Internet connected vehicle platoon system modeling and linear stability analysis

Abstract The fast development of communication technique builds an era of Internet of Everything. In the automobile world, Internet-Connected Vehicle (CV), also called IntelliDrive, will accelerate the autonomous driving era’s coming. And the traffic flow theory will undergo major changes as CV platoon becomes common way for our travel. As we know that the CV has different driving decision mechanisms from human-driven vehicles, such as an CV can receive more information and make much faster decision than a human-driven vehicle. Only if we fully understand the autonomous traffic flow’s characteristics, then we can improve the level of traffic engineering study and management in the future. On the thought of building autonomous traffic flow theory, we present CV platoon car-following model as the theoretical microscopic traffic flow model. The model is described by a mathematical differential-difference equation, which has a synthesized optimal velocity differential function and a full platoon with weighted relative velocity difference function. There are three main factors in the model, which are the optimal velocity difference impact factor p and action related vehicle number l , and weighted velocity difference sum coefficient λ . The model reflects the effects of front interactions between every two adjacent CV instead of the weighted average headway in the platoon. We deduce the model’s stability conditions with linear system stability theory and do some computer numerical simulations to verify our suppositions. Simulation results show that the more information broadcasting among CV, the more stable of the traffic flow system is. At last, we discuss our study’s contributions from the viewpoint of theoretical and practical value, and point out future research directions. In a word, the highlight of the paper is that we find the CV platoon system’s running mechanism by building an CV platoon car-following model under connected environment and give its linear stability condition, which is great value for advanced traffic management in autonomous driving era.