Evaluating Human Interaction with Automation in a Complex UCAV Control Station Simulation Using Multiple Performance Metrics

  • Sasanka Prabhala
  • Jennie J. Gallimore
  • Jesse R. Lucas


The dynamics and complexities of human–machine systems and the overwhelming amount of data that must be handled by human operators is making automation a critical factor in planning, decision-making, and in execution in many complex systems. Complex systems are characterized by uncertainty, ambiguity, ill-defined goals, dynamically changing conditions, distractions, and time pressures. A semi-autonomous system requiring significant human-centered design support are remotely operated vehicles (ROVs) such as unmanned aerial vehicles (UAVs), unmanned combat aerial vehicles (UCAVs), space maneuverable vehicles (SMVs), and unmanned emergency vehicles (UEVs). The objectives of this research are to (1) develop a simulation system that would allow investigation of human operator performance issues when supervising multiple UCAV vehicles, and (2) investigate human performance through the collection of multiple dependent measures. The simulation tool was designed to be adaptable to allow continued research on a variety of factors related to the control of autonomous vehicles. A research study using this simulation tool investigated the effects of automation and the number of UCAVs being controlled on operator performance during an identify and destroy mission. Results indicate that increasing the number of UCAVs significantly increased workload under LOW Automation and the increase in workload was reduced when HIGH Automation was introduced. This study showed that operators are able to control multiple UCAVs more effectively with appropriate automation.


Flight Path Automation Condition Unknown Target Simulation Architecture Mission Area 
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. Alberdi E, Strigini L, Povyakalo AA, Ayton P (2009) Why are people’s decisions sometimes worse with computer support? Proceedings of the 28th International Conference on Computer safety, reliability and security, Hamburg, Germany, pp18--31Google Scholar
  2. Barnes MJ, Matz MF (1998) Crew simulations for unmanned aerial vehicle (UAV) applications: sustained effects, shift factors, interface issues, and crew size. In: Proceedings of the human factors and ergonomics society 42nd annual meeting, Chicago, IL, October 5–9, pp 143--147Google Scholar
  3. Cummings ML, Bruni S, Mercier S, Mitchell PF (2007) Automation architecture for single operator, multiple UAV command and control. Int CE J 1(2)Google Scholar
  4. Cummings ML, Mitchell PJ (2008) Predicting controller capacity in supervisory control of multiple UAVs. IEEE Trans Syst Man Cybern Part A Syst Hum 38:451–460CrossRefGoogle Scholar
  5. Dixon SR, Wickens CD (2004) Automation, reliability in unmanned aerial vehicle flight control. In: Vincenzi DA, Mouloua M, Hancock PA (eds) Human performance, situational awareness and automation: current research and trends, pp 205--210Google Scholar
  6. Endsley MR (1987) The application of human factors to the development of expert systems for advanced cockpits. Proc Hum Factors Soc 31st Annu Meet 1387--1392Google Scholar
  7. Endsley MR (1996) Automation and situation awareness. In: Parasuraman R, Mouloua M (eds) Automation and human performance: theory and application, pp 163--181Google Scholar
  8. Endsley MR, Kaber DB (1999) Level of automation effects on performance, situational awareness and workload in a dynamic control task. Ergonomics 42:462–492CrossRefGoogle Scholar
  9. Jessie YC, Barnes MJ (2008) Robotics operator performance in a military multi-tasking environment. In: Proceedings of the 3rd ACM/IEEE international conference on human robot interaction, Amsterdam, Netherlands, pp 279--286Google Scholar
  10. Lucas JR, Gallimore, JJ, Prabhala S (2001) Using decision structure to analyze complex semi-autonomous systems. In: Proceedings of the international conference on computer-aided ergonomics and safety, 28 July–2 August 2001, Maui, HawaiiGoogle Scholar
  11. Meyer J, Feinshreiber L, Parmet Y (2003) Levels of Automation in a Simulated Failure Detection Task. IEEE Int Conf Syst Man Cybern 3:2101–2106Google Scholar
  12. Mooij M, Corker K (2002) Supervisory control paradigm: limitations in applicability to advanced air traffic management systems. In: Proceedings of IEEE digital avionics system conference, vol 1, pp IC3.1--IC3.8Google Scholar
  13. Mosier KL, Skitka LJ (1996) Human decision makers and automated decision aids: made for each other? In: Mouloua M and Parasuraman R (eds) Human performance in automated systems: current research and trends, pp 201--220Google Scholar
  14. Nathalie P, Xavier N, David H, Eric S (2008) Physiological investigation of vigilance decrement: Boredom or cognitive fatigue? Physiol Behav 93(1–2):369–378Google Scholar
  15. arasuraman P R, Sheridan TB, Wickens CD (2000) A model for type and level of human interaction with automation. IEEE Trans Syst Man Cybern 30(3):286–297CrossRefGoogle Scholar
  16. Parasuraman R, Hancock PA (2008) Mitigating the adverse effects of workload, stress, and fatigue with adaptive automation. In: Hancock P, Szalma JL (eds) Performance under Stress, pp 45--58Google Scholar
  17. Riley V 1996 Operator reliance on automation: theory and data. In: Parasuraman R, Mouloua M (eds) Automation and human performance: theory and applications, pp 19--35Google Scholar
  18. Ruff HA, Narayanan S, Draper MH (2002) Human interaction with levels of automation and decision-aid fidelity in the supervisory control of multiple unmanned air vehicles. Presence 11(4):335–351CrossRefGoogle Scholar
  19. Sarter NB, Woods DD (1995) How in the world did we get into that mode? mode error and awareness in supervisory control. Hum Factors 37(1):5–19CrossRefGoogle Scholar
  20. Sheridan TB (1980) Computer control and human alienation. Technol Rev, pp 60--73Google Scholar
  21. Taylor RM (2002) Capability, cognition and autonomy. In: Proceedings of RTO Human Factors and Medicine Panel (HFM) Symposium, Warsaw, Poland, October 7–9Google Scholar
  22. Warm JS, Matthews G, Finomore JR VS (2008) Vigilance, workload, and stress. In: Hancock P, Szalma J.L (eds) Performance under stress, pp 115--142Google Scholar
  23. Wickens CD (1999) Automation in air traffic control: the human performance Issues. In: Scerbo M, Mouloua M (eds) Automation technology and human performance: current research and trends, pp 2--10Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Sasanka Prabhala
    • 1
  • Jennie J. Gallimore
    • 2
  • Jesse R. Lucas
    • 2
  1. 1.Interactions and Experience Research Group, Intel LabsHillsboroUSA
  2. 2.Department of Biomedical, Industrial, and Human Factors EngineeringWright State UniversityDaytonUSA

Personalised recommendations