Platoon agglomeration strategy and analysis in CAV dedicated lanes under low CAV penetration

Abstract

Platoon agglomeration is a key research focus aimed at enhancing road traffic efficiency and safety for Connected Human-driven Vehicles (CHVs) and Connected and Autonomous Vehicles (CAVs) in mixed traffic scenarios. Given the low utilization of CAV Dedicated Lanes (CDLs) caused by platoon agglomeration under low CAV penetration rates, coupled with challenges in ensuring safe and efficient vehicle operations, vehicle control methods for various scenarios were comprehensively analyzed, leading to the proposal of a Lane Level Mixed Agglomeration (LLMA) strategy. This strategy can select CAVs and CHVs that meet the agglomeration conditions to enter the CDL based on the designed vehicle agglomeration algorithm. Additionally, to accurately capture the driving characteristics of CHVs within the CDL under the LLMA strategy, a CHV molecular force field model was designed. This model incorporates a speed coordination term accounting for V2V real-time information and driver subjective perception, building upon the traditional molecular force field model. The results indicate that the LLMA strategy significantly enhances CDL utilization at low CAV penetration rates, increases road capacity and average vehicle speed, and reduces travel risk. This study offers theoretical insights for enhancing traffic efficiency and safety in CDL scenarios and plays a crucial role in advancing the practical implementation of connected autonomous driving technologies in future mixed traffic conditions.