COMPUTER IMPLEMENTED METHOD AND TEST UNIT FOR APPROXIMATING TEST RESULTS AND A METHOD FOR PROVIDING A TRAINED, ARTIFICIAL NEURAL NETWORK

1 . A computer-implemented method for approximating test results of a virtual test of a device for the at least partially autonomous guidance of a motor vehicle, the method comprising:

receiving a first data set comprising a plurality of driving situation parameters including environment parameters describing the environment of the motor vehicle and EGO parameters describing the state of the motor vehicle, each driving situation parameter having a predetermined domain of definition;

approximating a numerical value range of a target function for the domain of definition of at least one driving situation parameter using a trained, artificial neural network that is applied to the plurality of driving situation parameters, wherein the target function represents a driving maneuver of the motor vehicle in response to a control of the device generated by the plurality of driving situation parameters of the domain of definition; and

providing a second data set including the numerical value range of the target function and the associated domain of definition of the at least one driving situation parameter.

2 . The computer-implemented method according to claim 1 , wherein the EGO parameters comprise a speed of the motor vehicle, and the environment parameters comprise a speed of another motor vehicle and a distance between the motor vehicle and the further motor vehicle.

3 . The computer-implemented method according to claim 2 , wherein the target function is a safety target function having a numerical value, which has a minimum value at a safety distance between the motor vehicle and the further motor vehicle of ≥V FELLOW ×0.55, has a maximum value in a collision between the motor vehicle and the further motor vehicle, and at a safety distance between the motor vehicle and the further motor vehicle of ≤V FELLOW ×0.55, has a value which is greater than the minimum value.

4 . The computer-implemented method according to claim 2 , wherein the target function is a comfort target function or an energy consumption target function, having a numerical value which has a minimum value if there is no change in the acceleration of the motor vehicle, has a maximum value in a collision between the motor vehicle and the further motor vehicle, and has a value between the minimum value and the maximum value when the acceleration of the motor vehicle changes as a function of an amount of the change in the acceleration.

5 . The computer-implemented method according to claim 2 , wherein the plurality of driving situation parameters, or the speed of the motor vehicle and the speed of the further motor vehicle, are generated within the predetermined domain of definition by a random algorithm.

6 . The computer-implemented method according to claim 1 , wherein, for approximating the numerical value range of each target function, a separate artificial neural network is used, and wherein individual hyperparameters of each artificial neural network are stored in a database.

7 . The computer-implemented method according to claim 1 , wherein, as a function of the target function, the artificial neural network uses between 2 to 15 layers, between 6 and 2048 neurons per layer and ReLU, LeakyReLU or ELU as the activation function.

8 . The computer-implemented method according to claim 1 , wherein the second data set including the numerical value range of the target function and the associated domain of definition of the at least one driving situation parameter is adapted to be graphically displayed two-dimensionally or three-dimensionally as a function of a number of driving situation parameters.

9 . A computer-implemented method for providing a trained, artificial neural network for approximating test results of a virtual test of a device for the at least partially autonomous guidance of a motor vehicle, the method comprising:

receiving a first data set of input training data comprising a plurality of driving situation parameters including environment parameters describing the surroundings of the motor vehicle and EGO parameters describing the state of the motor vehicle, wherein each driving situation parameter has a predetermined domain of definition;

receiving a second data set of output training data including a numerical value range of the target function and the associated domain of definition of the at least one driving situation parameter, wherein the output training data is related to the input training data;

training the artificial neural network for approximating a numerical value range of the target function for the domain of definition of at least one driving situation parameter, wherein the target function represents a driving maneuver of the motor vehicle in response to a control of the device generated by the plurality of driving situation parameters of the domain of definition, based on the input training data and the output training data with a training calculation unit; and

providing the trained, artificial neural network for approximating test results of the virtual test of the device for the at least partially autonomous guidance of the motor vehicle.

10 . The computer-implemented method according to claim 9 , wherein the plurality of driving situation parameters, or the speed of the motor vehicle and the speed of the further motor vehicle, are generated within the predetermined domain of definition by a random algorithm and/or by a simulation.

11 . The computer-implemented method according to claim 9 , wherein the artificial neural network is trained by weight settings of a plurality of data sets of driving situation parameters using a gradient descent backpropagation or using the Adam optimization method.

12 . A test unit for approximating test results of a virtual test of a device for the at least partially autonomous guidance of a motor vehicle, the test unit comprising:

an input to receive a first data set comprising a plurality of driving situation parameters including environment parameters describing the environment of the motor vehicle and EGO parameters describing the state of the motor vehicle, each driving situation parameter having a predetermined domain of definition;

a processor for approximating a numerical value range of a target function for the domain of definition of at least one driving situation parameter using a trained, artificial neural network, which is applied to the plurality of driving situation parameters, wherein the target function represents a driving maneuver of the motor vehicle in response to a control of the device generated by the plurality of driving situation parameters of the domain of definition; and

an output to provide a second data set including the numerical value range of the target function and the associated domain of definition of the at least one driving situation parameter.

13 . The test unit according to claim 12 , wherein a driving situation underlying the approximation of the test results of the virtual test of the device, formed in particular as a control unit, is a lane change of another motor vehicle into a lane of the motor vehicle employing the plurality of driving situation parameters.

14 . A computer program with program code for carrying out the method according to claim 1 , when the computer program is executed on a computer.

15 . A computer-readable data carrier with program code of a computer program for carrying out the method according to claim 1 , when the computer program is executed on a computer.