Longitudinal car-following control strategy integrating predictive collision risk

Abstract

Many controllers have been designed for the longitudinal car-following mode of connected and automated vehicles. The existing body of research shows that these controllers more or less have some deficiencies in controlling connected and automated vehicles, which directly hinder their development. To cope with these problems, we design a novel longitudinal car-following control strategy integrating predictive collision risk. Among the controllers for connected and automated vehicles, the intelligent driver model is one of the most widely adopted controller. There is evidence that the intelligent driver model controller will produce larger gap distance and slower response time. In the meantime, the string stability of CAVs platoon controlled by the intelligent driver model controller is poor. Therefore, to verify the effectiveness of this control strategy, we integrate proposed control strategy with intelligent driver model, and the predictive-intelligent drive model controller is formulated. Theoretical analysis is exerted by transfer function method, and some numerical simulation experiments are designed and carried out. Analytical results show that the proposed control strategy can improve the defects of intelligent driver model controller in poor string stability, larger gap distance and slower response. In the meantime, it can also moderate the stopping process of connected and automated vehicles and enhance the throughput of intersection. More importantly, it does not affect the structure of connected and automated vehicles, which suggests that it is possible to integrate it with other controllers to improve their performance in controlling connected and automated vehicles. All findings in this study provide a novel way to establish car-following control strategy improving traffic stability and efficiency and a set of theoretical frameworks for verifying the control effect of future proposed control strategy.