Vol. 2 No. 2 (2022): African Journal of Artificial Intelligence and Sustainable Development
Articles

AI-Driven Systems for Improving Autonomous Vehicle Adaptability

Dr. Evelyn Cruz
Associate Professor of Electrical Engineering, University of Puerto Rico at Mayagüez
Cover

Published 15-11-2022

Keywords

  • Vehicle Adaptability,
  • Autonomous

How to Cite

[1]
D. E. Cruz, “AI-Driven Systems for Improving Autonomous Vehicle Adaptability”, African J. of Artificial Int. and Sust. Dev., vol. 2, no. 2, pp. 282–297, Nov. 2022, Accessed: Nov. 14, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/188

Abstract

Current interest in and research attention to artificial intelligence (AI)-driven systems has been increasing, especially as applied to autonomous vehicles. The demand for vehicles built based on AI technology has also been growing. The adaptability of autonomous vehicles is indispensable for improving vehicle performance and safety. To meet the requirements for intelligent adaptability, an accurate understanding of autonomous vehicles, with the help of intelligent systems, should be incorporated. This will also increase public confidence in autonomous vehicles. Semi-autonomous and autonomous vehicles are being developed and will be in commercial operation soon. This development is not possible without the use of AI to enhance their technical capabilities.

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References

  1. 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.
  2. 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.
  3. 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.
  4. 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
  5. 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.