Advertisement

Man–machine Integration Design and Analysis System (MIDAS) v5: Augmentations, Motivations, and Directions for Aeronautics Applications

Conference paper

Abstract

As automation and advanced technologies are introduced into transport systems ranging from the Next Generation Air Transportation System termed NextGen, to the advanced surface vehicle Intelligent Transportations Systems, to future systems designed for space exploration, there is an increased need to validly predict how the future systems will be vulnerable to error given the demands imposed by assisted technologies. One formalized method to study the impact of assisted technologies on the human operator in a safe and non-obtrusive manner is through the use of human performance models (HPMs). HPMs play an integral role when complex human–system designs are proposed, developed, and tested. One HPM tool termed the Man–machine Integration Design and Analysis System (MIDAS) is a NASA Ames Research Center HPM software tool that has been applied to predict human–system performance in various domains since 1986. MIDAS is a dynamic, integrated HPM environment that facilitates the design, visualization, and computational evaluation of complex man–machine system concepts in simulated operational environments. A range of aviation specific applications including an approach used to model human error for NASA’s Aviation Safety Program, and “what-if” analyses to evaluate flight deck technologies for NextGen operations will be discussed. This chapter will culminate by raising two challenges for the field of predictive HPMs for complex human–system designs that evaluate assisted technologies: that of (1) model transparency and (2) model validation.

Keywords

Human error Human performance modeling MIDAS v5 NASA 

Notes

Acknowledgment

The composition of this work was supported by the Federal Aviation Authority (FAA)/NASA Inter Agency Agreement DTFAWA-10-X-80005 Annex 5. The author would like to thank all reviewers for their insightful comments.

References

  1. 1.
    Gore BF (2008) Human performance: evaluating the cognitive aspect (Chapter 32). In: Duffy V (ed) Handbook of digital human modeling. CRC Press Inc., Boca Raton, pp 32:1–32:18Google Scholar
  2. 2.
    Gore BF, Hooey BL, Wickens CD, Scott-Nash S (2009) A computational implementation of a human attention guiding mechanism in MIDAS v5. In: 12th international conference, HCI international, San Diego, CA. Springer, BerlinGoogle Scholar
  3. 3.
    Hooey BL, Gore BF, Wickens CD, Salud E, Scott-Nash S, Socash C et al (2010) Modeling pilot situation awareness in human modelling of assisted technologies workshop. Belgirate, Italy. Springer, BerlinGoogle Scholar
  4. 4.
    Gore BF, Smith JD (2006) Risk assessment and human performance modeling: the need for an integrated approach. Int J Hum Factors Modeling Simulation 1(1):119–139CrossRefGoogle Scholar
  5. 5.
    McCracken JH, Aldrich TB (1984) Analysis of selected LHX mission functions: implications for operator workload and system automation goals. Anacapa Sciences, Inc., Fort RuckerGoogle Scholar
  6. 6.
    Mitchell DK (2000) Mental workload and ARL workload modeling tools. U.S. Army Research Laboratory, Aberdeen Proving GroundGoogle Scholar
  7. 7.
    Baddeley AD, Hitch G (1974) Working memory. In: Bower GH (ed) The psychology of learning and motivation. Academic Press, LondonGoogle Scholar
  8. 8.
    Wickens CD, McCarley JS (2008) Applied attention theory. CRC Press, Taylor and Francis Group, Boca RatonGoogle Scholar
  9. 9.
    Wickens CD, Goh J, Helleberg J, Horrey W, Talleur DA (2003) Attentional models of multi-task pilot performance using advanced display technology. Hum Factors 45(3):360–380CrossRefGoogle Scholar
  10. 10.
    Wickens CD, Hooey BL, Gore BF, Sebok A, Koenecke C (2010) Identifying black swans in NextGen: predicting human performance in off-nominal conditions. Hum Factors 51(5):638–651CrossRefGoogle Scholar
  11. 11.
    JPDO (2009) In: JPDO (ed) Concept of operations for the next generation air transportation system. JPDO, WashingtonGoogle Scholar
  12. 12.
    Sarter NB, Woods DD, Billings CE (1997) Automation surprises. In: Salvendy G (ed) Handbook of Human Factors and Ergonomics. Wiley, New York, pp 1926–1943Google Scholar
  13. 13.
    Foyle DC, Andre AD, Hooey BL (2005) Situation awareness in an augmented reality cockpit: design, viewpoints and cognitive glue. In: 11th international conference on human computer interaction, Las Vegas, NVGoogle Scholar
  14. 14.
    Gore BF, Verma S, Jadhav A, Delnegro R, Corker K (2002) Human error modeling predictions: air MIDAS human performance modeling of T-NASA. SJSU, San JoseGoogle Scholar
  15. 15.
    Verma S, Lozito S, Trott G (2008) Preliminary guidelines on flight deck procedures for very closely spaced parallel approaches. In: International Council for the Aeronautical Sciences (ICAS) 2008 congress. American Institute for Aeronautics and Astronautics (AIAA), AnchorageGoogle Scholar
  16. 16.
    Gore BF, Hooey BL, Salud E, Wickens CD, Sebok A, Hutchins S et al (2008) Meeting the challenge of cognitive human performance model interpretability though transparency: MIDAS v5.x. In: Applied human factors and ergonomics international conference. USA Publishing, Las VegasGoogle Scholar

Copyright information

© Springer-Verlag Italia Srl 2011

Authors and Affiliations

  1. 1.San Jose State University Foundation / NASA Ames Research CenterMoffett FieldUSA

Personalised recommendations