CLIPping the Limits: Finding the Sweet Spot for Relevant Images in
  Automated Driving Systems Perception Testing
Philipp Rigoll Laurenz Adolph Lennart Ries Eric Sax
Perception systems, especially cameras, are the eyes of automated driving
systems. Ensuring that they function reliably and robustly is therefore an
important building block in the automation of vehicles. There are various
approaches to test the perception of automated driving systems. Ultimately,
however, it always comes down to the investigation of the behavior of
perception systems under specific input data. Camera images are a crucial part
of the input data. Image data sets are therefore collected for the testing of
automated driving systems, but it is non-trivial to find specific images in
these data sets. Thanks to recent developments in neural networks, there are
now methods for sorting the images in a data set according to their similarity
to a prompt in natural language. In order to further automate the provision of
search results, we make a contribution by automating the threshold definition
in these sorted results and returning only the images relevant to the prompt as
a result. Our focus is on preventing false positives and false negatives
equally. It is also important that our method is robust and in the case that
our assumptions are not fulfilled, we provide a fallback solution.