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Aerospace
Special Issues
Human Factors and Performance in Aviation Safety
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Human Factors and Performance in Aviation Safety

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Air Traffic and Transportation".

Deadline for manuscript submissions: 30 October 2026 | Viewed by 2291

Special Issue Editors

Dr. Douglas D. Boyd

E-Mail Website
Guest Editor
College of Aeronautics, Embry-Riddle Aeronautical University Worldwide, 1 Aerospace Blvd, Daytona Beach, FL 32114, USA
Interests: general aviation safety; aviation accident analysis; decision-making in aviation; general aviation flight safety; general aviation flight accidents
Special Issues, Collections and Topics in MDPI journals
Dr. Elizabeth L. Blickensderfer

E-Mail Website
Guest Editor
Human Factors and Behavioral Neurobiology, Embry-Riddle Aeronautical University Worldwide, 1 Aerospace Blvd, Daytona Beach, FL 32114, USA
Interests: human factors; automation; human performance; general aviation; human–machine interface; cognitive analysis; aviation weather

Special Issue Information

Dear Colleagues,

Civil aviation can be arbitrarily separated into three main categories, (i) airlines (air carriers), (ii) on-demand (e.g., air taxi, air tours, air medical) and (iii) general aviation (composed mainly of light aircraft flown for personal missions), based on specific operational regulations applicable to each. While air carrier transport is exceedingly safe, this is less apparent for on-demand and general aviation operations. Indeed the fatal accident rate for general aviation is 80-200-fold higher than that evident for air carriers [1]. That said, aviation accidents are rarely due to equipment failure [2]. Rather, multiple studies have documented that most aviation mishaps are a consequence of human performance deficits for personnel employed across the enterprise spectrum—pilots, air traffic control, maintenance technicians and the organizational/supervisory hierarchy. For example, poor pilot decision making (the continuation of a flight into adverse weather), situational pressures (financial or personal), the lack of cognitive engagement, complacency, ennui, elevated risk tolerance, excessive reliance on automation and a poor human–system interface [3–6] have all been cited as causal/contributory factors to aviation accidents over the last three decades. This Special Issue on human factors in aviation welcomes manuscripts addressing all aspects of human performance pertaining to safe aviation practices across the enterprise.

References

[1] A Review of General Aviation Safety (1984–2017). Aerosp Med Hum Perform 2017, 88, 657–664.

[2] Joseph T. Nall Report; General Aviation Accidents in 2021. 2024.

[3] Cross-country VFR crashes: pilot and contextual factors. Aviat Space Environ Med 2002, 73, 363–366.

[4] The Effectiveness of Airline Pilot Training for Abnormal Events. Human Factors 2013, 55, 475–485.

[5] Situational Pressures on Aviation Decision Making: Goal Seduction and Situation Aversion. Aviat Space Environ Med 2009, 80, 556–560.

[6] The Impact of Motivation on Continued VFR into IMC: Another Perspective to an On-Going Problem.  Collegiate Aviation Review International 2020, 38, 51–66.

Dr. Douglas D. Boyd
Dr. Elizabeth L. Blickensderfer
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • human factors
  • human performance
  • aviation safety
  • cognition
  • human–machine interface
  • automation
  • pilot training

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Published Papers (3 papers)

