  Scaling the distribution of automated vehicles requires handling various
unexpected and possibly dangerous situations, termed corner cases (CC). Since
many modules of automated driving systems are based on machine learning (ML),
CC are an essential part of the data for their development. However, there is
only a limited amount of CC data in large-scale data collections, which makes
them challenging in the context of ML. With a better understanding of CC,
offline applications, e.g., dataset analysis, and online methods, e.g.,
improved performance of automated driving systems, can be improved. While there
are knowledge-based descriptions and taxonomies for CC, there is little
research on machine-interpretable descriptions. In this extended abstract, we
will give a brief overview of the challenges and goals of such a description.
