Abstract: A method of detecting ghost objects in sensor measurements of an environment of a vehicle involves obtaining map context information from a digital road map. Objects having associated attributes are recognized in an environment of the vehicle, and social context information of the objects in relation to one another is generated. All available data of a traffic situation with the vehicle and the objects is stored in a graph structure, having nodes and edges. Relational information is depicted in the graph structure by the edges. Anomalies and patterns are searched for in features of the graph structure while taking the map context information and the social context information into account, whereby ghost objects are classified using recognized anomalies and patterns.

Abstract: A method for trajectory prediction of vehicles in the surroundings of an ego vehicle involves determining degrees of interaction between the vehicles using an attention-based interaction algorithm trained by machine. Individual vehicles from the multitude of the vehicles located in the surroundings of the ego vehicle are identified as relevant to the trajectory prediction by the interaction algorithm and selected for this when their respective degree of interaction with at least one of the vehicles whose trajectory is to be predicted exceeds a predetermined threshold value. In a subsequent learning step, a trajectory prediction algorithm is trained with the vehicles selected as relevant to the trajectory prediction, and the trajectory prediction carried out by the trajectory prediction algorithm is carried out for the vehicles selected as relevant to the trajectory prediction.

Abstract: A computing system can receive motion prediction data from a vehicle, where the motion prediction data is generated by a motion prediction model executing on the vehicle. Based on the motion prediction data, the system can determine predicted trajectories for a plurality of entities in a surrounding environment of the vehicle. The system can evaluate a prediction performance of the motion prediction model by (i) matching, for each respective entity of the plurality of entities, a predicted endpoint of each predicted trajectory of the set of predicted trajectories to one or more underlying lanes of the road segment, (ii) matching a ground truth future position of the entity to one or more underlying lanes of the underlying lane topology, and (iii) determining a distance along one or more lane segments between the lane(s) matched to the predicted endpoint and the lane(s) matched to the ground truth future position.

Abstract: A method for simulating a technical system. Time series are obtained with the aid of a simulation model of the system, variable values being assigned to at least one epistemic parameter of the simulation model, at least one measurement series is obtained by corresponding measurements on the system, for each value of the epistemic parameter, a real-value error measure of the time series obtained for this value with respect to the measurement series and a distribution function of the error measure are calculated, and for the simulation, that value is used, for which the distribution function has the smallest distance to a Heaviside function.

Abstract: A method for determining simulation data. The method includes: providing a simulation probability distribution including a number x of simulation time series, and providing a reference probability distribution including a number y of reference time series; determining a first Gaussian random process for the simulation probability distribution, and determining a second Gaussian random process for the reference probability distribution, a Gaussian random process being assigned a mean and a covariance matrix; calculating a model error with the aid of the 2-Wasserstein distance between the first Gaussian random process and the second Gaussian random process over the Euclidian distance of the means of the first and second Gaussian random processes and the trace of the covariance matrices of the first and second Gaussian random processes; and determining a family of simulation probability distributions by integrating the model form error into an updated Gaussian random process.

Abstract: A computer-implemented method for validating simulation data of a simulation model of a technical system. The method includes: providing simulation data including a number of simulation signals and providing reference data including a number of reference signals, the simulation signals and reference signals being multidimensional signals, at least two-dimensional signals; and determining a score map between a first probability distribution including the simulation data and a second probability distribution including the reference data using the Wasserstein metric, the determination of the score map including: creating a score matrix based on the simulation signals and the reference signals; converting the score matrix into a cost matrix; calculating optimal transport costs for the cost matrix, and converting the optimal transport costs into the score map.

Abstract: A computer-implemented method for validating simulation data of a simulation model of a technical system. The method includes the following steps: providing a number n of simulation signals for a number N of QOIs (Quantities of Interest), of the simulation model and providing a number m of reference signals for a number N of QOIs of a reference corresponding to the QOIs of the simulation model; determining a particular metric for the N QOIs, determining an overall metric based on the N metrics, at least one metric of the N metrics being taken into consideration in weighted form in the overall metric using a respective weighting coefficient, and determining an overall difference between the n simulation signals and m reference signals, using the Wasserstein metric based on the overall metric.

Abstract: A computer-implemented method for validating simulation data of a simulation model of a technical system. The method includes the following steps: providing simulation data including a number of simulation signals and providing reference data including a number of reference signals, the simulation signals and the reference signals being multidimensional signals, at least two-dimensional signals, and determining a metric between a first probability distribution including the simulation data and a second probability distribution including the reference data by using the 1 Wasserstein metric.

Abstract: A method for simulating a technical system. Time series are obtained with the aid of a simulation model of the system, variable values being assigned to at least one epistemic parameter of the simulation model, at least one measurement series is obtained by corresponding measurements on the system, for each value of the epistemic parameter, a real-value error measure of the time series obtained for this value with respect to the measurement series and a distribution function of the error measure are calculated, and for the simulation, that value is used, for which the distribution function has the smallest distance to a Heaviside function.