Machine learning and mixed reality for smart aviation: Applications and challenges

Abstract

The aviation industry is a dynamic and ever-evolving sector. As technology advances and becomes more sophisticated, the aviation industry must keep up with the changing trends. While some airlines have made investments in machine learning and mixed reality technologies, the vast majority of regional airlines continue to rely on inefficient strategies and lack digital applications. This paper investigates the state-of-the-art applications that integrate machine learning and mixed reality into the aviation industry. Smart aerospace engineering design, manufacturing, testing, and services are being explored to increase operator productivity. Autonomous systems, self-service systems, and data visualization systems are being researched to enhance passenger experience. This paper investigate safety, environmental, technological, cost, security, capacity, and regulatory challenges of smart aviation, as well as potential solutions to ensure future quality, reliability, and efficiency.

Publication DOI: https://doi.org/10.1016/j.jairtraman.2023.102437
Divisions: College of Business and Social Sciences
College of Business and Social Sciences > Aston Business School
Aston University (General)
Additional Information: Crown Copyright © 2023 Published by Elsevier Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).
Uncontrolled Keywords: Aerospace engineering,Artificial intelligence,Intelligent aviation,Machine learning,Mixed reality,Passenger experience,Smart aviation,Transportation,Strategy and Management,Management, Monitoring, Policy and Law,Law
Publication ISSN: 0969-6997
Last Modified: 25 Apr 2025 07:12
Date Deposited: 24 Apr 2025 13:37
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.sci ... 0807?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2023-08
Published Online Date: 2023-06-04
Accepted Date: 2023-05-14
Authors: Jiang, Yirui
Tran, Trung Hieu (ORCID Profile 0000-0002-3989-4502)
Williams, Leon

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