Skip to main content

Influence of Dynamic Automation Function Allocations on Operator Situation Awareness and Workload in Unmanned Aerial Vehicle Control

  • Conference paper
  • First Online:
Advances in Human Factors and Systems Interaction (AHFE 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 959))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. 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)

    Google Scholar 

  2. 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)

    Article  Google Scholar 

  3. Wiener, E.L., Curry, R.E.: Flight-deck automation: promises and problems. Ergonomics 23(10), 995–1011 (1980)

    Article  Google Scholar 

  4. 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)

    Google Scholar 

  5. 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)

    Article  Google Scholar 

  6. 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)

    Article  Google Scholar 

  7. 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)

    Article  Google Scholar 

  8. 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)

    Google Scholar 

  9. 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)

    Article  Google Scholar 

  10. 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)

    Article  Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. 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)

    Article  Google Scholar 

  13. 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)

    Google Scholar 

  14. 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

    Google Scholar 

  15. Osburn, W.J.: Levels of difficulty in long division. Elementary Sch. J. 46(8), 441–447 (1946)

    Article  Google Scholar 

  16. Wickens, C.D., Gordon, S.E., Liu, Y., Lee, J.: An introduction to human factors engineering (1998)

    Google Scholar 

  17. 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)

    Google Scholar 

  18. 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)

    Article  Google Scholar 

  19. Endsley, M.R.: Measurement of situation awareness in dynamic systems. Hum. Factors 37(1), 65–84 (1995)

    Article  Google Scholar 

  20. 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)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to David Kaber .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

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)

Publish with us

Policies and ethics