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：机器学习技术在自动驾驶决策中已超越众多基于规则的方法。尽管近年来取得进展，但由于复杂驾驶场景与周围车辆多变的社会行为，车道变更仍是一项重大挑战。为促进该领域的发展，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"DRNet"——一种新颖高效的DRL框架，使DRL智能体能够在具有任意数量车道的模拟高速公路上，通过考虑周围车辆驾驶风格来执行合理的车道变更，从而学习驾驶并做出更优决策。此外，为实现安全的决策策略，DRNet融合了安全验证思想——自动驾驶中最重要的组成部分，确保任何时刻仅选择安全动作。状态表示与奖励函数的设置使训练后的智能体能够在类似真实世界的模拟器中采取适当行动。我们的DRL智能体能够在避免碰撞的同时学习所需任务，其性能优于DDQN及其他基线模型。