Platooning of connected and autonomous vehicles (CAVs) plays a vital role in modernizing highways, ushering in enhanced efficiency and safety. This paper explores the significance of platooning in smart highways, employing a coupled partial differential equation (PDE) and ordinary differential equation (ODE) model to elucidate the complex interaction between bulk traffic flow and CAV platoons. Our study focuses on developing a Dyna-style planning and learning framework tailored for platoon control, with a specific goal of reducing fuel consumption. By harnessing the coupled PDE-ODE model, we improve data efficiency in Dyna-style learning through virtual experiences. Simulation results validate the effectiveness of our macroscopic model in modeling platoons within mixed-autonomy settings, demonstrating a notable $10.11\%$ reduction in vehicular fuel consumption compared to conventional approaches.

翻译：网联自动驾驶车辆编队在现代高速公路智能化进程中发挥关键作用，可显著提升交通效率与安全性。本文采用耦合偏微分方程与常微分方程模型，深入探究智能高速公路中编队行驶的重要性，揭示了宏观交通流与CAV编队间的复杂交互机理。我们专注于开发面向编队控制的动态规划学习框架，旨在降低燃油消耗。通过利用耦合偏微分方程-常微分方程模型，借助虚拟经验提升了动态规划学习的数据效率。仿真结果验证了所提宏观模型在混合交通环境中建模编队行驶的有效性，相比传统方法，车辆燃油消耗实现了10.11%的显著降低。