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Research Article

Ultra-Low Latency V2X Systems with AI-Driven Resource Optimization

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  • @ARTICLE{10.4108/eetsc.8366,
        author={Milad Rahmati},
        title={Ultra-Low Latency V2X Systems with AI-Driven Resource Optimization},
        journal={EAI Endorsed Transactions on Smart Cities},
        volume={8},
        number={1},
        publisher={EAI},
        journal_a={SC},
        year={2025},
        month={11},
        keywords={Autonomous Vehicles, V2X Communication, Ultra-Low Latency, Artificial Intelligence, Resource Optimization, Edge Computing, 5G Networks, Smart Cities},
        doi={10.4108/eetsc.8366}
    }
    
  • Milad Rahmati
    Year: 2025
    Ultra-Low Latency V2X Systems with AI-Driven Resource Optimization
    SC
    EAI
    DOI: 10.4108/eetsc.8366
Milad Rahmati1,*
  • 1: Independent Researcher, Los Angeles, California
*Contact email: mrahmat3@uwo.ca

Abstract

Achieving ultra-low latency in Vehicle-to-Everything (V2X) communication is essential for ensuring the safety and effectiveness of autonomous vehicles (AVs). However, existing systems often struggle to meet the stringent latency demands, particularly in complex and rapidly changing urban environments. This study introduces an innovative framework that utilizes artificial intelligence (AI) for dynamic resource allocation in V2X networks. By integrating real-time data analysis, edge computing, and 5G capabilities, the proposed approach effectively minimizes latency. Simulation results indicate up to a 35% reduction in latency compared to conventional models, underscoring the potential of AI in enhancing the responsiveness and reliability of V2X systems. These findings offer a significant step toward making autonomous vehicle deployments more viable in smart cities.

Keywords
Autonomous Vehicles, V2X Communication, Ultra-Low Latency, Artificial Intelligence, Resource Optimization, Edge Computing, 5G Networks, Smart Cities
Received
2025-01-04
Accepted
2025-03-05
Published
2025-11-18
Publisher
EAI
http://dx.doi.org/10.4108/eetsc.8366

Copyright © 2025 Milad Rahmati, licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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