Enhancing Vehicle-to-Everything (V2X) Communication with AI
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Keywords

Vehicle-to-Everything
V2X
Communication
Communication with AI

How to Cite

[1]
D. L. Wang, “Enhancing Vehicle-to-Everything (V2X) Communication with AI”, Journal of Bioinformatics and Artificial Intelligence, vol. 2, no. 2, pp. 57–69, Nov. 2022, Accessed: Nov. 15, 2024. [Online]. Available: https://biotechjournal.org/index.php/jbai/article/view/112

Abstract

Both research and industrial communities envision a future where vehicles become "active actors" in the transportation system rather than mere "cargo units." Vehicle-to-Everything (V2X) communications are fundamental to ensure such advanced services, which are built on top of vehicles' abilities to transmit and receive information. This new strand of vehicular communication, where every tangible and intangible entity can interact with vehicles, is referred to by the community as V2X (Vehicle-to-Everything). With V2X communications, vehicles can share any possible type of information they might have: measurements, intentions, requests, identifications, etc., with any possible entity, ranging from roadside units to nearby infrastructure, other vehicles, as well as authorities' servers. The V2X communication system is usually imagined from a network engineering standpoint concerning wave-based communications, multi-access edge technology communications, contextual data exchanges, and software services.

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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.

J. Singh, “Understanding Retrieval-Augmented Generation (RAG) Models in AI: A Deep Dive into the Fusion of Neural Networks and External Databases for Enhanced AI Performance”, J. of Art. Int. Research, vol. 2, no. 2, pp. 258–275, Jul. 2022

Machireddy, Jeshwanth Reddy. "Data-Driven Insights: Analyzing the Effects of Underutilized HRAs and HSAs on Healthcare Spending and Insurance Efficiency." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 450-470.

S. Kumari, “Kanban and AI for Efficient Digital Transformation: Optimizing Process Automation, Task Management, and Cross-Departmental Collaboration in Agile Enterprises”, Blockchain Tech. & Distributed Sys., vol. 1, no. 1, pp. 39–56, Mar. 2021

Tamanampudi, Venkata Mohit. "Natural Language Processing in DevOps Documentation: Streamlining Automation and Knowledge Management in Enterprise Systems." Journal of AI-Assisted Scientific Discovery 1.1 (2021): 146-185.

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