Energy-Efficient Reactive and Predictive Connected Cruise Control

Abstract

Connected and automated vehicles (CAVs) have shown great potential in improving the energy efficiency of road transportation. Energy savings, however, greatly depends on driving behavior. Therefore, the controllers of CAVs must be carefully designed to fully leverage the benefits of connectivity and automation, especially if CAVs travel amongst other non-connected and human-driven vehicles. With this as motivation, we introduce a framework for the longitudinal control of CAVs traveling in mixed traffic including connected and non-connected human-driven vehicles. Reactive and predictive connected cruise control strategies are proposed. Reactive controllers are given by explicit feedback control laws. Predictive controllers, on the other hand, optimize the control input in a receding-horizon fashion, by predicting the motions of preceding vehicles. Beyond-line-of-sight information obtained via vehicle-to-vehicle (V2V) communication is leveraged by the proposed reactive and predictive controllers. Simulations utilizing real traffic data show that connectivity can bring up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$30\%$</tex-math></inline-formula> energy savings in certain scenarios.