Written by Ines Aviles-Spadoni, M.S., UFTI Research Coordinator

As self-driving vehicles (also known as connected and autonomous vehicles (CAVs) become increasingly common, it is vital that their movements are seamlessly coordinated so they can drive safely and efficiently on our highways and roads. Among those coordinated driving maneuvers such as car-following and lane change, vehicle platooning, a group of vehicles traveling in close proximity to one another, present promising benefits for enhancing driving safety and efficiency while reducing energy consumption.

A study led by Dr. Lili Du, a professor in the University of Florida’s Department of Civil and Coastal Engineering at the Herbert Wertheim College of Engineering, and her research team has developed two control algorithms respectively using Model Predictive Control (MPC) and Complex SMD (Spring Mass Damper) control, both intelligent control approaches of sorts, which show great potential in synchronizing the movements of two self-driving vehicles to form a short platoon, and then sequentially growing the platoon size as more CAVs join in and the traffic condition permits.

So, how can this new synchronization model be used? Imagine that, through this new model, CAV trucks would be able to sync up to reduce the amount of air resistance or aerodynamic drag, thus improving fuel efficiency by forming a platoon. Other potential benefits include reducing congestion on highways through the optimal synchronization of speeds and movements.

The researchers also foresee utilizing this technology for autonomous police patrolling, where two CAVs are deployed—one representing the police vehicle and the other the target vehicle. Separated in a mixed traffic flow alongside human-driven vehicles, the police vehicle would approach the target by synchronizing their movements efficiently, with the least disruption to surrounding traffic safety and efficiency.

When Dr. Du and her team reviewed the literature on synchronization models, they noticed that the research community has not yet been able to find a concrete way to coordinate CAVs in a manner that generates stability in a mixed-traffic environment. These two studies aimed to fill this gap in the literature.

So where is the innovation in this study? The innovation can be found in the MINLP-MPC method that Dr. Du and her team created, which considers certain factors such as traffic flow and coordination between CAVs. This is what would ensure that CAVs remain sync.

This study contributes significantly to the field of transportation engineering. In a future where CAVs will become more prevalent, ensuring they move safely and harmoniously is a number one priority.

Note: Dr. Du has also published another paper showing similar results but using different approaches. The work was funded by the Freight Mobility Research Institute (FMRI) at Florida Atlantic University. [Narasimhan, M., Du, L., Washburn, S., & F. (2024). Sequential Truck Platoon Formation in Mixed Traffic using multiple Spring Mass Damper Systems. The paper will be presented at the 2024 American Control Conference, Toronto, ON, Canada, July 10-12, 2024.]