# US 12,762,309 B2 · Method and system for classifying scenarios of a virtual test, and training method > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,762,309 B2 - Document ID: US12762309B2 - Status: Granted - Kind: B2 - Application number: 18/082,756 - Filed: 2022-12-16 - Published: 2023-06-22 - Granted: 2026-10-13 - Earliest claimed: 2021-12-21 - Assignees: dSPACE GmbH - Applicants: dSPACE GmbH - Inventors: Sven Flake - Web page: https://patentgrep.com/patent/US12762309B2 ## CPC - G06F 30/27: using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model - G01M 17/007: Wheeled or endless-tracked vehicles (G01M17/08 takes precedence) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G01M: TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR ## Abstract A computer-implemented method and system for classifying scenarios of a virtual test, including a provision of a first data set of sensor data of a travel of an ego vehicle captured by a plurality of vehicle-side surroundings detection sensors; a transformation of the first data set of sensor data into a data-reduced second data set of sensor data by a first algorithm, in particular a multivariate data analysis method; an application of a second machine learning algorithm to the data-reduced second data set of sensor data for classifying scenarios comprised by the second data set; and an output of a third data set having a plurality of classes representing a vehicle action. Provided is also a computer-implemented method for providing a trained second machine learning algorithm for classifying scenarios of a virtual test. ## Claims ### Claim 1 A computer-implemented method for classifying scenarios of a virtual test, the method comprising: providing a first data set of sensor data of a travel of an ego vehicle captured by a plurality of vehicle-side surroundings detection sensors; transforming the first data set of sensor data into a single data-reduced second data set of sensor data, including the sensor data from each of the plurality of vehicle-side surroundings detection sensors, by a first algorithm or a multivariate data analysis method; applying a second machine learning algorithm to the data-reduced second data set of sensor data for classifying scenarios comprised by the second data set; and outputting a third data set having a plurality of classes representing a vehicle action, wherein the first algorithm carries out a principle component analysis method, and wherein the principle component analysis method combines correlating first features of the plurality of vehicle-size surroundings detection sensors into a single data-reduced feature as a linear combination of values of the plurality of surroundings detection sensors. ### Claim 2 (depends on 1) The computer-implemented method according to claim 1, wherein the plurality of vehicle-side surroundings detection sensors includes an essentially identical field of vision in sections, a data set of a first surroundings detection sensor, a data set of a second surroundings detection sensor, and a data set of a third surroundings detection sensor comprising at least one same object. ### Claim 3 (depends on 2) The computer-implemented method according to claim 2, wherein the first surroundings detection sensor is formed by a radar sensor, the second surroundings detection sensor is formed by a LIDAR sensor, and the third surroundings detection sensor is formed by a camera sensor. ### Claim 4 (depends on 1) The computer-implemented method according to claim 1, wherein the first algorithm further carries out a factor analysis method and/or a correspondence analysis method. ### Claim 5 (depends on 1) The computer-implemented method according to claim 1, wherein the second machine learning algorithm is formed by an artificial neural network, a size of an input layer being given by a number of second features of the data-reduced second data set, and a size of an output layer being given by a number of classes. ### Claim 6 (depends on 5) The computer-implemented method according to claim 5, wherein a size of the input layer of the artificial neural network is identical to a size of the output layer of the artificial neural network. ### Claim 7 (depends on 6) The computer-implemented method according to claim 6, wherein a number of hidden layers of the artificial neural network is smaller than the size of the input layer of the artificial neural network and the size of the output layer of the artificial neural network. ### Claim 8 (depends on 1) The computer-implemented method according to claim 1, wherein the second machine learning algorithm carries out a multiclass classification, in which a probability is calculated for each class, and wherein the class having the highest probability is selected as a prediction. ### Claim 9 (depends on 8) The computer-implemented method according to claim 8, wherein a fourth data set having a logical scenario is generated based on the selected class representing the vehicle action. ### Claim 10 (depends on 1) The computer-implemented method according to claim 1, wherein the plurality of classes representing the vehicle action comprises at least one value of an acceleration operation, a braking operation, a change in direction and/or lane, a travel at a constant speed of the ego vehicle, a lane ID, and/or a time- or location-related condition for carrying out a vehicle action. ### Claim 11 (depends on 1) The computer-implemented method according to claim 1, wherein, for the purpose of transforming the first data set of sensor data into a data-reduced second data set of sensor data, the first algorithm or the multivariate data analysis method comprises: a standardization of the first data set of sensor data of a travel of the ego vehicle captured by the plurality of vehicle-side surroundings detection sensors; a calculation of a covariance matrix from the standardized first data set; a determination of eigenvectors representing principle components; and a creation of a matrix made up of the determined eigenvectors for providing a data-reduced second data set. ### Claim 12 (depends on 1) A computer program including program code for carrying out the method according to claim 1 when the computer program is executed on a computer. ### Claim 13 (depends on 1) A non-transitory computer-readable storage medium including program code for carrying out the method according to claim 1 when executed on a computer. ### Claim 14 (depends on 1) The computer-implemented method according to claim 1, wherein the single data-reduced second data set includes a linear combination of values of the plurality of vehicle-side surroundings detection sensors. ### Claim 15 A computer-implemented method for providing a trained second machine learning algorithm for classifying scenarios of a virtual test, the method comprising: receiving a single data-reduced second data set of sensor data, including the sensor data from each of the plurality of vehicle-side surroundings detection sensors, transformed by a first algorithm or a multivariate data analysis method based on a first data set of sensor data of a travel of an ego vehicle captured by a plurality of vehicle-side surroundings detection sensors; receiving a third data set having a plurality of classes representing a vehicle action; and training the second machine learning algorithm by an optimization algorithm, which calculates an extreme value of a loss function for classifying scenarios of a virtual test, wherein the first algorithm carries out a principle component analysis method, and wherein the principle component analysis method combines correlating first features of the plurality of vehicle-size surroundings detection sensors into a single data-reduced feature as a linear combination of values of the plurality of surroundings detection sensors. ### Claim 16 A system for classifying scenarios of a virtual test, the system comprising: a plurality of vehicle-side surroundings detection sensors to provide a first data set of sensor data of a captured travel of an ego vehicle; a transformer to transform the first data set of sensor data into a single data-reduced second data set of sensor data, including the sensor data from each of the plurality of vehicle-side surroundings detection sensors, by a first algorithm or a multivariate data analysis method; an applicator to apply a second machine learning algorithm to the data-reduced second data set of sensor data for classifying scenarios comprised by the second data set, the applicator being configured to output a third data set having a plurality of classes representing a vehicle action, wherein the first algorithm carries out a principle component analysis method, and wherein the principle component analysis method combines correlating first features of the plurality of vehicle-size surroundings detection sensors into a single data-reduced feature as a linear combination of values of the plurality of surroundings detection sensors.