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.

翻译：当前，决策制定是自动驾驶领域最大的挑战之一。本文提出了一种结合深度Q网络与控制理论洞察的方法，用于在高速公路场景下安全导航自动驾驶车辆。通过训练模拟环境中的深度Q网络作为核心决策单元，为轨迹规划器提供目标。生成的轨迹与纵向运动控制器相结合，用于执行车道变换操作。为验证该方法的有效性，在两种不同的高速公路交通场景中进行了评估。此外，分析了不同状态表示对性能及训练过程的影响。结果表明，所提系统能够产生高效且安全的驾驶行为。