In this paper, we focus on the problem of highway merge via parallel-type on-ramp for autonomous vehicles (AVs) in a decentralized non-cooperative way. This problem is challenging because of the highly dynamic and complex road environments. A deep reinforcement learning-based approach is proposed. The kernel of this approach is a Deep Q-Network (DQN) that takes dynamic traffic state as input and outputs actions including longitudinal acceleration (or deceleration) and lane merge. The total reward for this on-ramp merge problem consists of three parts, which are the merge success reward, the merge safety reward, and the merge efficiency reward. For model training and testing, we construct a highway on-ramp merging simulation experiments with realistic driving parameters. The experimental results show that the proposed approach can make reasonable merging decisions based on the observation of the traffic environment. We also compare our approach with a state-of-the-art approach and the superior performance of our approach is demonstrated by making challenging merging decisions in complex highway parallel-type on-ramp merging scenarios.

在本文中，我们关注于以分散式非合作方式通过并行类型的自动驾驶汽车匝道合并公路的问题。由于高度动态和复杂的道路环境，该问题具有挑战性。提出了一种基于深度强化学习的方法。这种方法的核心是深度Q网络（DQN），它以动态交通状态作为输入并输出包括纵向加速（或减速）和车道合并的动作。此匝道合并问题的总奖励由三部分组成，分别是合并成功奖励，合并安全奖励和合并效率奖励。为了进行模型训练和测试，我们构建了具有实际驾驶参数的高速公路匝道合并模拟实验。实验结果表明，该方法能够在对交通环境进行观察的基础上做出合理的合并决策。我们还将我们的方法与最先进的方法进行了比较，并且通过在复杂的公路并行类型匝道合并场景中做出具有挑战性的合并决策，证明了该方法的优越性能。