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

Date

2023-06-04

Supervisor/s

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier

Department

Type

Article

ISSN

0969-6997

Format

Free to read from

Citation

Jiang Y, Tran TH, Williams L. (2023) Machine learning and mixed reality for smart aviation: applications and challenges. Journal of Air Transport Management, Volume 111, August 2023, Article number 102437

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.

Description

Software Description

Software Language

Github

Keywords

Aerospace engineering, Artificial intelligence;, Intelligent aviation, Machine learning, Mixed reality, Passenger experience, Smart aviation

DOI

Rights

Attribution 4.0 International

Relationships

Relationships

Supplements

Funder/s