A learning concept for behavior prediction at intersections

The idea presented in this paper is an online learning approach for behavior prediction of other road participants at an intersection. Learning traffic situations online has the advantage that it is possible to react to changes in driving behavior due to changes in the environment. If visual obstruction occurs because of changes in the environment, e.g. a growing corn field, the behavior of drivers changes. In contrast to pre-trained models an online learning concept is able to react to these changes in driving behavior. In this contribution Case-Based Reasoning, a concept which adapts human reasoning and thinking to a system, is used. The functionality of the concept is shown by predicting the maneuver of an approaching vehicle at an intersection. The presented concept is able to predict if a vehicle turns in front of the ego-vehicle or stops and give the ego-vehicle right of way.