AI-Driven Systems for Vehicle-to-Infrastructure Communication

Authors

  • Dr. Aisha Hassan Professor of Computer Science, University of Khartoum, Sudan Author

Keywords:

AI Driven Systems, Vehicle-to-Infrastructure, Communication

Abstract

In the 21st century, an efficient transportation system is key for gigacities, special economic zones, or places with a huge number of vehicles per unit of time. Our society is more dependent on transportation systems as we need to move from one place to another. As a result, there is a serious need for an efficient transportation system. A growing issue for modern vehicles on the roads needs to be handled properly. The vehicles need to be informed about less trafficked roads, avoiding more congested ones. In the modern era, the majority of the work has shifted towards intelligent structures. Artificial intelligence has played a vital role in making systems intelligent. Vehicle-to-Infrastructure communication is one of the key issues of the present century. This feature needs to be handled intelligently; supported by artificial intelligence, the dialogue is becoming smarter, as intelligent systems in modern scenarios offer advancements such as scalability, reliability, and computational cost efficiency to the limit and line of sight scenarios.

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References

Tamanampudi, Venkata Mohit. "Automating CI/CD Pipelines with Machine Learning Algorithms: Optimizing Build and Deployment Processes in DevOps Ecosystems." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 810-849.

Singh, Jaswinder. "Deepfakes: The Threat to Data Authenticity and Public Trust in the Age of AI-Driven Manipulation of Visual and Audio Content." Journal of AI-Assisted Scientific Discovery 2.1 (2022): 428-467.

Machireddy, Jeshwanth Reddy. "Revolutionizing Claims Processing in the Healthcare Industry: The Expanding Role of Automation and AI." Hong Kong Journal of AI and Medicine 2.1 (2022): 10-36.

S. Kumari, “Kanban-Driven Digital Transformation for Cloud-Based Platforms: Leveraging AI to Optimize Resource Allocation, Task Prioritization, and Workflow Automation”, J. of Artificial Int. Research and App., vol. 1, no. 1, pp. 568–586, Jan. 2021

Tamanampudi, Venkata Mohit. "AI and DevOps: Enhancing Pipeline Automation with Deep Learning Models for Predictive Resource Scaling and Fault Tolerance." Distributed Learning and Broad Applications in Scientific Research 7 (2021): 38-77.

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Published

08-11-2022

How to Cite

[1]
D. A. Hassan, “AI-Driven Systems for Vehicle-to-Infrastructure Communication”, J. of Artificial Int. Research and App., vol. 2, no. 2, pp. 529–544, Nov. 2022, Accessed: Nov. 22, 2024. [Online]. Available: https://aimlstudies.co.uk/index.php/jaira/article/view/274

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