Method and system for classifying scenarios of a virtual test, and training method

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.

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.