Abstract: A method for generating a simulation scenario includes: receiving raw data, wherein the raw data comprises a plurality of successive LIDAR point clouds, a plurality of successive camera images, and successive velocity and/or acceleration data; merging the plurality of LIDAR point clouds from a determined region into a common coordinate system to produce a composite point cloud; locating and classifying one or more static objects within the composite point cloud; generating road information based on the composite point cloud, one or more static objects and at least one camera image; locating and classifying one or more dynamic road users within the plurality of successive LIDAR point clouds and generating trajectories for the one or more dynamic road users; creating a simulation scenario based on the one or more static objects, the road information, and the generated trajectories for the one or more dynamic road users; and exporting the simulation scenario.

Abstract: A method for parameterizing a program logic for image synthesis to adapt images synthesized by the program logic to a camera model. A digital photograph of a three-dimensional scene is processed by a neural network and an abstract first representation of the photograph is extracted from a selection of layers of the neural network. The program logic is parameterized according to an initial set of output parameters in order to synthesize an image that recreates the photograph from a three-dimensional model of the scene. The synthetic image is processed by the same neural network, an abstract second representation of the synthetic image is extracted from the same selection of layers, and a distance between the synthetic image and the photograph is calculated based on a metric that takes into account the first representation and the second representation.

Abstract: A computer-implemented method for providing a machine learning algorithm for determining similar scenarios based on scenario data of a data set of sensor data, wherein an optimization algorithm is applied to the feature representation, output by the first machine learning algorithm, of the first augmentation of the data set of sensor data, wherein the optimization algorithm approximates the feature representation, output by the second machine learning algorithm, of the second augmentation of the data set of sensor data. The invention further relates to a method for determining similar scenarios based on scenario data of a data set of sensor data and to a training controller.

Abstract: A method generates synthetic sensor data corresponding to a LiDAR sensor of a vehicle, the synthetic sensor data including superimposed distance and intensity information. The method includes: providing a hierarchical variational autoencoder; conditioning a first feature vector and a second feature vector with a second data set, the second data set including distance and intensity information; combining the conditioned first feature vector and the conditioned second feature vector into a resulting third feature vector; and decoding the resulting third feature vector to generate a third data set of synthetic sensor data, the third data set including superimposed distance and intensity information.

Abstract: A method of reducing training data via a system having an encoder, wherein at least a portion of the training data forms a temporal sequence and is combined into a first set of training data, and the encoder maps input data to prototype feature vectors of a set of prototype feature vectors. A first input datum is received from the first set of training data, and propagated by the encoder. The input datum is assigned one or more feature vectors by the encoder, and depending on the assigned feature vectors, a defined set of prototype feature vectors is determined and assigned to the first input datum. An aggregated vector is created for the first input datum. A second aggregated vector is created for the second input datum and the first and second aggregated vectors are compared and a measure of similarity for the aggregated vectors is determined.