A Set-Theoretic Receding Horizon Control Based on a Q-Learning Approach for Sustainability Purposes

This paper presents a set-theoretic receding horizon control strategy for platoons of autonomous vehicles driving in smart cities context. In order to reduce traffic and
$CO_{2}$
emissions, we propose a path planer based on Deep Reinforcement Learning (DRL). The advantages of this solution is the ability to deal with the actual traffic congestion, while driving the autonomous vehicles to their destination and fulfilling the constraints. In particular, the high-level routing decisions are translated into set-points for the receding horizon controllers, making the control actions on the vehicle dynamics more computational efficient. In order to show the effectiveness of the overall architecture, a campaign of simulations on a platoon of eight vehicles, moving in the city center of Bologna in Italy, is provided.