Abstract: A method and system for generating a reduced complexity logical representation of a data set of sensor data, having a using of an algorithm on the second data set for reducing the complexity of the logical scenario, and an outputting of a third data set representing a reduced complexity logical scenario of the second data set. The invention additionally relates to a method for providing a trained machine learning algorithm for generating a reduced complexity representation of a data set of sensor data.

Abstract: A computer-implemented method for creating a virtual vehicle environment includes: receiving data of a real vehicle environment; generating a first feature vector representing a respective real object by applying a second machine learning algorithm to the respective real object and storing the first feature vector; providing a plurality of stored second feature vectors representing synthetically generated objects; identifying a second feature vector having a greatest degree of similarity to the first feature vector; selecting the identified second feature vector and retrieving a stored synthetic object that is associated with the second feature vector and that corresponds to the real object or procedurally generating the synthetic object that corresponds to the real object, depending on the degree of similarity of the identified second feature vector to the first feature vector; and integrating the synthetic object into a predetermined virtual vehicle environment.

Abstract: A method for determining similarity values of traffic scenarios based on movement profiles of traffic participants in the traffic scenarios includes: generating, by a test device, at least one movement profile of a traffic participant using measurement data, wherein the at least one movement profile comprises at least one movement element, and wherein a new segment in the at least one movement profile begins with a change of a movement element; generating, by the test device, a sequence of the at least one movement profile for an ego vehicle and/or a fellow vehicle of a traffic scenario, wherein a sequence change is generated based on a segment being changed for a movement element; determining, by the test device, a measure of similarity based on movement profiles from at least two traffic scenarios by comparing respective sequences created from the movement profiles; and providing the measure of similarity.

Abstract: A computer-implemented method for creating a virtual vehicle environment includes: receiving data of a real vehicle environment; generating a first feature vector representing a respective real object by applying a second machine learning algorithm to the respective real object and storing the first feature vector; providing a plurality of stored second feature vectors representing synthetically generated objects; identifying a second feature vector having a greatest degree of similarity to the first feature vector; selecting the identified second feature vector and retrieving a stored synthetic object that is associated with the second feature vector and that corresponds to the real object or procedurally generating the synthetic object that corresponds to the real object, depending on the degree of similarity of the identified second feature vector to the first feature vector; and integrating the synthetic object into a predetermined virtual vehicle environment.

Abstract: A method and a device for testing a control unit, in which sensor data are transmitted over a network connection to a real or simulated control unit, which data are calculated by a data processing system using simulation, in which the simulation of the sensor data takes place at least in part with at least one graphics processor of at least one graphics processor unit of the data processing system. The simulated sensor data are encoded in image data that are output via a visualization interface to a data conversion unit that simulates a visualization unit connected to the visualization interface. Via the data conversion unit the received image data are converted into packet data containing the sensor data through the network connection to the control unit.

Abstract: A method for real-time testing of a control unit for an internal combustion engine using a simulator is provided. The control unit and the simulator are connected to one another by a first data channel. The control unit transmits engine control data to the simulator through the first data channel and the simulator calculates engine state variables in real time on its first simulator processor with a first sampling step size and transmits at least some of the engine state variables to the control unit. Thus, selected engine state variables can be made available at a different frequency, and in particular at a higher frequency, than is possible by the first sampling step size of the first simulator processor, in that the simulator calculates at least one specific engine state variable using a partial engine model and with a second sampling step size different from the first sampling step size.

Abstract: A method and a device for testing a control unit, in which sensor data are transmitted over a network connection to a real or simulated control unit, which data are calculated by a data processing system using simulation, in which the simulation of the sensor data takes place at least in part with at least one graphics processor of at least one graphics processor unit of the data processing system. The simulated sensor data are encoded in image data that are output via a visualization interface to a data conversion unit that simulates a visualization unit connected to the visualization interface. Via the data conversion unit the received image data are converted into packet data containing the sensor data through the network connection to the control unit.