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Research

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22 pages, 8434 KB  
Article
Research on Pilot Workload Identification Based on EEG Time Domain and Frequency Domain
by Weiping Yang, Yixuan Li, Lingbo Liu, Haiqing Si, Haibo Wang, Ting Pan, Yan Zhao and Gen Li
Aerospace 2026, 13(2), 114; https://doi.org/10.3390/aerospace13020114 - 23 Jan 2026
Viewed by 564
Abstract
Pilot workload is a critical factor influencing flight safety. This study collects both subjective and objective data on pilot workload using the NASA-TLX questionnaire and electroencephalogram acquisition systems during simulated flight tasks. The raw EEG signals are denoised through preprocessing techniques, and relevant [...] Read more.
Pilot workload is a critical factor influencing flight safety. This study collects both subjective and objective data on pilot workload using the NASA-TLX questionnaire and electroencephalogram acquisition systems during simulated flight tasks. The raw EEG signals are denoised through preprocessing techniques, and relevant EEG features are extracted using time-domain and frequency-domain analysis methods. One-way ANOVA is employed to examine the statistical differences in EEG indicators under varying workload levels. A fusion model based on CNN-Bi-LSTM is developed to train and classify the extracted EEG features, enabling accurate identification of pilot workload states. The results demonstrate that the proposed hybrid model achieves a recognition accuracy of 98.2% on the test set, confirming its robustness. Additionally, under increased workload conditions, frequency-domain features outperform time-domain features in discriminative power. The model proposed in this study effectively recognizes pilot workload levels and offers valuable insights for civil aviation safety management and pilot training programs. Full article
(This article belongs to the Special Issue Human Factors and Performance in Aviation Safety)
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21 pages, 1065 KB  
Article
GC-ViT: Graph Convolution-Augmented Vision Transformer for Pilot G-LOC Detection Through AU Correlation Learning
by Bohuai Zhang, Zhenchi Xu and Xuan Li
Aerospace 2026, 13(1), 93; https://doi.org/10.3390/aerospace13010093 - 15 Jan 2026
Viewed by 353
Abstract
Prolonged +Gz acceleration during high-performance flight exposes pilots to the risk of G-induced loss of consciousness (G-LOC), a dangerous condition that compromises operational safety. To enable early detection without intrusive sensors, we present a vision-based warning system that analyzes facial action units (AUs) [...] Read more.
Prolonged +Gz acceleration during high-performance flight exposes pilots to the risk of G-induced loss of consciousness (G-LOC), a dangerous condition that compromises operational safety. To enable early detection without intrusive sensors, we present a vision-based warning system that analyzes facial action units (AUs) as physiological indicators of impending G-LOC. Our approach combines computer vision with physiological modeling to capture subtle facial microexpressions associated with cerebral hypoxia using widely available RGB cameras. We propose a novel Graph Convolution-Augmented Vision Transformer (GC-ViT) network architecture that effectively captures dynamic AU variations in pilots under G-LOC conditions by integrating global context modeling with vision Transformer. The proposed framework integrates a vision–semantics collaborative Transformer for robust AU feature extraction, where EfficientNet-based spatiotemporal modeling is enhanced by Transformer attention mechanisms to maintain recognition accuracy under high-G stress. Building upon this, we develop a graph-based physiological model that dynamically tracks interactions between critical AUs during G-LOC progression by learning the characteristic patterns of AU co-activation during centrifugal training. Experimental validation on centrifuge training datasets demonstrates strong performance, achieving an AUC-ROC of 0.898 and an AP score of 0.96, confirming the system’s ability to reliably identify characteristic patterns of AU co-activation during G-LOC events. Overall, this contact-free system offers an interpretable solution for rapid G-LOC detection, or as a complementary enhancement to existing aeromedical monitoring technologies. The non-invasive design demonstrates significant potential for improving safety in aerospace physiology applications without requiring modifications to current cockpit or centrifuge setups. Full article
(This article belongs to the Special Issue Human Factors and Performance in Aviation Safety)
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Review

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22 pages, 363 KB  
Review
Human Factors, Competencies, and System Interaction in Remotely Piloted Aircraft Systems
by John Murray and Graham Wild
Aerospace 2026, 13(1), 85; https://doi.org/10.3390/aerospace13010085 - 13 Jan 2026
Viewed by 880
Abstract
Research into Remotely Piloted Aircraft Systems (RPASs) has expanded rapidly, yet the competencies, knowledge, skills, and other attributes (KSaOs) required of RPAS pilots remain comparatively underexamined. This review consolidates existing studies addressing human performance, subject matter expertise, training practices, and accident causation to [...] Read more.
Research into Remotely Piloted Aircraft Systems (RPASs) has expanded rapidly, yet the competencies, knowledge, skills, and other attributes (KSaOs) required of RPAS pilots remain comparatively underexamined. This review consolidates existing studies addressing human performance, subject matter expertise, training practices, and accident causation to provide a comprehensive account of the KSaOs underpinning safe civilian and commercial drone operations. Prior research demonstrates that early work drew heavily on military contexts, which may not generalize to contemporary civilian operations characterized by smaller platforms, single-pilot tasks, and diverse industry applications. Studies employing subject matter experts highlight cognitive demands in areas such as situational awareness, workload management, planning, fatigue recognition, perceptual acuity, and decision-making. Accident analyses, predominantly using the human factors accident classification system and related taxonomies, show that skill errors and preconditions for unsafe acts are the most frequent contributors to RPAS occurrences, with limited evidence of higher-level latent organizational factors in civilian contexts. Emerging research emphasizes that RPAS pilots increasingly perform data-collection tasks integral to professional workflows, requiring competencies beyond aircraft handling alone. The review identifies significant gaps in training specificity, selection processes, and taxonomy suitability, indicating opportunities for future research to refine RPAS competency frameworks and support improved operational safety. Full article
(This article belongs to the Special Issue Human Factors and Performance in Aviation Safety)
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