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The availability of representative datasets is an essential prerequisite for
many successful artificial intelligence and machine learning models. However,
in real life applications these models often encounter scenarios that are
inadequately represented in the data used for training. There are various
reasons for the absence of sufficient data, ranging from time and cost
constraints to ethical considerations. As a consequence, the reliable usage of
these models, especially in safety-critical applications, is still a tremendous
challenge. Leveraging additional, already existing sources of knowledge is key
to overcome the limitations of purely data-driven approaches. Knowledge
augmented machine learning approaches offer the possibility of compensating for
deficiencies, errors, or ambiguities in the data, thus increasing the
generalization capability of the applied models. Even more, predictions that
conform with knowledge are crucial for making trustworthy and safe decisions
even in underrepresented scenarios. This work provides an overview of existing
techniques and methods in the literature that combine data-driven models with
existing knowledge. The identified approaches are structured according to the
categories knowledge integration, extraction and conformity. In particular, we
address the application of the presented methods in the field of autonomous
driving.