Modelling and controlling of car-following behavior in real traffic flow using ARMAX identification and Model Predictive Control

Nowadays, car following models, as the most popular microscopic traffic flow modeling, are increasingly being used by transportation experts to evaluate new Intelligent Transportation System (ITS) and Advanced Driver Assistance Systems (ADAS) applications. The control of car following is essential due to its safety and its operational efficiency. For this purpose, this paper builds a model of car following behavior based on ARMAX structure from a real traffic data set and presents a Model Predictive Control (MPC) controller. An important advantage of this type of control is its ability to cope with constraints on controls. Since safety and operational efficiency are constraints for car following, therefore we have recruited this type of controller in this study to deal with these constraints. Based on the relative distance and relative acceleration of each instant, the MPC predicts the future behavior of the leader vehicle (LV) and according to this behavior, the acceleration of the follower vehicle (FV) is controlled. The MPC tries to control this acceleration in a way to keep the relative distance at a safe region. To investigate the performance of the designed controller, the result of the system is compared with the behavior of human drivers with similar initial conditions. Also, some other test performances were accomplished to investigate other features such as robustness and the stability of the designed MPC. The simulation results show that the MPC controller has a behavior much safer than that of real drivers and it can provide a pleasant trip for passengers.