  Semantic segmentation is a crucial component for perception in automated
driving. Deep neural networks (DNNs) are commonly used for this task and they
are usually trained on a closed set of object classes appearing in a closed
operational domain. However, this is in contrast to the open world assumption
in automated driving that DNNs are deployed to. Therefore, DNNs necessarily
face data that they have never encountered previously, also known as anomalies,
which are extremely safety-critical to properly cope with. In this work, we
first give an overview about anomalies from an information-theoretic
perspective. Next, we review research in detecting semantically unknown objects
in semantic segmentation. We demonstrate that training for high entropy
responses on anomalous objects outperforms other recent methods, which is in
line with our theoretical findings. Moreover, we examine a method to assess the
occurrence frequency of anomalies in order to select anomaly types to include
into a model's set of semantic categories. We demonstrate that these anomalies
can then be learned in an unsupervised fashion, which is particularly suitable
in online applications based on deep learning.
