Published 22-11-2022
Keywords
- Vehicle-to-Vehicle (V2V) Communication,
- V2V,
- Communication
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
Abstract
Vehicles in motion produce a vast volume of highly diverse data, valuable for many applications, from traffic and asset management to advanced vehicle diagnostics. This data explosion can now be assimilated and coalesced in real or near-real time, using state-of-the-art communication devices and infrastructure. The cornerstone of the integration of Comech for tomorrow's vehicular communications is the paradigm of vehicle-to-all communications. To this end, one specific type of interaction would be that between vehicles, for vehicle-to-vehicle communication. This embraces all types of communication just among and between vehicles. For instance, a vehicle may send and receive status information from other vehicles, such as their speeds as determined from roadside speed-activated data messages, and use them for one or more purposes, such as tracking.
Downloads
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.