Abstract: A method for enriching a knowledge graph. The method includes identifying a sub-graph of the knowledge graph which includes nodes connected to a node which misses information; determining sub-graphs in the knowledge graph which are similar to the identified sub-graph; extracting examples for the missing information from the found sub-graphs; supplying the extracted examples to a large language model as part of a prompt instructing the large language model to generate the missing information; and enriching the knowledge graph with an output generated by the large language model in response to the prompt.

Abstract: A computer-implemented method for trajectory prediction. The method includes: receiving trajectory data of motion trajectories of road users arranged in a surrounding area of the ego vehicle by a prediction module, wherein the trajectory data are arranged in a graph representation; receiving map data of a map representation mapping the surrounding area of the ego vehicle by the prediction module; generating an interaction graph representation for the plurality of road users based on the trajectory data of the road users and roadway location information of the map representation by the prediction module; and predicting a future motion trajectory to be executed for at least one other road user based on the trajectory data, the map data, and the interaction graph representation of the road users by the prediction module.

Abstract: A computer-implemented method for training a classification device including an embedding part and a classification part. The method includes: providing a training dataset comprising a plurality of training examples, wherein each training example comprises an input signal and a desired classification, generating a knowledge graph containing additional information linked to at least one desired classification, wherein the additional information is represented by a plurality of knowledge graph entities and by a plurality of knowledge graph relationships linking the knowledge graph entities, providing input signal embeddings by embedding the input signals in a latent space, providing knowledge graph embeddings by embedding the knowledge graph in the latent space, and performing a training based on the input signal embeddings and the knowledge graph embeddings according to a training objective function which is composed of a regularization loss function and a cross-entropy loss function.

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 predicting trajectories of road users includes (i) representing a traffic scene as an agent interaction graph, each having a node for a road user corresponding to a target vehicle and for one or more other road users and having a plurality of edges, wherein each edge between two of the nodes is associated with a respective edge type, which indicates a type of movement of the road users represented by the nodes relative to each other on a respective roadway, (ii) processing the agent interaction graph by a graph transformer to determine embeddings of the target vehicle and the one or more other road users, wherein the graph transformer has an attention mechanism which takes into account the edge types of the edges of the agent interaction graph, and (iii) predicting at least one trajectory of the target vehicle from the embeddings.

Abstract: A method for generating a behaviour tree for controlling a robot device. The method includes: combining a plurality of predetermined behaviour trees and background knowledge into a behaviour tree knowledge graph, representing the behaviour tree knowledge graph in a latent space; extracting, from a prompt describing a desired behaviour of the robot device, a prompt representation graph; supplementing the prompt representation graph according to relations specified by the behaviour tree knowledge graph; selecting a sub graphs of the behaviour tree knowledge graph depending on a similarity to the supplemented prompt representation graph; and generating the behaviour tree for controlling the robot device by adjusting the selected sub graph according to knowledge from the prompt and the behaviour tree knowledge graph.

Abstract: A method is for training a base model for object detection, trajectory prediction, and/or motion planning of a vehicle. The method includes providing a training data set of image data, with each piece of image data having information about at least one driving scene from a point of view of the vehicle, and providing a knowledge graph including domain-specific knowledge of the at least one driving scene. The method further includes optionally partitioning the image data into a plurality of image sections, and generating information matrices corresponding to the image sections by assigning domain-specific knowledge about the at least one driving scene extracted from the knowledge graph and/or directly from the image data to the plurality of image sections of the image data. The method also includes training the base model based on the information matrices.

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 computer-implemented method for trajectory prediction. The method includes: receiving trajectory data of motion trajectories of road users arranged in a surrounding area of the ego vehicle by a prediction module, wherein the trajectory data are arranged in a graph representation; receiving map data of a map representation mapping the surrounding area of the ego vehicle by the prediction module; generating an interaction graph representation for the plurality of road users based on the trajectory data of the road users and roadway location information of the map representation by the prediction module; and predicting a future motion trajectory to be executed for at least one other road user based on the trajectory data, the map data, and the interaction graph representation of the road users by the prediction module.

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 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.