标题： HCOMC：混合交通环境下双车道高速公路上的分层协作并道控制框架 显示英文标题

标题： HCOMC: A Hierarchical Cooperative On-Ramp Merging Control Framework in Mixed Traffic Environment on Two-Lane Highways

摘要： 高速公路入口合流区域是交通拥堵和事故的常见瓶颈。 目前，基于联网和自动驾驶车辆（CAVs）的协同控制策略是解决这一问题的基本方案。 尽管CAVs尚未全面普及，但仍有必要提出一种分层协同入口合流控制（HCOMC）框架，用于处理双车道高速公路上的异构交通流，以弥补这一差距。 本文扩展了基于智能驾驶员模型的纵向跟车模型，并利用五次多项式曲线横向变道模型，以考虑人工驾驶车辆（HDVs）和CAVs，全面考虑人为因素和协同自适应巡航控制。 此外，本文提出了一种HCOMC框架，包括基于改进虚拟车辆模型的分层协同规划模型、基于博弈论的自愿变道模型，以及使用精英非支配排序遗传算法的多目标优化模型，以确保安全、平稳和高效的合流过程。 随后，通过仿真分析了我们的HCOMC在不同交通密度和CAV渗透率下的性能。 研究结果强调了我们的HCOMC在提高群体车辆的安全性、稳定和加快合流过程、优化交通效率以及节省燃油消耗方面相比基准具有显著的综合优势。 显示英文摘要

摘要： Highway on-ramp merging areas are common bottlenecks to traffic congestion and accidents. Currently, a cooperative control strategy based on connected and automated vehicles (CAVs) is a fundamental solution to this problem. While CAVs are not fully widespread, it is necessary to propose a hierarchical cooperative on-ramp merging control (HCOMC) framework for heterogeneous traffic flow on two-lane highways to address this gap. This paper extends longitudinal car-following models based on the intelligent driver model and lateral lane-changing models using the quintic polynomial curve to account for human-driven vehicles (HDVs) and CAVs, comprehensively considering human factors and cooperative adaptive cruise control. Besides, this paper proposes a HCOMC framework, consisting of a hierarchical cooperative planning model based on the modified virtual vehicle model, a discretionary lane-changing model based on game theory, and a multi-objective optimization model using the elitist non-dominated sorting genetic algorithm to ensure the safe, smooth, and efficient merging process. Then, the performance of our HCOMC is analyzed under different traffic densities and CAV penetration rates through simulation. The findings underscore our HCOMC's pronounced comprehensive advantages in enhancing the safety of group vehicles, stabilizing and expediting merging process, optimizing traffic efficiency, and economizing fuel consumption compared with benchmarks.