Abstract: A computer-implemented method and system for classifying a predefined traffic situation comprised by a data record of environment data of a motor vehicle, with an application of a directed graph to the first data record. Nodes of the directed graph segment the first data record in each case into at least one segment of a movement behavior of the ego vehicle and/or the fellow vehicle relative to a vehicle environment according to a first condition satisfied in a time interval comprising edges of the directed graph symbolizing links between the respective nodes. The predefined traffic situation is classified if all of the specified segments meet a second condition of the predefined traffic situation. A class is outputted representing the predefined traffic situation and/or a respective start and end time of the second data record comprising a segment representing the predefined traffic situation.

Abstract: A computer-implemented method and system for classifying traffic situations of a virtual test. The method comprises concatenating a plurality of determined data segments of the lateral and longitudinal behavior of the ego vehicle to identify vehicle actions and classifying traffic situations by linking a subset of the determined data segments of the lateral and longitudinal behavior of the ego vehicle with the identified vehicle actions. The invention further comprises a computer-implemented method for providing a trained machine learning algorithm for classifying traffic situations of a virtual test.

Abstract: A method and system for generating a reduced complexity logical representation of a data set of sensor data, having a using of an algorithm on the second data set for reducing the complexity of the logical scenario, and an outputting of a third data set representing a reduced complexity logical scenario of the second data set. The invention additionally relates to a method for providing a trained machine learning algorithm for generating a reduced complexity representation of a data set of sensor data.