  Automated driving has become a major topic of interest not only in the active
research community but also in mainstream media reports. Visual perception of
such intelligent vehicles has experienced large progress in the last decade
thanks to advances in deep learning techniques but some challenges still
remain. One such challenge is the detection of corner cases. They are
unexpected and unknown situations that occur while driving. Conventional visual
perception methods are often not able to detect them because corner cases have
not been witnessed during training. Hence, their detection is highly
safety-critical, and detection methods can be applied to vast amounts of
collected data to select suitable training data. A reliable detection of corner
cases will not only further automate the data selection procedure and increase
safety in autonomous driving but can thereby also affect the public acceptance
of the new technology in a positive manner. In this work, we continue a
previous systematization of corner cases on different levels by an extended set
of examples for each level. Moreover, we group detection approaches into
different categories and link them with the corner case levels. Hence, we give
directions to showcase specific corner cases and basic guidelines on how to
technically detect them.
