Method for training an artificial neural generator network, method for training an artificial neural discriminator network, and test unit

Abstract

A computer-implemented method for training an artificial neural generator network of generative adversarial networks for the purpose of approximating second test results from an identified subset of first test results of a virtual test of a device for at the least partial autonomous guidance of a motor vehicle. The invention also relates to a computer-implemented method for training an artificial neural discriminator network, a test unit, a computer program and a computer-readable data carrier.

What is claimed is: 
     
         1 . A computer-implemented method for training an artificial neural generator network of generative adversarial networks to approximate second test results from an identified subset of first test results of a virtual test of a device for at the least partial autonomous guidance of a motor vehicle, the method comprising:
 providing an artificial neural generator network, which approximates a first parameter set of driving situation parameters made up of surroundings parameters describing surroundings of the motor vehicle and ego parameters describing a status of the motor vehicle, based on randomly generated input data, the second test results being formed by the first parameter set;   provision an artificial neural discriminator network, which receives the first parameter set of driving situation parameters approximated by the artificial neural generator network and a second parameter set or a reference parameter set of driving situation parameters provided by a data source and made up of surroundings parameters describing the surrounding of the motor vehicle and ego parameters describing the status of the motor vehicle, the identified subset of first test results being formed by the second parameter set;   outputting a function value to evaluate the first parameter set or the second parameter set by the artificial neural discriminator network; and   training the artificial neural generator network by the artificial neural discriminator network based on the evaluation of the first parameter set or the second parameter set.   
     
     
         2 . The computer-implemented method according to  claim 1 , wherein, if the function value output by the artificial neural discriminator network is within a first value range, the artificial neural discriminator network determines that the evaluated parameter set has been approximated by the artificial neural generator network and, if the function value output by the artificial neural discriminator network is within a second value range, the artificial neural discriminator network determines that the evaluated parameter set has been provided by the data source. 
     
     
         3 . The computer-implemented method according to  claim 2 , wherein a total value range encompassing the first value range and the second value range is between 0 and 1, the total value range being divided into the first value range and the second value range adjacent to the first value range. 
     
     
         4 . The computer-implemented method according to  claim 1 , wherein the second parameter set (P 2 ) of driving situation parameters provided by the data source is generated by an artificial neural network or by a simulation. 
     
     
         5 . The computer-implemented method according to  claim 1 , wherein the ego parameters comprise a velocity of the motor vehicle, and the surroundings parameters comprise a velocity of another motor vehicle and a distance between the motor vehicle and the other motor vehicle. 
     
     
         6 . The computer-implemented method according to  claim 1 , wherein the artificial neural generator network includes an input layer having 24 neurons and 5 hidden layers, a first hidden layer having 92 neurons and a dropout of 10%, a second hidden layer having 64 neurons and a dropout of 10%, a third hidden layer having 32 neurons and a dropout of 10%, a fourth hidden layer having 24 neurons without a dropout and a fifth hidden layer having 24 neurons without a dropout, and an output layer having 2 neurons for outputting the parameter set of driving situation parameters. 
     
     
         7 . The computer-implemented method according to  claim 1 , wherein the artificial neural discriminator network includes an input layer having 128 neurons and 6 hidden layers, a first hidden layer having 128 neurons and a dropout of 20% and a second through sixth hidden layer having 80 neurons and a dropout of 10%, and an output layer having 1 neuron with a sigmoid activation function. 
     
     
         8 . The computer-implemented method according to  claim 6 , wherein the hidden layers of the artificial neural generator network and the artificial neural discriminator layer use an ELU activation function and an Adam optimization function. 
     
     
         9 . A computer-implemented method for training an artificial neural discriminator network of generative adversarial networks for distinguishing a first parameter set of driving situation parameters from a second parameter set of driving situation parameters of a virtual test of a device for the at least partial autonomous guidance of a motor vehicle, the method comprising:
 receiving by the artificial neural discriminator network, a first parameter set of driving situation parameters approximated by an artificial neural generator network and made up of surroundings parameters describing the surroundings of the motor vehicle and ego parameters describing the status of the motor vehicle;   receiving by the artificial neural discriminator network, a second parameter set or a reference parameter set of driving situation parameters provided by a data source and made up of surroundings parameters describing the surroundings of the motor vehicle and ego parameters describing the status of the motor vehicle;   approximating and outputting a function value by the artificial neural discriminator network to evaluate the first parameter set or the second parameter set; and   training the artificial neural discriminator network based on an approximation error calculated by comparing the output function value with a predefined setpoint value.   
     
     
         10 . The computer-implemented method according to  claim 9 , wherein the function value output by the artificial neural discriminator network is an actual value of the artificial neural discriminator network, the approximation error of the artificial neural discriminator network being constituted by a difference between the actual value and the setpoint value, and a weighting of the of the artificial neural discriminator network being adapted using the approximation error. 
     
     
         11 . The computer-implemented method according to  claim 1 , wherein the artificial neural generator network and the artificial neural discriminator network are trained in turns, the artificial neural discriminator network being trained more frequently than the artificial neural generator network. 
     
     
         12 . The computer-implemented method according to  claim 1 , wherein the artificial neural discriminator network is trained using mini-batches of the parameter sets of driving situation parameters, the artificial neural discriminator network alternately receiving a mini-batch of the first parameter set of driving situation parameters approximated by the artificial neural generator network and a mini-batch of the second parameter set of driving situation parameters provided by the data source. 
     
     
         13 . A test unit for approximating second test results from an identified subset of first test results of a virtual test of a device for the at least partial autonomous guidance of a motor vehicle using generative adversarial networks, which are configured to train an artificial neural generator network, the test unit comprising:
 an artificial neural generator network, which is configured to approximate a first parameter set of driving situation parameters made up of surroundings parameters describing surroundings of the motor vehicle and ego parameters describing the status of the motor vehicle based on randomly generated input data, the second test results being formed by the first parameter set; and   an artificial neural discriminator network, which is configured to receive the first parameter set of driving situation parameters approximated by the artificial neural generator network and a second parameter set or a reference parameter set of driving situation parameters provided by a data source and made up of surroundings parameters describing the surroundings of the motor vehicle and ego parameters describing the status of the motor vehicle, the identified subset of first test results being formed by the second parameter set, the artificial neural discriminator network being configured to output a function value to evaluate the first parameter set or the second parameter set, and the artificial neural discriminator network being configured to train the artificial neural generator network based on the evaluation of the first parameter set or the second parameter set.   
     
     
         14 . A computer program, including program code, for carrying out the method according to  claim 1  when the computer program is run on a computer. 
     
     
         15 . A computer-readable data carrier, including program code of a computer program for carrying out the method according to one of  claim 1  when the computer program is run on a computer.