Device and method for training a model, and a vehicle

1. A method for the computer-implemented training of a model, which includes a first sub-model and a second sub-model, the method comprising the following steps:

downscaling digital data to generate first input data;

dividing the digital data into multiple data areas to generate second input data;

generating, by the first sub-model, first sub-model data relating to the first input data fed to the first sub-model;

up-scaling the first sub-model data to form first output data;

generating, by the second sub-model for the multiple data areas, corresponding output data areas relating to the second input data fed to the second sub-model;

assembling the output data areas to form second output data;

combining the first output data and the second output data to form third output data;

training the first sub-model by comparing provided target data, which are assigned to the digital data, and the first output data; and

training the second sub-model by comparing the target data and the third output data, wherein the model is a generative model, the digital data being associated with a first domain and the third output data being associated with a second domain, the method further comprising the following steps:

generating training data via the trained model;

training a second model using the generated training data;

processing second digital data, which are associated with the second domain, via the second model in a driving assistance system; and

determining a controlling command for an actuator of a vehicle based on the processed second digital data.

2. The method as recited in claim 1 , wherein the first sub-model data generated by the first sub-model has the same resolution as the first input data.

3. The method as recited in claim 1 , wherein the first sub-model includes a first auto-encoder.

4. The method as recited in claim 1 , wherein the second sub-model generates the second sub-model data relating to the fed second input data, for each data area of the multiple data areas an associated output data area of the multiple output data areas being generated.

5. The method as recited in claim 1 , wherein the second sub-model includes a second auto-encoder.

6. A training device configured to train a model, which includes a first sub-model and a second sub-model, the training device configured to:

downscale digital data to generate first input data;

divide the digital data into multiple data areas to generate second input data;

generate, by the first sub-model, first sub-model data relating to the first input data fed to the first sub-model;

up-scale the first sub-model data to form first output data;

generate, by the second sub-model for the multiple data areas, corresponding output data areas relating to the second input data fed to the second sub-model;

assemble the output data areas to form second output data;

combine the first output data and the second output data to form third output data;

train the first sub-model by comparing provided target data, which are assigned to the digital data, and the first output data; and

train the second sub-model by comparing the target data and the third output data, wherein the model is a generative model, the digital data being associated with a first domain and the third output data being associated with a second domain, the training device being further configured to:

generate training data via the trained model;

train a second model using the generated training data;

process second digital data, which are associated with the second domain, via the second model in a driving assistance system; and

determine a controlling command for an actuator of a vehicle based on the processed second digital data.