Motivation-based approach to behavior prediction

Abstract

The design of advanced driver assistance systems always aims at enabling the driver to master today's traffic in a more safe and comfortable way. In order to judge the risks in a situation, the assistance system must be able to predict traffic behavior. Taking into account all possible future situations for the next few seconds is a task which quickly produces a complexity that can hardly be handled. Taking the human driver as a role model for its software-counterpart, we propose a new concept which aims at modeling anticipation by taking the motivations of drivers as a basis. Starting with a set of motivations typical for highway traffic, concrete situation specific goals are derived. A planning component generates the possible and fulfillable plans for all vehicles with respect to the goals. Then, the observed actions of the vehicles around are compared to these plans in order to derive a plausibility for the underlying intentions. Eventually, prediction is performed for plausible behaviors of vehicles, which are always based on a motivation that can be taken as an explanation for it. First results are shown in simulation for highway exit scenarios.