Probabilistic Approach for Modeling and Identifying Driving Situations

Intelligent vehicles need increasing knowledge about both their own state and the driving environment. In this work a novel method for interpreting this information by a reliable detection of relevant driving situations and driving maneuvers is proposed. The information of a situation or maneuver is extracted and hence provided for subsequent processing in the applications. As a result of different situation perception and maneuver realization of the drivers, the selected method is based on probabilistic decisions. Furthermore the inaccuracy of this decision is estimated by the inaccuracies of the sensor measurements. This value can be seen as quality measure of the probabilistic situation and maneuver detection. In addition the model allows to derivate requirements on the sensors, while determining a relevance ranking of the separate sensor information regarding the situation decision.