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

该项目的目标是探索智能互联社区中大规模的车辆排队化。方法是开发技术和模型，为车辆加入排并保持其排成员身份提供激励。互联车辆技术有助于形成车辆排(车辆的虚拟列车，它们之间的间隔很小)，其好处包括提高能源效率、增加道路容量和增强移动性。排中的车辆可能会根据它们在虚拟列车中的位置而不同地受益(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，或者在出现更好的排编队机会时留在一个排里。这个项目探索如何形成和控制具有这些相互竞争的目标的排，以便了解如何激励车辆参与其中。MCity试验床是研究工作验证的一部分，计划举办有当地高中生和大学生参加的研讨会。一个稳定的排结构不包含任何可以通过交换排成员身份来增加个人公用事业的车辆联盟。考虑到交通流的动态性质，形成和保持稳定的排结构是一项复杂的任务，需要在排形成时考虑到当地和网络层面的情况。该建议引入了一个通用框架，该框架增强了基于最优控制的轨迹规划模型，使其也能够考虑网络级别的交通状况。这项建议进一步将稳定的排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别的信息，形成行为兼容的排结构，在动态的交通流中保持稳定。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.