Multi-Vehicle Platooning Strategy for Ramp Merging in Mixed Traffic Environments

Nowadays, on-ramp area is one of the bottlenecks of traffic congestion. With the advancement of intelligent connected technology, it is important to study how to improve traffic efficiency when ensuring the safety of connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs) in ramp merge zones. Especially under high traffic flow conditions, HDVs often fail to yield to each other, leading to frequent traffic congestion and gridlock. To address these issues, this paper proposes a new multi-vehicle platooning strategy and designs a bilevel trajectory planning framework to ensure the safe merging of vehicles and improve traffic efficiency in mixed traffic environments. Additionally, the framework further enhances the computational efficiency of vehicle trajectory planning by using a multi-step increasingly approximating algorithm to transform the non-linear trajectory planning model into a linear model for solving. The multi-vehicle platooning strategy was tested through a series of experiments under different traffic states and market penetration rates (MPRs) of CAVs. The results demonstrated that the proposed strategy significantly reduced model-solving time and enhanced traffic efficiency compared to scenarios where vehicles do not form platoons.