Connected vehicle following control based on gated recurrent unit with attention mechanism.

A delicate balance between safety, efficiency, and fluidity needs to be carefully maintained in vehicle following, in strict accordance with real-time control imperatives. Achieving efficient vehicle-following operations under safe driving conditions, through smooth behavioral adjustments, presents a significant challenge for data-driven vehicle-following models. In response to this challenge, we have developed a deep neural network based on gated recurrent unit (GRU) with attention mechanism, named AGRUNet model, for artificial intelligence (AI) control of vehicle following behavior. Through training and testing on diverse datasets, the AGRUNet model not only establishes a nonlinear mapping relationship between the following vehicle's acceleration and the speeds of the leading and following vehicles, their distance, and the control strategy of the leading vehicles but also accurately forecasts the future behaviors of following vehicles in complex vehicle-following scenarios in real-time. This capability enables the following vehicle to optimize its behavior based on the current vehicle-following situation and control requirements, thereby improving safety, efficiency, and smoothness. Rigorous simulations of AGRUNet on the Highway Drone(HighD), Next Generation Simulation(NGSIM), Waymo, and Lyft Level 5(Lyft) datasets demonstrate its superior performance in prediction accuracy and vehicle-following control. Compared to the widely adopted, high-performance Long Short-Term Memory (LSTM) model, AGRUNet achieves prediction accuracy gains of approximately 2%, 7%, 22%, and 3% across these datasets. Extensive testing further indicates that AGRUNet significantly reduces collision rates during sudden emergency braking by the leading vehicle, enhancing safety, and improving the efficiency and smoothness of behavior adjustments, all while ensuring vehicle-following safety. • An AGRUNet model is developed to optimize vehicle-following control under complex scenarios. • The model integrates attention mechanism and GRU to capture crucial features for vehicle following. • Extensive evaluations on public driving datasets demonstrate superior performance. • Improving vehicle-following behavior in terms of safety, efficiency, and smoothness. [ABSTRACT FROM AUTHOR]