Abstract: A method for determining an uncertainty associated with trajectory predictions of a vehicle.

Abstract: A computer-implemented method for generating a control command for an autonomous vehicle. The method includes: capturing sensor data by at least one sensor of the vehicle; generating an input text from the sensor data; interpreting the input text using a machine learning algorithm; and generating a control command for the vehicle from the interpreted input text.

Abstract: A method for controlling a robot device. The method includes: ascertaining potential trajectories of an object in the surrounding area of the robot device by ascertaining in each case one or more potential trajectories using one or more machine learning models and, for each of them, a weighting factor assigned thereto; ascertaining weighted similarity values by ascertaining a weighted similarity value for each of the potential trajectories, including: ascertaining a similarity value that represents a similarity between the potential trajectory and a set of proposed trajectories according to at least one similarity metric, and ascertaining the weighted similarity value by weighting the similarity value according to the weighting factor assigned to the potential trajectory; adapting the set of proposed trajectories to ascertain an adapted set of proposed trajectories that results in a reduced sum of the plurality of weighted similarity values.

Abstract: Methods for training and using a machine learning diffusion model to generate graph data based on samples from a data distribution as input. The diffusion model includes one or more diffusion layers, and the graph data include node attributes and edge attributes. The training method includes a diffusion process, including a forward- and a reverse-time pass, to learn parameters of the diffusion layers, and a joint diffusion process, including solving a forward- and a reverse-time stochastic differential equation. Both equations are based on both the node and edge attributes, the reverse-time equation being additionally based on the learnt parameters of the diffusion layers. Both equations are solved for both for the node and the edge attributes simultaneously. The trained diffusion model is provided for use, in which use method the trained diffusion model repeatedly performs the reverse-time pass to obtain graph data based on input samples.

Abstract: A method for lossily compressing a sequence of video frames into a representation, wherein each video frame comprises pixels that carry color values. The method includes: segmenting each video frame into superpixels, wherein these superpixels are groups of pixels that share at least one predetermined common property; assigning, to each superpixel in each video frame, at least one attribute derived from the pixels belonging to the respective superpixel; and combining superpixels as nodes in a graph representation, wherein superpixels in a same video frame are connected by spatial edges associated with at least one quantity that is a measure for a distance between these superpixels; and in response to superpixels in adjacent video frames in the sequence meeting at least one predetermined relatedness criterion, these superpixels are connected by temporal edges.

Abstract: A computer-implemented method of predicting dynamics of objects in a surrounding of a vehicle is disclosed. The method starts with a step of receiving a first data sets characterizing dynamics of the objects respectively. Then, each of the first data sets is propagated through an encoder outputting a latent representation for each of the first data sets. Then, a graph based on the latent representations is generated. Then, the graph is propagated through a Graph Neural Network outputting an updated graph. Based on the updated graph a decoder outputs a predicted dynamic for selected object for a subsequent time step.

Abstract: A method for training a neural network system to predict the behavior of a set of interacting agents.

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 for generating additional training data for training a machine learning algorithm is disclosed. The method includes (i) providing training data for training the machine learning algorithm, wherein the training data includes labeled sensor data from at least one sensor, (ii) transforming the training data for training the machine learning algorithm in a graph structure, wherein nodes in the graph structure represent objects represented in the corresponding sensor data, and wherein a starting node of the graph structure represents the position of the at least one sensor with respect to the objects represented in the corresponding sensor data, and (iii) generating additional training data for training the machine learning model by modifying the graph structure.

Abstract: A method for lossily compressing a sequence of video frames into a representation, wherein each video frame comprises pixels that carry color values. The method includes: segmenting each video frame into superpixels, wherein these superpixels are groups of pixels that share at least one predetermined common property; assigning, to each superpixel in each video frame, at least one attribute derived from the pixels belonging to the respective superpixel; and combining superpixels as nodes in a graph representation, wherein superpixels in a same video frame are connected by spatial edges associated with at least one quantity that is a measure for a distance between these superpixels; and in response to superpixels in adjacent video frames in the sequence meeting at least one predetermined relatedness criterion, these superpixels are connected by temporal edges.

Abstract: A method for generating a graph structure for training a graph neural network. The method includes: obtaining data representing a computational graph, wherein the computational graph comprises a plurality of nodes connected by edges; and generating the graph structure for training the graph neural network by removing edges from the computational graph. The edges are removed in such a way that an environment in the computational graph corresponds to an environment in the graph structure.