  While automated driving is often advertised with better-than-human driving
performance, this work reviews that it is nearly impossible to provide direct
statistical evidence on the system level that this is actually the case. The
amount of labeled data needed would exceed dimensions of present day technical
and economical capabilities. A commonly used strategy therefore is the use of
redundancy along with the proof of sufficient subsystems' performances. As it
is known, this strategy is efficient especially for the case of subsystems
operating independently, i.e. the occurrence of errors is independent in a
statistical sense. Here, we give some first considerations and experimental
evidence that this strategy is not a free ride as the errors of neural networks
fulfilling the same computer vision task, at least for some cases, show
correlated occurrences of errors. This remains true, if training data,
architecture, and training are kept separate or independence is trained using
special loss functions. Using data from different sensors (realized by up to
five 2D projections of the 3D MNIST data set) in our experiments is more
efficiently reducing correlations, however not to an extent that is realizing
the potential of reduction of testing data that can be obtained for redundant
and statistically independent subsystems.
