Pilot workload assessment under different levels of autopilot failure

Document Type : Article


1 Department of Aerospace Engineering, Amirkabir University of Technology

2 Department of Aerospace Engineering, Amirkabir University of Technology, Tehran, Iran

3 Aerospace Research Institute, Tehran, Iran


One of the most interesting topics in the field of human machine interaction is workload. In this paper, using information theory concepts, baud rates generated in all subsystems of a generic simulator of piloting tasks were calculated and then, a unique numerical index presenting an estimation of overall workload was extracted. To examine the effectiveness of offered criteria, three tests with different levels of autopilot failure were designed in which existing workload were labeled based on involving baud rates. A group of subjects performed these tests as the pilots while recording their own idea about perceived workload. Results confirmed that there were statistically significant differences between the averages of scores assigned by subjects to the overall workload for three levels of difficulty. Consequently, the proposed quantitative index is effective enough for determination of workload levels in the simulator environment and facilitates creation of needed scenario noticeably.


Main Subjects

1. Cain, B., A review of the Mental Workload Literature, Defence Research and Development Canada Toronto, Human System Integration Section, Toronto, Canada (2007). 2. Eggemeier, F.T., Wilson, G.F., and Kramer, A.F. Workload assessment in multi-task environments", In Multiple-Task Performance, pp. 207-216, Taylor & Francis, Ltd., London, UK (1991). 3. Rusnock, C.F. and Borghetti, B.J. Workload pro les: A continuous measure of mental workload", International
Journal of Industrial Ergonomics, 63, pp. 49-64 (2018). 4. Jaquess, K.J., Gentili, R.J., Lo, L.-C., et al. Empirical
evidence for the relationship between cognitive workload and attentional reserve", International Journal of
Psychophysiology, 121, pp. 46-55 (2017). 5. Johannsdottir, K.R., Magnusdottir, E.H., Sigurj onsdottir, S., et al. The role of working memory capacity in cardiovascular monitoring of cognitive workload", Biological Psychology, 132, pp. 154-163
(2018). 6. Puma, S., Matton, N., Paubel, P.-V., et al. Using theta and alpha band power to assess cognitive
workload in multitasking environments", International Journal of Psychophysiology, 123, pp. 111-120 (2018). 7. Orlandi, L. and Brooks, B. Measuring mental workload and physiological reactions in marine pilots: Building bridges towards redlines of performance", Applied Ergonomics, 69, pp. 74-92 (2018). 8. Santiago-Espada, Y., Myer, R.R., Latorella, K.A.,
et al., The Multi-Attribute Task Battery II (MATBII) Software for Human Performance and Workload Research: A User's Guide, National Aeronautics and Space Administration (NASA), Langley Research Center, Virginia, USA (2011).
9. Kurapati, S., Lukosch, H., Eckerd, S., et al. Relating planner task performance for container terminal operations
to multi-tasking skills and personality type", Transportation Research Part F: Trac Psychology and Behaviour, 51, pp. 47-64 (2017). 10. Bommer, S.C. and Fendley, M. A theoretical framework for evaluating mental workload resources in
human systems design for manufacturing operations", International Journal of Industrial Ergonomics, 63, pp. 7-17 (2018).
11. Liu, S., Nam, C.S., and Fitts, E.P. Quantitative modeling of user performance in multitasking environments", Computers in Human Behavior, 84, pp. 130-  140 (2018). 12. Karpinsky, N.D., Chancey, E.T., Palmer, D.B., et
al. Automation trust and attention allocation in multitasking workspace", Applied Ergonomics, 70, pp. 194-201 (2018).
13. Mortazavi, M.R., Raissi, K., and Hashemi, S.H. Creating a numerical index for measurement of workload levels in the simulator of piloting tasks", iehfsj, 4(4), pp. 24-32 (2017). 14. Wilson, G., Schlegel, R., and Veltman, J., Operator
