Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey. (arXiv:2205.04712v1 [cs.LG])

The existence of representative datasets is a prerequisite of many successful
artificial intelligence and machine learning models. However, the subsequent
application of these models often involves scenarios that are inadequately
represented in the data used for training. The reasons for this are manifold
and range from time and cost constraints to ethical considerations. As a
consequence, the reliable use of these models, especially in safety-critical
applications, is a huge challenge. Leveraging additional, already existing
sources of knowledge is key to …

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