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大规模探索车辆排队。该方法是开发技术和模型，为车辆加入排和保持排成员资格提供激励。互联车辆技术有助于形成车辆队列（虚拟车辆列车，它们之间有很小的间隙），其好处包括提高能源效率，增加道路容量和增强移动性。队列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好处（例如，与中间的车辆相比，领先的车辆节省的能量较少）。因此，一些车辆可能不愿意加入队列，或者在队列形成的更好机会出现时留在队列中。这个项目探讨了如何形成和控制具有这些竞争目标的车队，以了解如何激励车辆参与其中。Mcity试验台是验证研究工作的一部分，计划举办有当地高中和大学生参加的讲习班。一个稳定的车队结构不包含任何可以通过交换车队成员资格来增加其个人效用的车辆联盟。考虑到交通流的动态特性，形成和保持稳定的队列结构是一项复杂的任务，并且需要在队列形成时考虑本地和网络级条件。该建议引入了一个通用框架，通过使基于最优控制的轨迹规划模型能够考虑网络级交通状况来增强该模型。该提案进一步将稳定的队列编队整合到增强的轨迹规划模型中，使它们能够结合本地和网络级信息，以形成在动态交通流中保持稳定的行为兼容的队列结构。该奖项反映了NSF的法定使命，并通过使用基金会的智力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.