论文标题
巷道中的深度加强学习合并的互联车辆协调
Deep Reinforcement Learning in Lane Merge Coordination for Connected Vehicles
论文作者
论文摘要
在本文中，使用集中式系统为连接的车辆提供了巷道合并协调的框架。向道路上的连接车辆提供轨迹建议是基于交通编排者和数据融合作为主要组件的。深入强化学习和数据分析用于预测连接车辆的轨迹建议，并考虑到这些建议的未连接车辆。结果突出了交通编排者的适应性，当时在看不见的现实世界中使用深层Q网络时。还提供了不同强化学习模型和评估与关键绩效指标（KPI）的性能比较。
In this paper, a framework for lane merge coordination is presented utilising a centralised system, for connected vehicles. The delivery of trajectory recommendations to the connected vehicles on the road is based on a Traffic Orchestrator and a Data Fusion as the main components. Deep Reinforcement Learning and data analysis is used to predict trajectory recommendations for connected vehicles, taking into account unconnected vehicles for those suggestions. The results highlight the adaptability of the Traffic Orchestrator, when employing Dueling Deep Q-Network in an unseen real world merging scenario. A performance comparison of different reinforcement learning models and evaluation against Key Performance Indicator (KPI) are also presented.
