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Automated driving has become a major topic of interest not only in the activeresearch community but also in mainstream media reports. Visual perception ofsuch intelligent vehicles has experienced large progress in the last decadethanks to advances in deep learning techniques but some challenges stillremain. One such challenge is the detection of corner cases. They areunexpected and unknown situations that occur while driving. Conventional visualperception methods are often not able to detect them because corner cases havenot been witnessed during training. Hence, their detection is highlysafety-critical, and detection methods can be applied to vast amounts ofcollected data to select suitable training data. A reliable detection of cornercases will not only further automate the data selection procedure and increasesafety in autonomous driving but can thereby also affect the public acceptanceof the new technology in a positive manner. In this work, we continue aprevious systematization of corner cases on different levels by an extended setof examples for each level. Moreover, we group detection approaches intodifferent categories and link them with the corner case levels. Hence, we givedirections to showcase specific corner cases and basic guidelines on how totechnically detect them.

Corner Cases for Visual Perception in Automated Driving: Some Guidance on Detection Approaches