A Study of Mixed Platooning Considering Driver Perceptual Uncertainty

Human-driven vehicles (HDVs) are the most critical element in mixed platoon research for their uncertainty. This paper presents a novel car-following model that considers the driver’s perceived uncertainty. A mixed platoon model of HDVs and connected and autonomous vehicles (CAVs) is established. Through data analysis, the stability of this model is validated. Additionally, a meticulous comparison and analysis regarding the platoon convergence ability and stable state under various platoon grouping forms and running speeds are carried out. Further, this paper introduces a virtual spring strategy to describe the car-following relationship between mixed platoon vehicles. Numerical simulations are then employed to explore the anti-interference capabilities of different mixed platoon modes and lengths. The results indicate that CAVs can effectively attenuate the randomness of HDVs. The platoon formation and operating speed impact the stability of mixed vehicle platoons. The platooning configuration “1 + n + 1” as the smallest platooning unit can help mixed vehicle platoons achieve a better stable state more quickly, with the optimal platoon length being four vehicles. However, as the platoon combinations grow more complex, the optimal platooning unit tends to shorten.