A learning concept for behavior prediction in traffic situations

Future driving assistance systems will need an increase ability to handle complex driving situations and to react appropriately according to situation criticality and requirements for risk minimization. Humans, driving on motorways, are able to judge, for example, cut-in situations of vehicles because of their experiences. The idea presented in this paper is to adapt these human abilities to technical systems and learn different situations over time. Case-Based Reasoning is applied to predict the behavior of road participants because it incorporates a learning aspect, based on knowledge acquired from the driving history. This concept facilitates recognition by matching actual driving situations against stored ones. In the first instance, the concept is evaluated on action prediction of vehicles on adjacent lanes on motorways and focuses on the aspect of vehicles cutting into the lane of the host vehicle.