  Currently decision making is one of the biggest challenges in autonomous
driving. This paper introduces a method for safely navigating an autonomous
vehicle in highway scenarios by combining deep Q-Networks and insight from
control theory. A Deep Q-Network is trained in simulation to serve as a central
decision-making unit by proposing targets for a trajectory planner. The
generated trajectories in combination with a controller for longitudinal
movement are used to execute lane change maneuvers. In order to prove the
functionality of this approach it is evaluated on two different highway traffic
scenarios. Furthermore, the impact of different state representations on the
performance and training process is analyzed. The results show that the
proposed system can produce efficient and safe driving behavior.
