  Designing a controller for autonomous vehicles capable of providing adequate
performance in all driving scenarios is challenging due to the highly complex
environment and inability to test the system in the wide variety of scenarios
which it may encounter after deployment. However, deep learning methods have
shown great promise in not only providing excellent performance for complex and
non-linear control problems, but also in generalising previously learned rules
to new scenarios. For these reasons, the use of deep learning for vehicle
control is becoming increasingly popular. Although important advancements have
been achieved in this field, these works have not been fully summarised. This
paper surveys a wide range of research works reported in the literature which
aim to control a vehicle through deep learning methods. Although there exists
overlap between control and perception, the focus of this paper is on vehicle
control, rather than the wider perception problem which includes tasks such as
semantic segmentation and object detection. The paper identifies the strengths
and limitations of available deep learning methods through comparative analysis
and discusses the research challenges in terms of computation, architecture
selection, goal specification, generalisation, verification and validation, as
well as safety. Overall, this survey brings timely and topical information to a
rapidly evolving field relevant to intelligent transportation systems.
