  Autonomous driving is of great interest to industry and academia alike. The
use of machine learning approaches for autonomous driving has long been
studied, but mostly in the context of perception. In this paper we take a
deeper look on the so called end-to-end approaches for autonomous driving,
where the entire driving pipeline is replaced with a single neural network. We
review the learning methods, input and output modalities, network architectures
and evaluation schemes in end-to-end driving literature. Interpretability and
safety are discussed separately, as they remain challenging for this approach.
Beyond providing a comprehensive overview of existing methods, we conclude the
review with an architecture that combines the most promising elements of the
end-to-end autonomous driving systems.
