  The usage of environment sensor models for virtual testing is a promising
approach to reduce the testing effort of autonomous driving. However, in order
to deduce any statements regarding the performance of an autonomous driving
function based on simulation, the sensor model has to be validated to determine
the discrepancy between the synthetic and real sensor data. Since a certain
degree of divergence can be assumed to exist, the sufficient level of fidelity
must be determined, which poses a major challenge. In particular, a method for
quantifying the fidelity of a sensor model does not exist and the problem of
defining an appropriate metric remains. In this work, we train a neural network
to distinguish real and simulated radar sensor data with the purpose of
learning the latent features of real radar point clouds. Furthermore, we
propose the classifier's confidence score for the `real radar point cloud'
class as a metric to determine the degree of fidelity of synthetically
generated radar data. The presented approach is evaluated and it can be
demonstrated that the proposed deep evaluation metric outperforms conventional
metrics in terms of its capability to identify characteristic differences
between real and simulated radar data.
