论文标题
在连接和自动化的环境中，基于强化学习的合作高速公路工作区合并控制
Cooperative Highway Work Zone Merge Control based on Reinforcement Learning in A Connected and Automated Environment
论文作者
论文摘要
鉴于美国的老化基础设施以及预期的公路工作区数量不断增长，研究工作区合并控制非常重要，这对于改善工作区的安全和能力至关重要。本文提出并评估了基于人工智能实现的合作驾驶行为的新型公路工作区合并控制策略。提出的方法假设所有车辆都是完全自动化，连接和合作的。它在开放的车道中插入了两个计量区，以在封闭车道中合并车辆的空间。此外，封闭车道中的每辆车都学会了如何使用脱机软演员评论家（SAC）增强算法（RL）算法来最佳调整其纵向位置，以在开放车道中找到安全的间隙，考虑到其周围的交通状况。学习结果是在卷积神经网络中捕获的，用于在测试阶段控制单个车辆。通过添加计量区并考虑周围车辆的位置，速度和加速度，可以隐式考虑车辆之间的合作。使用微观流量模拟器对基于RL的模型进行了训练和评估。结果表明，这种基于RL的合并控制极大地超过了流行策略，例如延迟合并和早期合并，在移动性和安全措施方面。
Given the aging infrastructure and the anticipated growing number of highway work zones in the United States, it is important to investigate work zone merge control, which is critical for improving work zone safety and capacity. This paper proposes and evaluates a novel highway work zone merge control strategy based on cooperative driving behavior enabled by artificial intelligence. The proposed method assumes that all vehicles are fully automated, connected and cooperative. It inserts two metering zones in the open lane to make space for merging vehicles in the closed lane. In addition, each vehicle in the closed lane learns how to optimally adjust its longitudinal position to find a safe gap in the open lane using an off-policy soft actor critic (SAC) reinforcement learning (RL) algorithm, considering the traffic conditions in its surrounding. The learning results are captured in convolutional neural networks and used to control individual vehicles in the testing phase. By adding the metering zones and taking the locations, speeds, and accelerations of surrounding vehicles into account, cooperation among vehicles is implicitly considered. This RL-based model is trained and evaluated using a microscopic traffic simulator. The results show that this cooperative RL-based merge control significantly outperforms popular strategies such as late merge and early merge in terms of both mobility and safety measures.
