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.

通过耦合的偏微分方程和常微分方程模型，本研究着眼于发展针对车队控制的 Dyna 式计划和学习框架，旨在减少燃料消耗，模拟结果验证了我们宏观模型在混合自治设置中建模车队的有效性，与传统方法相比，燃料消耗明显减少了 10.11%。

混合自主交通中用宏观模型进行车队行驶的动态学习

Dyna-Style Learning with A Macroscopic Model for Vehicle Platooning in  Mixed-Autonomy Traffic

Autonomous driving has attracted significant attention from both academia and
industries, which is expected to offer a safer and more efficient driving
system. However, current autonomous driving systems are mostly based on a
single vehicle, which has significant limitations which still poses threats to
driving safety. Collaborative perception with connected and autonomous vehicles
(CAVs) shows a promising solution to overcoming these limitations. In this
article, we first identify the challenges of collaborative perception, such as
data sharing asynchrony, data volume, and pose errors. Then, we discuss the
possible solutions to address these challenges with various technologies, where
the research opportunities are also elaborated. Furthermore, we propose a
scheme to deal with communication efficiency and latency problems, which is a
channel-aware collaborative perception framework to dynamically adjust the
communication graph and minimize latency, thereby improving perception
performance while increasing communication efficiency. Finally, we conduct
experiments to demonstrate the effectiveness of our proposed scheme.

自动驾驶引起学术界和工业界的广泛关注，但目前的自动驾驶系统大多基于单一车辆，存在较大限制，可能威胁驾驶安全。基于连接和自动驾驶车辆的协作感知被认为是解决这些限制的一种有前景的方法。本文首先确定了协作感知的挑战，如数据共享异步性、数据量和姿态误差等。然后，我们讨论了使用各种技术解决这些挑战的可能方法，并阐述了相关的研究机会。此外，我们提出了一种应对通信效率和延迟问题的方案，即一种通道感知的协作感知框架，通过动态调整通信图并减小延迟，提高感知性能和通信效率。最后，我们进行实验证明了我们提出方案的有效性。