Functional State Assessment, RTO, NATO Research and Technology Organization, Paris, France (2004). 15. Hart, S.G. and Staveland, L.E. Development of NASA-TLX (task load index): Results of empirical and theoretical research", Advances in Psychology, 52, pp. 139-183 (1988). 16. Hill, S.G., Iavecchia, H.P., Byers, J.C., et al. Comparison of four subjective workload rating scales", Human Factors, 34(4), pp. 429-439 (1992). 17. Hart, S.G. Nasa-task load index (NASA-TLX); 20
years later", Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 50(9), pp. 904- 908 (2006).
18. Park, S., Jeong, S., and Myung, R. Modeling of multiple sources of workload and time pressure e ect with ACT-R", International Journal of Industrial Ergonomics, 63, pp. 37-48 (2018). 19. Winter, S.R., Milner, M.N., Rice, S., et al. Pilot
performance comparison between electronic and paper instrument approach charts", Safety Science, 103, pp.
280-286 (2018). 20. Shannon, C.E. A mathematical theory of communication", Bell System Technical Journal, 27(3), pp. 379- 423 (2001). 21. Serej, N.D., Ahmadian, A., Kasaei, S., et al. A robust keypoint extraction and matching algorithm based on wavelet transform and information theory for pointbased registration in endoscopic sinus cavity data",
126 M.R. Mortazavi et al./Scientia Iranica, Special Issue on: Socio-Co nitive Engineering 26 (2019) 114{126
Signal, Image and Video Processing, 10(5), pp. 983- 991 (2016). 22. Wang, Z., Alahmadi, A., Zhu, D.C., et al. Causality
analysis of fMRI data based on the directed information theory framework", IEEE Transactions on Biomedical Engineering, 63(5), pp. 1002-1015 (2016). 23. Saxena, S., Sanyal, G., Srivastava, S., et al. Preventing from cross-VM side-channel attack using new replacement method", Wireless Personal Communications, 97(3), pp. 4827-4854 (2017).
24. Depizzol, D.B., Montalv~ao, J., Lima, F.d.O., et al. Feature selection for optical network design via a new
mutual information estimator", Expert Systems with Applications, 107, pp. 72-88 (2018). 25. Jha, D.K., Virani, N., Reimann, J., et al. Symbolic analysis-based reduced order Markov modeling of time series data", Signal Processing, 149, pp. 68-81 (2018). 26. Hick, W.E. On the rate of gain of information", Quarterly Journal of Experimental Psychology, 4(1),
pp. 11-26 (1952). 27. Hyman, R. Stimulus information as a determinant of reaction time", Journal of Experimental Psychology, 45(3), pp. 188-196 (1953). 28. Fitts, P.M. The information capacity of the human motor system in controlling the amplitude of movement", Journal of Experimental Psychology: General, 121(3), pp. 262-269 (1992). 29. Phillips, C.A., Repperger, D.W., Kinsler, R. et al. A quantitative model of the human-machine interaction and multi-task performance: A strategy function and the unity model paradigm", Computers in Biology and Medicine, 37(9), pp. 1259-1271 (2007).
30. Walters, C.M. Application of the human-machine interaction model to multiple attribute task battery (MATB): Task component interaction and the strategy paradigm", MSc Thesis, Wright State University, Ohio, USA (2012).
31. Phillips, C.A., Kinsler, R.E., Repperger, D.W., et al. A human-machine interaction strategy function: information
throughput and weighting with application to multiple-attribute-task-battery", Theoretical Issues in Ergonomics Science, 14(4), pp. 379-401 (2013). 32. Camden, A.N. Theoretical throughput capacity: Capabilities of human information processing during multitasking", PhD Dissertation, Wright State University, Ohio, USA (2015). 33. Bishop, C.M., Pattern Recognition and Machine Learning, 1st Edn., Springer, New York, USA (2006). 34. MATLAB Primer", The MathWorks, Inc., Natick, USA (2015). 35. Schneider, D.I., An Introduction to Programming Using Visual Basic 2012, 9th Edn., Prentice Hall Press, New Jersey, USA (2013). 36. Field, A. Discovering statistics using IBM SPSS statistics", Ed., 4th Edn., Sage, London, UK (2013).