CPS: Small: Behaviorally Compatible, Energy Efficient, and Network-Aware Vehicle Platooning Using Connected Vehicle Technology

该项目的目标是在智能互联社区中探索大规模的车辆队列。该方法是开发技术和模型，为车辆加入排并保持其排成员提供激励。互联汽车技术有助于形成车辆队列（车辆之间间隔较小的虚拟列车），其好处包括提高能源效率、增加道路容量和增强机动性。车队中的车辆可能会根据它们在虚拟列车中的位置而获得不同的收益（例如，与中间的车辆相比，领先的车辆节省的能量更少）。因此，一些车辆可能不愿意加入一个排，或者在更好的组队机会出现时留在一个排。这个项目探讨了如何形成和控制具有这些竞争目标的排，从而了解如何激励车辆参与其中。Mcity的试验台是对研究工作进行验证的一部分，当地高中生和大学生也计划参加研讨会。一个稳定的排结构不包含任何可以通过交换排成员来增加各自效用的车辆联盟。考虑到交通流的动态性，形成和保持稳定的排结构是一项复杂的任务，并且需要考虑排形成时的局部和网络级条件。该建议引入了一个通用框架，该框架通过使基于最优控制的轨迹规划模型也能考虑网络级交通状况，从而增强了这些模型。该方案进一步将稳定排的形成整合到增强的轨迹规划模型中，使其能够结合本地和网络级信息，形成行为兼容的排结构，在动态交通流中保持稳定。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The goal of this project is to explore vehicle platooning at scale in Smart and Connected Communities. The approach is the development of techniques and models that provide incentives for vehicles to join platoons and maintain their platoon memberships. Connected vehicle technology helps in forming vehicle platoons (virtual trains of vehicles traveling with small gaps between them) with benefits including improved energy efficiency, increased road capacity, and enhanced mobility. Vehicles in a platoon may benefit differently depending on where they are in the virtual train (for example, the lead saves less energy compared to a vehicle in the middle). As such, some vehicles may not be willing to join a platoon, or stay in one as better opportunities for platoon formation arise. This project explores how platoons with these competing goals will be formed and controlled, so as to understand how to motivate vehicles to participate in them. The Mcity testbed is part of the validation of the research work, and workshops involving local high-school and college students are planned. A stable platoon structure does not contain any coalition of vehicles who could increase their individual utilities by trading their platoon memberships. Given the dynamic nature of traffic streams, forming and maintaining stable platoon structures is a complex task, and requires accounting for both local and network-level conditions at the time of platoon formation. This proposal introduces a general framework that enhances optimal-control-based trajectory planning models by enabling them to also account for network level traffic conditions. This proposal further integrates stable platoon formation into the enhanced trajectory planning models, enabling them to incorporate both local and network-level information to form behaviorally compatible platoon structures that stay stable in a dynamic traffic stream.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.