Abstract: A method for training a machine learning model. The method includes: determining a plurality of training sequences of training-input data elements, wherein for each training sequence each training-input data element contains sensor data for a time point from a time period assigned to the training sequence in which a prespecified event takes place at least once at one or more respective event time points; determining, for each training-input data element, the temporal distance between the time point for which the training-input data element contains sensor data and one of the one or more respective event time points; and training the machine learning model depending on the determined temporal distances.

Abstract: A method of classifying a driving maneuver performed by another vehicle in an environment of an ego-vehicle. In the method: a time series of a metrologically determined position of the other vehicle relative to the ego-vehicle that extends to a time step t is provided; spatial profiles of lanes in which the other vehicle may be located are provided; for a plurality of driving maneuvers from a predetermined catalog of possible driving maneuvers, conditional probabilities for the other vehicle to perform this driving maneuver at the time t are respectively determined with a predetermined model by using the time series of the position and the profiles of the lanes; by using these conditional probabilities, a most likely position and/or a probability distribution of positions of the other vehicle at the time step t is determined.