Motivation-Based Approach To Behavior Prediction

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. For future assistance systems, the assisting system must be aware of the current overall traffic situation. In order to judge the risks in a situation, the assistance system must be able to predict the behavior of the traffic participants around. 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, for example the navigation motivation of taking an exit or of changing lanes to keep a constant speed, concrete situation specific goals are derived. A planning component generates the possible and fulfillable plans for all vehicles in the scenario with respect to the set of 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. In the situations shown, a prediction for an upcoming lane change of a vehicle can be made just by the knowledge of pre-defined motivations and the observation of a sequence of actions.