  Academic research in the field of autonomous vehicles has reached high
popularity in recent years related to several topics as sensor technologies,
V2X communications, safety, security, decision making, control, and even legal
and standardization rules. Besides classic control design approaches,
Artificial Intelligence and Machine Learning methods are present in almost all
of these fields. Another part of research focuses on different layers of Motion
Planning, such as strategic decisions, trajectory planning, and control. A wide
range of techniques in Machine Learning itself have been developed, and this
article describes one of these fields, Deep Reinforcement Learning (DRL). The
paper provides insight into the hierarchical motion planning problem and
describes the basics of DRL. The main elements of designing such a system are
the modeling of the environment, the modeling abstractions, the description of
the state and the perception models, the appropriate rewarding, and the
realization of the underlying neural network. The paper describes vehicle
models, simulation possibilities and computational requirements. Strategic
decisions on different layers and the observation models, e.g., continuous and
discrete state representations, grid-based, and camera-based solutions are
presented. The paper surveys the state-of-art solutions systematized by the
different tasks and levels of autonomous driving, such as car-following,
lane-keeping, trajectory following, merging, or driving in dense traffic.
Finally, open questions and future challenges are discussed.
