A Sustainable Multi-Agent Routing Algorithm for Vehicle Platoons in Urban Networks

In this paper, a sustainable routing algorithm for vehicle platoons operating in smart urban networks is presented. The proposed approach makes use of deep reinforcement learning (DRL) and set-theoretic model predictive control (MPC). In particular, the learning process aims at reducing traffic congestion and
$CO_{2}$
emissions, whereas the MPC unit allows to adequately track the assigned path by using real-time traffic data. To adequately analyze the performance of the resulting control architecture, the SUMO and MATLAB environments are used to implement complex operating scenarios where road maps data and vehicle state trajectories can be shared and exchanged. Finally, numerical studies are provided by resorting to the SUMO environment and considering a platoon of five vehicles. The resulting simulation campaign puts in light the capability of the training process to significantly mitigate the
$CO_{2}$
emissions of the whole platoon: from a minimum of 3.7 % to a maximum of 13% with respect to the use of the well-known Dijkstra algorithm.