Platoon or individual: An adaptive car-following control of connected and automated vehicles

With the rapid development of vehicle-to-everything communication and autonomous driving technology, research on connected and automated vehicles (CAVs) is experiencing significant growth. Multiple vehicles with different intelligence levels will coexist for the foreseeable future. This paper proposes an adaptive car-following control framework designed to dynamically form platoons or operate individually according to the traffic environment. The aim is to enhance platoon stability, improve efficiency and reduce emissions. Moreover, we consider the stochastic driving behaviors of human-driven vehicles and propose a transposition prediction method that predicts the reaction of rear vehicles to CAV velocity variations from the perspective of rear vehicles. The disturbance scenario and platoon reorganization scenario are designed to conduct comparative experiments with adaptive cruise control, cooperative adaptive cruise control, and distributed model predictive control. The experimental findings underscore the effectiveness of the proposed approach, showing its ability to swiftly and substantially mitigate the impacts of traffic disturbances while simultaneously reducing traffic emissions. Furthermore, the proposed prediction method is identified as a valuable asset for expediting the formation of CAV platoons and enhancing the stability of mixed traffic scenarios.