Intelligent back-looking distance driver model and stability analysis for connected and automated vehicles

Abstract

The connected and automated vehicles (CAVs) technologies provide more information to drivers in the car-following (CF) process. Unlike the human-driven vehicles (HVs), which only considers information in front, the CAVs circumstance allows them to obtain information in front and behind, enhancing vehicles perception ability. This paper proposes an intelligent back-looking distance driver model (IBDM) considering the desired distance of the following vehicle in homogeneous CAVs environment. Based on intelligent driver model (IDM), the IBDM integrates behind information of vehicles as a control term. The stability condition against a small perturbation is analyzed using linear stability theory in the homogeneous traffic flow. To validate the theoretical analysis, simulations are carried out on a single lane under the open boundary condition, and compared with the IDM not considering the following vehicle and the extended IDM considering the information of vehicle preceding and next preceding. Six scenarios are designed to evaluate the results under different disturbance strength, disturbance location, and initial platoon space distance. The results reveal that the IBDM has an advantage over IDM and the extended IDM in control of CAVs car-following process in maintaining string stability, and the stability improves by increasing the proportion of the new item.