Evaluating AI-UAV Systems: A Combined Approach with Operator Group Comparison

Alharasees, Omar; Abdalla, Muftah S.M.; Kale, Utku

doi:10.1109/hora58378.2023.10156755

Cited by 4 publications

References 17 publications

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Human Factors and AI in UAV Systems: Enhancing Operational Efficiency Through AHP and Real-Time Physiological Monitoring

Alharasees,

Kale

2024

J Intell Robot Syst

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Integrating Artificial Intelligence (AI) into Unmanned Aerial Vehicle (UAV) operations has advanced efficiency, safety, and decision-making. This study addresses critical gaps in UAV methods, including insufficient integration of human factors, operator variability, and the lack of systematic error analysis. To overcome these challenges, a novel approach combines the Analytic Hierarchy Process (AHP) with three core human factors models: the Observe-Orient-Decide-Act (OODA) loop, the Human Factors Analysis and Classification System (HFACS), and the SHELL model. An online survey was conducted across diverse UAV operator groups to prioritize critical factors within each model. Additionally, real-time monitoring of heart rate (HR), heart rate variability (HRV), and respiratory rate (RR) was conducted during UAV operations at various automation levels with different experience levels. Visualization through boxplots and percentage change matrices provided insights into operator stress and workload across automation levels. Integrating AHP findings and physiological data revealed significant differences in operator prioritization, highlighting the need for tailored AI-UAV strategies. This research combines survey data with real-time physiological monitoring, offering visions into optimizing human-AI interaction in UAV operations and providing a foundation for improving AI integration and operator strategies.

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Human Factors and AI in UAV Systems: Enhancing Operational Efficiency Through AHP and Real-Time Physiological Monitoring

Alharasees,

Kale

2024

J Intell Robot Syst

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Comprehensive Analysis of Advanced Display and Control Systems for eVTOL Aircraft

Alharasees,

Zolfaghari,

Kale

2024

2024 6th Global Power, Energy and Communication Conference (GPECOM)

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An Explainable Data-Driven Optimization Method for Unmanned Autonomous System Performance Assessment

Yi,

Zhang,

Wang

et al. 2024

Electronics

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Unmanned autonomous systems (UASs), including drones and robotics, are widely employed across various fields. Despite significant advances in AI-enhanced intelligent systems, there remains a notable deficiency in the interpretability and comprehensive quantitative evaluation of these systems. The existing literature has primarily focused on constructing evaluation frameworks and methods, but has often overlooked the rationality and reliability of these methods. To address these challenges, this paper proposes an innovative optimization evaluation method for data-driven unmanned autonomous systems. By optimizing the weights of existing indicators based on data distribution characteristics, this method enhances the stability and reliability of assessment outcomes. Furthermore, interpretability techniques such as Local Interpretable Model-agnostic Explanations (LIMEs) and Partial Dependence Plots (PDPs) were employed to verify the effectiveness of the designed evaluation indicators, thereby ensuring the robustness of the evaluation system. The experimental results validated the effectiveness of the proposed approach.

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Evaluating AI-UAV Systems: A Combined Approach with Operator Group Comparison

Cited by 4 publications

References 17 publications

Human Factors and AI in UAV Systems: Enhancing Operational Efficiency Through AHP and Real-Time Physiological Monitoring

Human Factors and AI in UAV Systems: Enhancing Operational Efficiency Through AHP and Real-Time Physiological Monitoring

Comprehensive Analysis of Advanced Display and Control Systems for eVTOL Aircraft

An Explainable Data-Driven Optimization Method for Unmanned Autonomous System Performance Assessment

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