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International Journal of Intelligent Transportation Systems Research

Erschienen in:

03.12.2022

Reinforcement Learning Based Control Scheme for Emergency Vehicle Preemption with Edge Computing

verfasst von: Prakash Rosayyan, Jasmine Paul, Senthilkumar Subramaniam, Saravana Ilango Ganesan

Erschienen in: International Journal of Intelligent Transportation Systems Research | Ausgabe 1/2023

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Abstract

This paper proposes a reinforcement learning-based collaborative multi-agent actor and critic scheme (RL-CMAS) under edge computing architecture for emergency vehicle preemption. The RL-CMAS deployed a parallel training process at the cloud side for building knowledge and well accelerating learning. Priority of message and model of message offloading strategy have been developed. The simulation results show that the proposed RL-CMAS is efficient in detecting even complex data. Finally, a comparison was made with other benchmark methods, namely, Regular scheduling algorithm, Alameddine’s DTOS algorithm, and independent multi-agent actor-critic. The result showed the proposed method outperforming the other three bench marking methods. The proposed RL-CMAS provides reduction in message processing delay, total delay, and an increase of message delivery success ratio of 14.22%, 18.21%, and 8.86% respectively.

Vorheriger Artikel Selecting the Representative Travel Time Reliability Measure Based on Metric (Dis)Agreement Patterns

Nächster Artikel Problems Seen from the Awareness on the Introduction and Operation of the Bus Location Systems and Examples of Problem Solving Through the Use of Data

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Titel: Reinforcement Learning Based Control Scheme for Emergency Vehicle Preemption with Edge Computing
verfasst von: Prakash Rosayyan
Jasmine Paul
Senthilkumar Subramaniam
Saravana Ilango Ganesan
Publikationsdatum: 03.12.2022
Verlag: Springer US
Erschienen in: International Journal of Intelligent Transportation Systems Research / Ausgabe 1/2023
Print ISSN: 1348-8503
Elektronische ISSN: 1868-8659
DOI: https://doi.org/10.1007/s13177-022-00334-0

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