@inproceedings{a9fa820c343d40628393dc338987d7b2,
title = "Human Factors in the Age of Autonomous UAVs: Impact of Artificial Intelligence on Operator Performance and Safety",
abstract = "This research reviews the current literature on the impact of Artificial Intelligence (AI) in the operation of autonomous Unmanned Aerial Vehicles (UAVs). This paper examines three key aspects in developing the future of Unmanned Aircraft Systems (UAS) and UAV operations: (i) design, (ii) human factors, and (iii) operation process. The use of widely accepted frameworks such as the {"}Human Factors Analysis and Classification System (HFACS){"} and {"}Observe– Orient–Decide–Act (OODA){"} loops are discussed. The comprehensive review of this research found that as autonomy increases, operator cognitive workload decreases and situation awareness improves, but also found a corresponding decline in operator vigilance and an increase in trust in the AI system. These results provide valuable insights and opportunities for improving the safety and efficiency of autonomous UAVs in the future and suggest the need to include human factors in the development process.",
keywords = "AI, HFACS, Human factor, OODA, Safety, UAV, Industries, Analytical models, Bibliographies, Collaboration, Human factors, Autonomous aerial vehicles",
author = "Omar Alharasees and Adali, {Osama H.} and Utku Kale",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Unmanned Aircraft Systems, ICUAS 2023 ; Conference date: 06-06-2023 Through 09-06-2023",
year = "2023",
doi = "10.1109/ICUAS57906.2023.10156037",
language = "English",
isbn = "9798350310382",
series = "2023 International Conference on Unmanned Aircraft Systems, ICUAS 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "798--805",
booktitle = "2023 International Conference on Unmanned Aircraft Systems, ICUAS 2023",
address = "United States",
}