Operator Trust Function for Predicted Drone Arrival

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 784)


To realize the full benefit from autonomy, systems will have to react to unknown events and uncertain dynamic environments. The resulting number of behaviors is essentially infinite; thus, the system is effectively non-deterministic but an operator needs to understand and trust the actions of the autonomous vehicles. This research began to tackle non-deterministic systems and trust by beginning to develop a user trust function based on intent information displayed and the prescribed bounds on allowable behaviors/actions of the non-deterministic system. Linear regression shows promise on being able to predict a person’s confidence of the machine’s prediction. Linear regression techniques indicated that subject characteristics, scenario difficulty, the experience with the system, and confidence earlier in the scenario account for approximately 60% of the variation in confidence ratings. This paper details the specifics of the liner regression model – essentially a trust function – for predicting a person’s confidence.


Trust function Non-deterministic system Linear regression Autonomy Confidence rating 



This research was supported by NASA Langley Research Center IRAD funding in 2016.


  1. 1.
    Chua, L.O.: Chua circuit. Scholarpedia 2, 1488 (2007)CrossRefGoogle Scholar
  2. 2.
    Chua, L.O., Wu, C.W., Huang, A., Zhong, G.-Q.: A universal circuit for studying and generating chaos-Part I: routes to chaos. IEEE Trans. Circ. Syst. I Fundam. Theor. Appl. 40, 13 (1993)CrossRefGoogle Scholar
  3. 3.
    Beller, J., Heesen, M., Vollrath, M.: Improving the driver-automation interaction. Hum. Factors J. Hum. Factors Ergon. Soc. 55, 11 (2013)CrossRefGoogle Scholar
  4. 4.
    McGuirl, J.M., Sarter, N.B.: Supporting trust calibration and the effective use of decision aids by presenting dynamic system confidence information. Hum. Factors J. Hum. Factors Ergon. Soc. 48, 10 (2006)CrossRefGoogle Scholar
  5. 5.
    Verberne, F.M.F., Ham, J., Midden, C.J.H.: Trust in smart systems. Hum. Factors J. Hum. Factors Ergon. Soc. 54, 11 (2012)CrossRefGoogle Scholar
  6. 6.
    Couch, L.L., Jones, W.H.: Measuring levels of trust. J. Res. Pers. 31, 18 (1997)CrossRefGoogle Scholar
  7. 7.
    Jian, J.-Y., Bizantz, A.M., Drury, C.G.: Foundations for an empirically determined scale of trust in automated systems. Int. J. Cogn. Ergon. 4, 16 (2000)CrossRefGoogle Scholar
  8. 8.
    Hoff, K.A., Bashir, M.: Trust in automation: integrating empirical evidence on factors that influence trust. Hum. Factors J. Hum. Factors Ergon. Soc. 57, 407–434 (2015)CrossRefGoogle Scholar
  9. 9.
    Schaeffer, K.E.: The perception and measurement of human-robot trust. Doctor of Philosophy, p. 359, Department of Modeling and Simulation in the College of Sciences, University of Central Florida, Orlando, Florida (2013)Google Scholar
  10. 10.
    Boyce, M.W., Chen, J.Y.C., Selkowitz, A.R., Lakmani, S.G.: Effects of agent transparency on operator trust. In: Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts, pp. 179–180. ACM, New York (2015)Google Scholar
  11. 11.
    Chen, J.Y.C., Barnes, M.J., Selkowitz, A.R., Stowers, K.: Effects of agent transparency on human-autonomy teaming effectiveness. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1838–1843. IEEE (2016)Google Scholar
  12. 12.
    Chen, J.Y.C., Procci, K., Boyce, M.W., Wright, J., Garcia, A., Barnes, M.J.: Situation awareness-based agent transparency, p. 36. Laboratory, U.S.A.R, U.S. Army Research Laboratory, Aberdeen Proving Ground (2014)Google Scholar
  13. 13.
    Lakhmani, S., Abich, J., Barber, D., Chen, J.: A proposed approach for determining the influence of multimodal robot-of-human transparency information on human-agent teams. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience: 10th International Conference, AC 2016, Held as Part of HCI International 2016, Toronto, ON, Canada, 17–22 July 2016, Proceedings, Part II, pp. 296–307. Springer International Publishing, Cham (2016)Google Scholar
  14. 14.
    Mercado, J.E., Rupp, M.A., Chen, J.Y.C., Barnes, M.J., Procci, K.: Intelligent agent transparency in human-agent teaming for multi-UxV management. Hum. Factors 58, 401–415 (2016)CrossRefGoogle Scholar
  15. 15.
    Wright, J.L., Chen, J.Y.C., Barnes, M.J., Hancock, P.A.: Agent reasoning transparency’s effect on operator workload. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 60, 249–253 (2016)CrossRefGoogle Scholar
  16. 16.
    Endsley, M.R.: Toward a theory of situation awareness in dynamic systems. Hum. Factors 37, 32–64 (1995)CrossRefGoogle Scholar
  17. 17.
    Endsley, M.R.: Situation awareness misconceptions and misunderstandings. J. Cogn. Eng. Decis. Making 9, 4–32 (2015)CrossRefGoogle Scholar
  18. 18.
    Parasuraman, R., Riley, V.: Humans and automation: use, misuse, disuse, abuse. Hum. Factors J. Hum. Factors Ergon. Soc. 39, 230–253 (1997)CrossRefGoogle Scholar
  19. 19.
    Moray, N., Inagaki, T., Makoto, I.: Adaptive automation, trust, and self-confidence in fault management of time-critical tasks. J. Exp. Psychol. Appl. 6, 44–58 (2000)CrossRefGoogle Scholar
  20. 20.
  21. 21.
    Trujillo, A.C.: How electronic questionnaire formats affect scaled responses. In: 15th International Symposium on Aviation Psychology, Dayton, OH (2009)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature (outside the USA) 2019

Authors and Affiliations

  1. 1.NASA Langley Research CenterHamptonUSA

Personalised recommendations