AIM5LA: A latency-aware deep reinforcement learning-based autonomous intersection management system for 5G communication networks
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This work was supported by the Grant PID2020-116329GB-C22 funded by MCIN/AEI/10.13039 /501100011033 and 20740/FPI/18 (Fundación Séneca, Región de Murcia, Spain).Fecha de publicación
2022-03-13Editorial
MDPICita bibliográfica
Antonio, G.-P.; Maria-Dolores, C. AIM5LA: A Latency-Aware Deep Reinforcement Learning-Based Autonomous Intersection Management System for 5G Communication Networks. Sensors 2022, 22, 2217. https://doi.org/10.3390/s22062217Revisión por pares
siPalabras clave
5G latency forecasting5G vehicular communication
Autonomous intersection management
Autonomous vehicles
Connected autonomous vehicles
Cooperative autonomous driving
Intelligent transport systems
Intersection traffic management
Latency-aware driving
Unsignalized intersection
Resumen
The future of Autonomous Vehicles (AVs) will experience a breakthrough when collective
intelligence is employed through decentralized cooperative systems. A system capable of controlling
all AVs crossing urban intersections, considering the state of all vehicles and users, will be able to
improve vehicular flow and end accidents. This type of system is known as Autonomous Intersection
Management (AIM). AIM has been discussed in different articles, but most of them have not considered the communication latency between the AV and the Intersection Manager (IM). Due to the
lack of works studying the impact that the communication network can have on the decentralized
control of AVs by AIMs, this paper presents a novel latency-aware deep reinforcement learning-based
AIM for the 5G communication network, called AIM5LA. AIM5LA is the first AIM that considers the
inherent latency of the 5G communication network to adapt the control of AVs using Multi-Agent
Deep Reinforcement Learning ...
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