Decision making through stochastic maneuver validation for overtaking on country roads

Abstract

Driver assistance systems have become more and more important in recent years due to the increasing degree of automation in road traffic - especially with regard to safety. Often a driver's perception of a situation is prone to inaccuracy, particularly on country roads. Consequently, there is a high mortality rate due to frontal collisions with oncoming traffic (among others). One of the most dangerous maneuvers that leads to such conflicts is overtaking. Overtaking involves other traffic participants and therefore uncertainties exist. We cope with this challenge by introducing a system that evaluates the options a vehicle has during overtaking (completing or aborting the maneuver) using stochastic models of the surrounding traffic. The stochastic models are used to predict the movements of surrounding road users. As a next step, the general feasibility of the possible maneuvers is checked and rated. The results of this analysis can either be directly used for longitudinal control, be forwarded to a low level controller, or serve as a guideline for the decision-making process of a human driver.