Application of Artificial Intelligence, Machine Learning and Deep Learning in Piloted Aircraft Operations: Systematic Review

Abstract: Aviation research on artificial intelligence (AI), machine learning (ML), and deep learning (DL) has seen significant growth as these emerging technologies hold immense potential for supporting both human-centred and technology-centred aspects of civil aircraft operations. This systematic review, following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020, was registered on the Open Science Framework (DOI 10.17605/OSF.IO/ZR7A3) and focused specifically on the use of AI, ML, and DL in human-centric flight operations. The review conducted a comprehensive search of databases including Scopus, Web of Science, IEEE Xplore, as well as online repositories (ResearchGate and Aerospace Research Central) to identify relevant articles published between 2013 and 2023. In total, 32 studies were included, which explored various applications of AI, ML, and DL in aircraft pilots and flight operations. The studies were categorized into four main areas: (i) assessment and management of human factors risks, including AI-assisted data analysis of pilot performance, crew resource management, and ML-based support for pilots’ cognitive workload monitoring, (ii) detection of human errors, with support systems based on ML-based approaches for real-time monitoring and DL models for biometric monitoring of cockpit pilots were identified for the detection of human errors in flight safety, (iii) reduction and prediction of human errors, categorized into AI-assisted predictive analytics in flight accidents, and ML-based pattern recognition to predict unstable approaches, and (iv) prevention of human errors in aviation through ML utilization for pilot training enhancement, and AI-supporting flight automation and decision support systems for flight operation. Analysis of the included studies revealed a rising trend in the publication of articles after 2020, albeit at a slow rate. It is worth noting that the majority of studies focused on conceptual applications, with fewer studies involving empirical testing. The findings of this review highlight the potential for future research in developing and testing improved human factors risk assessment (HRA) models assisted by computational intelligence in piloted aircraft operations, with the ultimate aim of enhancing flight safety.

Keywords: Artificial intelligence, Machine learning, Deep learning, Flight operation, Aircraft pilot, Human errors, Human factors

DOI: 10.54941/ahfe1004666

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