REVIEW ARTICLE
Navigating the Future of Secure and Efficient Intelligent Transportation Systems using AI and Blockchain
Jyotsna Ghildiyal Bijalwan1, Jagendra Singh2, Vinayakumar Ravi6, *, Anchit Bijalwan3, Tahani Jaser Alahmadi7, *, Prabhishek Singh2, Manoj Diwakar4, 5
Article Information
Identifiers and Pagination:
Year: 2024Volume: 18
E-location ID: e26671212291400
Publisher ID: e26671212291400
DOI: 10.2174/0126671212291400240315084722
Article History:
Received Date: 24/12/2023Revision Received Date: 18/02/2024
Acceptance Date: 21/02/2024
Electronic publication date: 28/3/2024
Collection year: 2024
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Introduction/Background
This study explores the limitations of conventional encryption in real-world communications due to resource constraints. Additionally, it delves into the integration of Deep Reinforcement Learning (DRL) in autonomous cars for trajectory management within Connected And Autonomous Vehicles (CAVs).
This study unveils the resource-constrained real-world communications, conventional encryption faces challenges that hinder its feasibility. This introduction sets the stage for exploring the integration of DRL in autonomous cars and the transformative potential of Blockchain technology in ensuring secure data transfer, especially within the dynamic landscape of the transportation industry.
Materials and Methods
The research methodology involves implementing DRL techniques for autonomous car trajectory management within the context of connected and autonomous CAVs. Additionally, a detailed exploration of Blockchain technology deployment, consensus procedures, and decentralized data storage mechanisms.
Results
Results showcase the impracticality of conventional encryption in resource-constrained real-world communications. Moreover, the implementation of DRL and Blockchain technology proves effective in optimizing autonomous car subsystems, reducing training costs, and establishing secure, globally accessible government-managed transportation for enhanced data integrity and accessibility.
Discussion
The discussion delves into the implications of the study's findings, emphasizing the transformative potential of DRL in optimizing autonomous car subsystems. Furthermore, it explores the broader implications of Blockchain technology in revolutionizing secure, decentralized data transfer within the transportation industry.
Conclusion
In conclusion, the study highlights the impracticality of conventional encryption in real-world communications and underscores the significant advancements facilitated by DRL in autonomous vehicle trajectory management. The integration of Blockchain technology not only ensures secure data transfer but also paves the way for a globally accessible transportation blockchain, reshaping the future landscape of the industry.