropriate behaviors. A crucial element of this research is the prediction of the evolution of the current driving situation with microscopic driver models. In this paper we present a microscopic driver model with a gradient-like, simple behavior generation that is fully and concisely derived from mathematical risk theory. Following this model, drivers act by estimating the expected, integral future risks and benefits and by seeking the best instantaneous tradeoff between these quantities, choosing the immediate action that reduces the hypothetical risks in the most efficient way. We show how this model is able to incorporate different risk types and situation parameters, allowing an extension and generalization to variable scenarios.