Abstract
The functional capabilities of unmanned aerial vehicles (UAVs) have dramatically expanded, placing substantial attentional and information processing demands on UAV operators. This study utilized a high-fidelity UAV flight simulation to explore the potential for DFAs in UAV control to reduce operator workload and support overall situation awareness. Three levels of UAV automation (LoAs) were compared, including DFA and static high and low level of automation. This research extended a preliminary investigation by Zhang et al. (2018). The present research addressed the limitations of the preliminary study by increasing the sample size and comparing effects of LoAs during ‘easy to hard’ and ‘hard to easy’ task difficulty transitions. Results of this study demonstrated the presence of “out-of-the-loop performance” issues under high LoA. Results also showed some support for use of DFAs to address out-of-the-loop problems in UAV operations. Findings of this study provide some guidance for design of DFAs in UAV control.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Monfort, S.S., Sibley, C.M., Coyne, J.T.: Using machine learning and real-time workload assessment in a high-fidelity UAV simulation environment. In: Next-Generation Analyst IV, vol. 9851, p. 98510B. International Society for Optics and Photonics (2016)
Endsley, M.R., Kiris, E.O.: The out-of-the-loop performance problem and level of control in automation. Hum. Factors 37(2), 381–394 (1995)
Wiener, E.L., Curry, R.E.: Flight-deck automation: promises and problems. Ergonomics 23(10), 995–1011 (1980)
Endsley, M.R., Onal, E., Kaber, D.B.: The impact of intermediate levels of automation on situation awareness and performance in dynamic control systems. In: Proceedings of the 1997 IEEE Sixth Conference on Human Factors and Power Plants, 1997. Global Perspectives of Human Factors in Power Generation, p. 7. IEEE (1997)
Porat, T., Oron-Gilad, T., Rottem-Hovev, M., Silbiger, J.: Supervising and controlling unmanned systems: a multi-phase study with subject matter experts. Front. Psychol. 7, 568 (2016)
Chen, J.Y., Barnes, M.J., Harper-Sciarini, M.: Supervisory control of multiple robots: human-performance issues and user-interface design. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 41(4), 435–454 (2011)
Kaber, D.B., Perry, C.M., Segall, N., McClernon, C.K., Prinzel III, L.J.: SA implications of adaptive automation for information processing in an air traffic control-related task. Int. J. Ind. Ergon. 36(5), 447–462 (2006)
Hou, M., Zhu, H., Zhou, M., Arrabito, G.R.: Optimizing operator–agent interaction in intelligent adaptive interface design: a conceptual framework. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 41(2), 161–178 (2011)
Kaber, D.B., Endsley, M.R.: The effects of level of automation and adaptive automation on human performance, situation awareness and workload in a dynamic control task. Theor. Issues Ergon. Sci. 5(2), 113–153 (2004)
de Visser, E., Parasuraman, R.: Adaptive aiding of human-robot teaming: effects of imperfect automation on performance, trust, and workload. J. Cogn. Eng. Decis. Making 5(2), 209–231 (2011)
Calhoun, G.L., Ward, V.B., Ruff, H.A.: Performance-based adaptive automation for supervisory control. In: Proceedings of Human Factors Ergonomics Society Annual Meeting, vol. 55, no. 1, pp. 2059–2063. SAGE Publications, Los Angeles (2011)
Parasuraman, R., Cosenzo, K.A., De Visser, E.: Adaptive automation for human supervision of multiple uninhabited vehicles: effects on change detection, situation awareness, and mental workload. In: Military Psychology, vol. 21, no. 2, p. 270 (2009)
Afergan, D., Peck, E.M., Solovey, E.T., Jenkins, A., Hincks, S.W., Brown, E.T., Jacob, R.J.: Dynamic difficulty using brain metrics of workload. In: Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, pp. 3797–3806. ACM (2014)
Zhang, W., Shirley, J., Deng, Y., Kim, N.Y., Kaber, D.: Effects of dynamic automation on situation awareness and workload in UAV control decision tasks. In: International Conference on Applied Human Factors and Ergonomics, pp. 193–203. Springer, Cham, July 2018
Osburn, W.J.: Levels of difficulty in long division. Elementary Sch. J. 46(8), 441–447 (1946)
Wickens, C.D., Gordon, S.E., Liu, Y., Lee, J.: An introduction to human factors engineering (1998)
Murata, A., Iwase, H.: Evaluation of mental workload by fluctuation analysis of pupil area. In: Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 6, pp. 3094–3097. IEEE (1998)
Endsley, M.R., Kaber, D.B.: Level of automation effects on performance, SA and workload in a dynamic control task. Ergonomics 42(3), 462–492 (1999)
Endsley, M.R.: Measurement of situation awareness in dynamic systems. Hum. Factors 37(1), 65–84 (1995)
Vidulich, M.A., Tsang, P.S.: Absolute magnitude estimation and relative judgement approaches to subjective workload assessment. In: Proceedings of Human Factors Ergonomics Society Annual Meeting, vol. 31, no. 9, pp. 1057–1061. SAGE Publications, Los Angeles (1987)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Deng, Y., Shirley, J., Zhang, W., Kim, N.Y., Kaber, D. (2020). Influence of Dynamic Automation Function Allocations on Operator Situation Awareness and Workload in Unmanned Aerial Vehicle Control. In: Nunes, I. (eds) Advances in Human Factors and Systems Interaction. AHFE 2019. Advances in Intelligent Systems and Computing, vol 959. Springer, Cham. https://doi.org/10.1007/978-3-030-20040-4_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-20040-4_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20039-8
Online ISBN: 978-3-030-20040-4
eBook Packages: EngineeringEngineering (R0)