The HUMAN Project: Model-Based Analysis of Human Errors During Aircraft Cockpit System Design

  • Andreas Lüdtke
  • Denis Javaux
  • the HUMAN Consortium
Conference paper


The objective of the HUMAN project is to develop a methodology with techniques and prototypical tools supporting the prediction of human errors in ways that are usable and practical for human-centred design of systems operating in complex cockpit environments. The current approach of analysing systems is error prone as well as costly and time-consuming (based on engineering judgement, operational feedback from similar aircraft, and simulator-based experiments). The HUMAN methodology allows to detect potential pilot errors more accurately and earlier (in the design) and with reduced effort. The detection of errors is achieved by developing and validating a cognitive model of crew behaviour. Cognitive models are a means to make knowledge about characteristic human capabilities and limitations readily available to designers in an executable form. They have the potential to automate parts of the analysis of human errors because they offer the opportunity to simulate the interaction with cockpit systems under various conditions and to predict cognitive processes like the assessment of situations and the resulting choice of actions including erroneous actions. In this way they can be used as a partial “substitute” for human pilots in early development stages when design changes are still feasible and affordable. Model- and simulation-based approaches are already well-established for many aspects of the study, design and manufacture of a modern airliner (e.g., aerodynamics, aircraft systems, engines), for the very same objective of detecting potential problems earlier and reducing the amount of testing required at a later stage. HUMAN extends the modelling approach to the interaction of flight crews with cockpit systems.


Human Error Analysis Cognitive Modelling Aircraft Cockpits Pilot-Cockpit Interaction 



The research leading to these results has received funding from the European Commission Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 21988 Project HUMAN


  1. 1.
    Stokes AF, Kemper K, Kite K (1997) Aeronautical decision making, cue recognition, and expertise under time pressure. In: Zsambok CE, Klein G (eds) Naturalistic decision making. Lawrence Erlbaum Associates, MahwahGoogle Scholar
  2. 2.
    Pew RW, Mavor AS (eds) (1998) Modeling human and organizational behavior: application to military simulations. National Academy Press, WashingtonGoogle Scholar
  3. 3.
    Gluck A, Pew R (2005) Modeling human behavior with integrated cognitive architectures: comparison, evaluation and validation. Lawrence Erlbaum Associates, MahwahGoogle Scholar
  4. 4.
    Newell A (1994) Unified theories of cognition. Harvard University Press, Cambridge Reprint editionGoogle Scholar
  5. 5.
    Anderson JR, Bothell D, Byrne MD, Douglass S, Lebiere C, Qin Y (2004) An integrated theory of the mind. Pittsburgh and Rice University, HoustonGoogle Scholar
  6. 6.
    Lewis RL (2001) Cognitive theory, soar. In: Neil J. Smelser and Paul B. Baltes (eds) International Encyclopedia of the social and behavioral sciences. Pergamon, Amsterdam (Elsevier Science)Google Scholar
  7. 7.
    Corker KM (2000) Cognitive models and control: Human and system dynamics in advanced airspace operations. In: Sarter N, Amalberti R (eds) Cognitive engineering in the aviation domain. Lawrence Erlbaum Associates, Mahwah, pp 13–42Google Scholar
  8. 8.
    Freed M (1998) Simulating human performance in complex, dynamic environments. PhD thesis, Northwestern UniversityGoogle Scholar
  9. 9.
    Zachary W, Santarelli T, Ryder J, Stokes J, Scolaro D (2001) Developing a multi-tasking cognitive agent using the COGNET/iGEN integrative architecture. In: Proceedings of 10th conference on computer generated forces and behavioral representation. Simulation Interoperability Standards Organization, Norfolk, pp 79–90Google Scholar
  10. 10.
    Foyle CF, Hooey BL (2008) Human performance modeling in aviation. CRC Press, Boca RatonGoogle Scholar
  11. 11.
    Edwards E (1988) The emergence of aviation ergonomics. In: Wiener EL, Nagel DC (eds) Human factors in aviation. Academic Press, San DiegoGoogle Scholar
  12. 12.
    Sherry L, Polson P, Feary M, Palmer E (2002) When does the MCDU interface. Work well? In: International conference on HCI-Aerro, Cambridge, MAGoogle Scholar
  13. 13.
    Anderson JR (2000) Learning and memory. Wiley, New YorkGoogle Scholar
  14. 14.
    Lüdtke A, Weber L, Osterloh J-P, Wortelen B (2009) Modeling pilot and driver behaviour for human error simulation. In Duffy VG (ed) Proceedings of the second international conference on digital human modeling (ICDHM), held as part of HCI international 2009. Lecture notes in computer science (LNCS 5620). Springer, Berlin, pp 403–412Google Scholar
  15. 15.
    Lüdtke A, Osterloh J-P, Mioch T, Rister F, Looije R (2009) Cognitive modelling of pilot errors and error recovery in flight management tasks. In: Vanderdonckt J, Palanque P (eds) Proceedings of the 7th working conference on human error, safety and systems development systems development (HESSD). LNCS. Springer, BerlinGoogle Scholar
  16. 16.
    Rasmussen J (1983) Skills, rules, knowledge: signals, signs and symbols and other distinctions in human performance models. IEEE Trans Syst Man Cybern, SMC-13:257–267Google Scholar

Copyright information

© Springer-Verlag Italia Srl 2011

Authors and Affiliations

  • Andreas Lüdtke
    • 1
  • Denis Javaux
    • 2
  • the HUMAN Consortium
  1. 1.OFFIS Institute for Information TechnologyOldenburgGermany
  2. 2.Next Step SolutionsLiègeBelgium

Personalised recommendations