Error Visualization and Information-Seeking Behavior for Air-Vehicle Control

  • Lewis L. ChuangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9183)


A control schema for a human-machine system allows the human operator to be integrated as a mathematical description in a closed-loop control system, i.e., a pilot in an aircraft. Such an approach typically assumes that error feedback is perfectly communicated to the pilot who is responsible for tracking a single flight variable. However, this is unlikely to be true in a flight simulator or a real flight environment. This paper discusses different aspects that pertain to error visualization and the pilot’s ability in seeking out relevant information across a range of flight variables.


Flight Control Error Feedback Task Objective Real Flight Scanning Behavior 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    McRuer, D.: Human dynamics in man-machine systems. Automatica. 16, 237–253 (1980)CrossRefzbMATHGoogle Scholar
  2. 2.
    McRuer, D.T., Jex, H.R.: A review of quasi-linear pilot models. IEEE Trans. Hum. Factors Electron. 3, 231–249 (1967)CrossRefzbMATHGoogle Scholar
  3. 3.
    Nieuwenhuizen, F., Jump, M., Perfect, P., White, M., Padfield, G., Floreano, D., Schill, F., Zufferey, J., Fua, P., Bouabdallah, S., et al.: myCopter – enabling technologies for personal aerial transportation systems. In: Proceeding of the 3rd International HELI World Conference, pp. 1–8 (2011)Google Scholar
  4. 4.
    Barfield, W., Rosenberg, C., Kraft, C.: The effects of visual cues to realism and perceived impact point during final approach. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 33(2), 115–119 (1983)CrossRefGoogle Scholar
  5. 5.
    De Maio, J., Rinalducci, E.J., Brooks, R., Brunderman, J.: Visual cueing effectiveness: comparison of perception and flying performance. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 27(11), 928–932 (1983)CrossRefGoogle Scholar
  6. 6.
    Zaal, P.M.T., Nieuwenhuizen, F.M., van Paassen, M.M., Mulder, M.: Modeling human control of self-motion direction with optic flow and vestibular motion. IEEE Trans. Syst. Man Cybern. B Cybern. 43(2), 544–556 (2012)Google Scholar
  7. 7.
    Scheer, M., Nieuwenhuizen, F.M., Bülthoff, H.H., Chuang, L.L.: The influence of visualization on control performance in a flight simulator. In: Harris, D. (ed.) EPCE 2014. LNCS, vol. 8532, pp. 202–211. Springer, Heidelberg (2014) Google Scholar
  8. 8.
    Yang, J.H., Kennedy, Q., Sullivan, J., Fricker, R.D.: Pilot performance: assessing how scan patterns navigational assessments vary by flight expertise. Aviat. Space Environ. Med. 84(2), 116–124 (2013)CrossRefGoogle Scholar
  9. 9.
    Rash, C.E. (ed.): Helmet Mounted Displays: Design Issues for Rotary-wing Aircraft. SPIE Press, Washington (1999) Google Scholar
  10. 10.
    Wildzunas, R.M., Barron, T.L., Wiley, R.W.: Visual display delay effects on pilot performance. Aviat. Space Environ. Med. 67, 214–221 (1996)Google Scholar
  11. 11.
    Jennings, S., Reid, L.D., Craig, G., Kruk, R.V.: Time delays in visually coupled systems during flight test and simulation. J. Aircraft. 41, 1327–1335 (2004)CrossRefGoogle Scholar
  12. 12.
    Middendorf, M., Lusk, S., Whitley, J.: Power spectral analysis to investigate the effects of simulator time delay on flight control activity. In: IAA Flight Simulation Technologies Conference, pp. 46–52 (1990)Google Scholar
  13. 13.
    Flad, N., Nieuwenhuizen, F.M., Bülthoff, H.H., Chuang, L.L.: System delay in flight simulators impairs performance and increases physiological workload. In: Harris, D. (ed.) EPCE 2014. LNCS, vol. 8532, pp. 3–11. Springer, Heidelberg (2014) Google Scholar
  14. 14.
    Middendorf, M., Fiorita, A., McMillan, G.: The effects of simulator transport delay on performance, workload, and control activity during low-level flight. In: AIAA Flight Simulation Technologies Conference, pp. 412–426 (1991)Google Scholar
  15. 15.
    Hosman, R.J.A.W., Van der Vaart, J.C.: Effects of vestibular and visual motion perception on task performance. Acta Psychol. 48(1), 271–287 (1981)CrossRefGoogle Scholar
  16. 16.
    Kenyon, R.V., Kneller, E.W.: The effects of field of view size on the control of roll motion. IEEE Trans. Syst. Man Cybern. 23(1), 183–193 (1993)CrossRefGoogle Scholar
  17. 17.
    Senders, J.W.: The human operator as a monitor and controller of multidegree of freedom systems. IEEE Trans. Hum. Factors Electron. 5, 2–5 (1964)CrossRefGoogle Scholar
  18. 18.
    Federal Aviation Administration, U.S. Department of Transportation Instrument Flying Handbook FAA-H-8083-15B (2012)Google Scholar
  19. 19.
    Wetzel, P.A., Anderson, G.M., Barelka, B.A.: Instructor use of eye position based feedback for pilot training. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 42, 1388–1392 (1998)CrossRefGoogle Scholar
  20. 20.
    Anders, G.: Pilot’s attention allocation during approach and landing: Eye-and head-tracking research in an A330 full flight simulator. Focusing Attention on Aviation Safety (2001)Google Scholar
  21. 21.
    Yu, C.S., Wang, E.M.Y., Li, W.C., Braithwaite, G.: Pilots visual scan patterns and situation awareness in flight operations. Aviat. Space Environ. Med. 85(7), 708–714 (2014)CrossRefGoogle Scholar
  22. 22.
    Chuang, L.L., Nieuwenhuizen, F.M., Bülthoff, H.H.: A fixed-based flight simulator study: the interdependence of flight control performance and gaze efficiency. In: Harris, D. (ed.) EPCE 2013, Part II. LNCS, vol. 8020, pp. 95–104. Springer, Heidelberg (2013) Google Scholar
  23. 23.
    Ellis, S.R., Stark, L.: Statistical dependency in visual scanning. Hum. Factors J. Hum. Factors Ergon. Soc. 28(4), 421–438 (1986)Google Scholar
  24. 24.
    Allsop, J., Gray, R.: Flying under pressure: effects of anxiety on attention and gaze behavior in aviation. J. Appl. Res. Mem. Cogn. 3(2), 63–71 (2014)CrossRefzbMATHGoogle Scholar
  25. 25.
    Hayashi, M.: Hidden markov models to identify pilot instrument scanning and attention patterns. In: IEEE International Conference on Systems, Man and Cybernetics, 3 (2003)Google Scholar
  26. 26.
    Browatzki, B., Bülthoff, H.H., Chuang, L.L.: A comparison of geometric-and regression-based mobile gaze-tracking. Front. Hum. Neurosci. 8, 200 (2014)CrossRefGoogle Scholar
  27. 27.
    Plöchl, M., Ossandón, J.P., König, P.: Combining EEG and eye tracking: identification, characterization, and correction of eye movement artifacts in electroencephalographic data. Front. Hum. Neurosci. 6, 278 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Perception, Cognition and ActionMax Planck Institute for Biological CyberneticsTübingenGermany

Personalised recommendations