Abstract: A computer-implemented method for generating a knowledge graph for traffic motion prediction. The method includes: receiving environment sensor data of at least one environment sensor of an ego-vehicle; receiving map data from an electronic road map; extracting the information regarding the at least one traffic participant from the environment sensor data and extracting the information regarding the motion track the traffic participant is positioned on from the map data; and generating a knowledge graph of the road network in the environment of the ego-vehicle including nodes and edges based on the map data and/or the environment sensor data. The knowledge graph includes at least one node representing the traffic participant and at least one node representing the lane the traffic participant is positioned on.

Abstract: A method for training a foundation model and/or a graph-based neural network. The method includes: providing at least one image and/or video file having image information from at least one domain and at least one image label; providing at least one general knowledge graph having information about the at least one domain; providing at least one textual description of image information of the at least one image and/or video datum; embedding the at least one textual description in the graph-based neural network using a large language model; embedding the general knowledge graph in the graph-based neural network; generating a graph-text feature vector by the graph-based neural network as a function of the at least one textual description and the general knowledge graph; generating an image feature vector by the foundation model; training the foundation model or the graph-based neural network.

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.

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.