Machine learning techniques have outperformed numerous rule-based methods for decision-making in autonomous vehicles. Despite recent efforts, lane changing remains a major challenge, due to the complex driving scenarios and changeable social behaviors of surrounding vehicles. To help improve the state of the art, we propose to leveraging the emerging \underline{D}eep \underline{R}einforcement learning (DRL) approach for la\underline{NE} changing at the \underline{T}actical level. To this end, we present "DRNet", a novel and highly efficient DRL-based framework that enables a DRL agent to learn to drive by executing reasonable lane changing on simulated highways with an arbitrary number of lanes, and considering driving style of surrounding vehicles to make better decisions. Furthermore, to achieve a safe policy for decision-making, DRNet incorporates ideas from safety verification, the most important component of autonomous driving, to ensure that only safe actions are chosen at any time. The setting of our state representation and reward function enables the trained agent to take appropriate actions in a real-world-like simulator. Our DRL agent has the ability to learn the desired task without causing collisions and outperforms DDQN and other baseline models.

翻译：机器学习技术已在自动驾驶决策中超越了许多基于规则的方法。尽管近年来有所进展，但由于复杂的驾驶场景及周围车辆多变的社会行为，车道变更仍是一项重大挑战。为促进现有技术水平提升，我们提出利用新兴的深度强化学习（DRL）方法，在战术层面实现车道变更。为此，我们提出了“DRNet”——一种新颖高效的DRL框架，使DRL智能体能够在任意车道数的模拟高速公路上通过学习执行合理的车道变更来驾驶，并考虑周围车辆的驾驶风格以做出更优决策。此外，为实现安全的决策策略，DRNet融入了安全验证的思想——这是自动驾驶最关键的部分，以确保任何时候仅选择安全动作。我们的状态表示与奖励函数设置，使训练后的智能体能够在类现实模拟器中采取适当行动。该DRL智能体能够无碰撞地学习目标任务，且性能优于DDQN及其他基线模型。