Method, system and program product for training a computer-implemented system for predicting future developments of a traffic scene

A method for training a computer-implemented system for predicting future developments of a traffic scene is proposed, the system comprising at least a perception level for aggregating scene-specific information of an input scene, a backbone network for generating a feature set of latent features based on the scene-specific information, a classifier network that evaluates a specified number of different modes for the future developments of the input scene based on the feature set, and for each mode, a prediction module for generating a prediction for the future development of the input scene. According to the disclosure, the backbone network is trained along with the classifier network by modifying the weights of the backbone network and/or the weights of the classifier network such that a deviation between the learning phase evaluation of the classifier network and a realistic evaluation of the different modes is reduced.