Deep Q-Network Based Decision Making for Autonomous Driving
This addresses the challenge of decision-making for autonomous vehicles, but it is incremental as it builds on existing deep Q-network and control methods.
The paper tackled decision-making for autonomous driving in highway scenarios by combining deep Q-networks with control theory, resulting in a system that produced efficient and safe driving behavior as evaluated in two traffic scenarios.
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.