  In recent years, considerable progress has been made towards a vehicle's
ability to operate autonomously. An end-to-end approach attempts to achieve
autonomous driving using a single, comprehensive software component. Recent
breakthroughs in deep learning have significantly increased end-to-end systems'
capabilities, and such systems are now considered a possible alternative to the
current state-of-the-art solutions. This paper examines end-to-end learning for
autonomous vehicles in simulated urban environments containing other vehicles,
traffic lights, and speed limits. Furthermore, the paper explores end-to-end
systems' ability to execute navigational commands and examines whether improved
performance can be achieved by utilizing temporal dependencies between
subsequent visual cues. Two end-to-end architectures are proposed: a
traditional Convolutional Neural Network and an extended design combining a
Convolutional Neural Network with a recurrent layer. The models are trained
using expert driving data from a simulated urban setting, and are evaluated by
their driving performance in an unseen simulated environment. The results of
this paper indicate that end-to-end systems can operate autonomously in simple
urban environments. Moreover, it is found that the exploitation of temporal
information in subsequent images enhances a system's ability to judge movement
and distance.
