Car following trajectory planning of CAVs: An improved APF model with considering the stochasticity of HDVs

Abstract

This paper considered the stochasticity of Human-Driven Vehicles (HDVs) and proposed an improved Artificial Potential Field (APF) method for car-following trajectory planning of Connected and automated vehicles (CAVs) based on real mixed traffic flow experiment. Firstly, the heterogeneity between HDVs and CAVs was considered to determine the type of attractive field. Then to adapt the APF model to dynamic traffic environments, the field functions were improved by incorporating the speed and position differences. In addition, taking into account both the vehicle itself and its impact on traffic flow, Grey Wolf Optimization-Chaos (GWO-C) was proposed to calibrate parameters, which helps avoid local optima. Furthermore, the proposed model was compared with experimental data and original APF method. The results show that the proposed APF improves the speed, jerk, and speed standard deviation of the platoon. Finally, the impact of different CAVs’ market penetration rates (MPR) on the stability and fundamental diagram were explored. It was found that traffic stability and capacity can be enhanced by CAVs, with a more significant impact at higher MPR.