A Mesoscopic Human-Inspired Adaptive Cruise Control for Eco-Driving

Interconnected and autonomous vehicles are proven to be helpful in reducing traffic congestion and dangerous emissions while enhancing safety on our roads. In this context, the present paper introduces a human-inspired Adaptive Cruise Control dedicated to improving the passenger experience using Model Predictive Control and traffic macroscopic information. To better describe the characteristics of human drivers, first a human-inspired microscopic hybrid automaton is considered, and an optimization problem targeting consumption minimization and collision avoidance is designed with a receding horizon approach. Then, traffic macroscopic information is included in the controller definition so that a mesoscopic Adaptive Cruise Control model is obtained. Simulations showing the efficacy of the proposed approaches for safety and eco-driving are provided.