  Systems and functions that rely on machine learning (ML) are the basis of
highly automated driving. An essential task of such ML models is to reliably
detect and interpret unusual, new, and potentially dangerous situations. The
detection of those situations, which we refer to as corner cases, is highly
relevant for successfully developing, applying, and validating automotive
perception functions in future vehicles where multiple sensor modalities will
be used. A complication for the development of corner case detectors is the
lack of consistent definitions, terms, and corner case descriptions, especially
when taking into account various automotive sensors. In this work, we provide
an application-driven view of corner cases in highly automated driving. To
achieve this goal, we first consider existing definitions from the general
outlier, novelty, anomaly, and out-of-distribution detection to show relations
and differences to corner cases. Moreover, we extend an existing camera-focused
systematization of corner cases by adding RADAR (radio detection and ranging)
and LiDAR (light detection and ranging) sensors. For this, we describe an
exemplary toolchain for data acquisition and processing, highlighting the
interfaces of the corner case detection. We also define a novel level of corner
cases, the method layer corner cases, which appear due to uncertainty inherent
in the methodology or the data distribution.
