  Automated driving is one of the most active research areas in computer
science. Deep learning methods have made remarkable breakthroughs in machine
learning in general and in automated driving (AD)in particular. However, there
are still unsolved problems to guarantee reliability and safety of automated
systems, especially to effectively incorporate all available information and
knowledge in the driving task. Knowledge graphs (KG) have recently gained
significant attention from both industry and academia for applications that
benefit by exploiting structured, dynamic, and relational data. The complexity
of graph-structured data with complex relationships and inter-dependencies
between objects has posed significant challenges to existing machine learning
algorithms. However, recent progress in knowledge graph embeddings and graph
neural networks allows to applying machine learning to graph-structured data.
Therefore, we motivate and discuss the potential benefit of KGs applied to the
main tasks of AD including 1) ontologies 2) perception, 3) scene understanding,
4) motion planning, and 5) validation. Then, we survey, analyze and categorize
ontologies and KG-based approaches for AD. We discuss current research
challenges and propose promising future research directions for KG-based
solutions for AD.
