In recent years, the growing development of Connected Autonomous Vehicles (CAV), Intelligent Transport Systems
(ITS), and 5G communication networks have led to the advent
of Autonomous Intersection Management (AIM) systems. AIMs
present a new paradigm for CAV control in future cities, taking
control of CAVs in scenarios where cooperation is necessary and
allowing safe and efficient traffic flows, eliminating traffic signals.
So far, the development of AIM algorithms has been based on
basic control algorithms, without the ability to adapt or keep
learning new situations. To solve this, in this paper we present a new
advanced AIM approach based on end-to-end Multi-Agent Deep
Reinforcement Learning (MADRL) and trained using Curriculum
through Self-Play, called advanced Reinforced AIM (adv.RAIM).
adv.RAIM enables the control of CAVs at intersections in a collaborative way, autonomously learning complex real-life traffic
dynamics. In addition, adv.RAIM provides a new way to build
smarter AIMs capable of proactively controlling CAVs in other
highly complex scenarios. Results show remarkable improvements
when compared to traffic light control techniques (reducing travel
time by 59% or reducing time lost due to congestion by 95%), as well
as outperforming other recently proposed AIMs (reducing waiting
time by 56%), highlighting the advantages of using MADRL.This work was supported in part by MCIN/AEI/10.13039/501100011033 under Grant PID2020-116329GB-C22, and in part by the Fundación Séneca, Región de Murcia, Spain under Grant 20740/FPI/18
