Vehicle-Following Control Based on Continuous Synthesis Variable Time Headway Model

Vehicle-following control is a crucial component of adaptive cruise control systems, playing a pivotal role in ensuring safe autonomous driving. Maintaining a reasonable distance between vehicles (DBV) is a prerequisite for safe vehicle following, with time headway (TH) being a key factor in ensuring this safe distance. To enhance the performance of vehicle following in diverse driving scenarios, this paper proposes a model-predictive-control-based (MPC) vehicle-following control scheme utilizing the continuous synthesis variable time headway (CSVTH) model. First, this paper introduces a novel CSVTH model based on existing TH models, and the sigmoid function is used as the transition function to realize the unique capability of seamlessly transitioning between different TH models according to the DBV, ensuring optimal performance in various driving scenarios. Second, the MPC algorithm based on incremental control is employed as the upper controller. By utilizing fuzzy reasoning to adjust the weight of the MPC algorithm, the adaptability of the controller to the different working conditions is enhanced. Finally, the lower controller is constructed within the simulation environment. The rationality of the proposed CSVTH model is verified through simulation experiments across three distinct driving conditions. Compared with the existing three TH models, the proposed CSVTH model improves economy by 22.7%, vehicle-following performance by 38.8%, and comfort by 15.3%.