[论文解读] Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs

Jonathan Lee, Han Wang+62 more|arXiv (Cornell University)|Feb 26, 2024

Traffic control and management被引用 7

一句话总结

本论文提出 MegaController/MegaVanderTest——一个在开放道路上部署100辆 CAV 的现场实验，通过一个分层、模块化控制框架将集中式 Speed Planner 与分散式 Vehicle Controllers 配对，在混合自治环境中平滑交通流。

ABSTRACT

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,"are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment leveraged a heterogeneous fleet of 100 longitudinally-controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this paper. The MegaController is a hierarchical control architecture, which consists of two main layers. The upper layer is called Speed Planner, and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock on-board sensors. The Speed Planner ingests live data feeds provided by third parties, as well as data from our own control vehicles, and uses both to perform the speed assignment. The architecture of the speed planner allows for modular use of standard control techniques, such as optimal control, model predictive control, kernel methods and others, including Deep RL, model predictive control and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers, or only some. Control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars, and electronic selection of ACC set points in others. The proposed architecture allows for the combination of all possible settings proposed above. Most configurations were tested throughout the ramp up to the MegaVandertest.

研究动机与目标

• 抑制由人类驾驶行为引发的交通不稳定性（幻堵）。
• 设计并部署一个模块化、分层的控制框架（Speed Planner + Vehicle Controllers）用于混合自治交通。
• 在可扩展的控制架构中实现异质车辆的仪表与传感能力。
• 在大规模开放道路测试中评估 MegaController 的真实世界性能，涉及多样的车型/品牌。

提出的方法

• 提出一个两层的 MegaController 架构，服务器端的 Speed Planner 与车辆端的 Vehicle Controllers。
• 使用模块化设计以适应异质车辆接口和传感能力。
• 结合 INRIX 与 AV ping 的数据融合以执行交通状态估计并生成速度规划。
• 实现基于加速度和基于 ACC 的车辆控制器，配以安全封装与变线恢复机制。
• 对宏观与微观交通动力学建模并分析平均场极限，以将有限的 AV 车队与连续描述联系起来。
• 描述在 I-24 上的 MegaVanderTest 的实际部署，涉及 100 辆车与开放道路数据采集。

实验结果

研究问题

• RQ1 在开放高速公路的混合自治交通中，集中式 Speed Planner 与分散式 Vehicle Controllers 是否能减少停走波？
• RQ2 来自异质源（INRIX 与 AV pings）的数据融合如何影响交通状态估计与速度规划？
• RQ3 面向大规模 AV 部署的模块化、车辆异质性控制框架的设计优势与挑战是什么？
• RQ4 在混合车队中，加速基与基于 ACC 的控制的稳定性与安全性考量有哪些？
• RQ5 如何利用宏观与微观模型（ODEs/PDEs、均值场极限）在现实世界情境中指导最优控制？

主要发现

• MegaVanderTest 于 2022 年 11 月部署了 100 辆车辆，代表对交通平滑的大规模开放道路测试。
• MegaController 整合了集中式 Speed Planner 与分散式 Vehicle Controllers，以协调流量改进。
• 该架构支持跨异质车辆平台的各种控制器算法与传感接口。
• Speed Planner 将来自多个来源的数据进行融合并考虑时延，以提升交通状态估计和目标速度设计。
• 车辆控制器可以使用基于加速度或基于 ACC 的输入，配以安全封装和变道恢复机制以维护安全和性能。
• 该研究将宏观流量控制方法与微观车辆动力学联系起来，并讨论未来分析的均场形式。