We aim to find platoon structures that are behaviorally stable, and to develop trajectory planning algorithms to physically guide vehicles to form such platoons.
Connected vehicle (CV) technology enables communications among vehicles, infrastructure, and other road users. This connectivity facilitates forming vehicle platoons, where a platoon is defined as a single-file line (i.e., a virtual train) of vehicles traveling with small gaps between them. The benefits of vehicle platooning include improved energy efficiency, increased road capacity, and enhanced mobility. In order for such benefits to be realized, we need to ensure drivers’ or owners’ willingness to form and maintain a platoon. This is particularly critical for human-driven connected vehicles or privately-owned automated vehicles. Because vehicles in a platoon may benefit differently from platooning, e.g., the lead vehicle of the platoon may not save any energy at all, some drivers or owners will not be willing to join or stay in a platoon even if they are advised to do so.
This project aims to achieve optimal and behaviorally stable vehicle platooning. 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 since better opportunities for platoon membership may arise when new vehicles join or current platoon members leave. This project integrates stable platoon formation into 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.
Acknowledgments: National Science Foundation