Abstract: A method for predicting the behavior of at least one road user in a traffic situation. The method includes: obtaining a graph representation of the traffic situation, wherein nodes represent road users, edges represent interactions between the road users and define an adjacency between road users, each node is associated with a state, and each edge is associated with edge attributes; computing an evolution of the states of the nodes based at least in part on a self-evolution of the state of each considered node that is dependent on this state and mediated by a self-evolution operator; and an interaction of each considered node with other nodes that is dependent on the states of these other nodes and mediated by an interaction operator; and computing a sought property that characterizes the behavior of the at least one road user.

Abstract: A method for training a neural network for evaluating measurement data. The neural network includes a feature extractor for generating feature maps. The method includes: providing training examples labeled with target outputs; providing a generic knowledge graph; selecting a subgraph relating to a context for solving a specified task; ascertaining, for each training example, a feature map using the feature extractor; ascertaining, from the respective training example, a representation of the subgraph in the space of the feature maps; evaluating an output from the feature map; assessing, using a specified cost function, to what extent the feature map is similar to the representation of the subgraph; optimizing parameters that characterize the behavior of the neural network; and adjusting the evaluation of the feature maps such that the output for each training example corresponds as well as possible to the target output for the respective training example.

Abstract: A computer-implemented method for training a classifier for classifying an input signal, the input signal comprising image data, the classifier comprising an embedding part configured to determine an embedding depending on the input signal inputted into the classifier and a classification part configured to determine a classification of the input signal depending on a the embedding. The method includes: providing a first training data set of training samples, each training sample comprising an input signal and a corresponding desired classification out of a plurality of classes, providing, in a knowledge graph, additional information associated with at least one of the target classifications, providing a knowledge graph embedding method of the knowledge graph, providing a knowledge graph embedding of the knowledge graph obtained by use of a knowledge graph embedding method, training the embedding part depending on the knowledge graph embedding and the first training data set.