Modeling AVs & RVs’ car-following behavior by considering impacts of multiple surrounding vehicles and driving characteristics

Abstract

This paper proposes a mixed-vehicles car-following model based on FVAD (Full Velocity and Acceleration Difference) model to describe the microscopic car-following behavior of RVs (Regular Vehicles) and AVs (Autonomous Vehicles). The model involves the velocity of multiple front vehicles and a rear vehicle as well as the velocity difference, acceleration difference and headway between each front vehicle and the host vehicle. As for AV’s car-following model we introduced the molecular dynamic theory to quantitatively express the influence of multiple front vehicles on the host vehicle. The velocity of multiple front vehicles and headway between each of them and the host vehicle are used to express the influence. Besides, we consider drivers’ car-following styles in constructing the RV model. The stability analysis results indicate that the stability of traffic flow under the proposed model is not easily affected by the change in velocity, and is better than FVAD model. According to the data collected from the car-following field test mixed with AVs and RVs, we obtain the optimal value of the parameters in the model, and examine the fitting accuracy with a numerical simulation. The results indicate that compared with FVAD model, the MME (Mean Maximum Error) and ME (Mean Error) of RV model is reduced by 39.50% and 13.12%, respectively, and the accuracy is improved by 14.48%. The acceleration strategy controlled by the AV model is smoother than by the ACC (Adaptive Cruise Control) model. With effective car-following behavior control, it is helpful to improve the operation efficiency of AVs and enhance the stability of traffic flow. Additionally, the model can be utilized for platoon control in the case of RVs’ and AVs’ heterogeneous flow. This model can also serve as a tool to simulate car-following behavior, which is beneficial for road traffic management and infrastructure layout in AV and RV mixed traffic environment.