Human Factors Considerations for the Design of Level 2 and Level 3 Automated Vehicles

  • Janet I. CreaserEmail author
  • Gregory M. Fitch
Conference paper
Part of the Lecture Notes in Mobility book series (LNMOB)


The success of automated vehicles ultimately hinges on how well they meet their users’ needs. The study and application of human factors throughout the automated-vehicle design cycle can yield a safe, useful, and reliable technology that does what its users want. This paper reports on a breakout session of the 2014 “Automated Vehicles Symposium” aiming to present the state of automated-vehicle human factors research and how it is being applied in the development of automated vehicles. Discussions were framed around two Transportation Research Board (TRB) Research Needs Statements that pertained to Human Factors research on automated vehicles. The two needs statements were officially balloted by TRB and covered: (1) the transfer of control between levels of automation or back to manual driving, and (2) the misuse and abuse of automated vehicles. Additionally, the group primarily considered issues associated with NHTSA’s Level 2 and Level 3 automation. The transfer of control discussions included designing for situation awareness, mental model development, and “failing gracefully.” For automation misuse, the consensus was that some drivers will unknowingly over-rely on the automation in situations that it was not designed to handle. For automation abuse, it was recognized that there will be a segment of the driving population who will knowingly improperly and unsafely use the automation for personal gain. Therefore, any design of Level 2 or Level 3 systems that require the driver to be in the loop or brought back into the loop should include feedback and possibly forcing functions that prevent unsafe vehicle operation. Ultimately, the attendees unanimously agreed that human factors methods should be employed early and iteratively in the design cycle to achieve this goal.


Human factors Automation Situation awareness Mental models Driver safety 


  1. 1.
  2. 2.
    Fitch GM (2014) Defining automated vehicle misuse and abuse: working document generated by the society of automotive engineers DVI task force 5—automated vehicles and DVI challengesGoogle Scholar
  3. 3.
    National Highway Traffic Safety Administration (2013) Policy on automated vehicle development. Retrieved from Accessed 30 May 2013
  4. 4.
    Society for Automotive Engineers International (2014) J3016: taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. Summary Retrieved from Accessed 20 Nov 2014
  5. 5.
    Marinik A, Bishop R, Fitchett V, Morgan JF, Trimble TE, Blanco M (2014) Human factors evaluation of level 2 and level 3 automated driving concepts: Concepts of operation (Report No. DOT HS 812 044). National Highway Traffic Safety Administration, Washington, DCGoogle Scholar
  6. 6.
    Trimble TE, Bishop R, Morgan JF, Blanco M (2014) Human factors evaluation of level 2 and level 3 automated driving concepts: past research, state of automation technology, and emerging system concepts (Report No. DOT HS 812 043). National Highway Traffic Safety Administration, Washington, DCGoogle Scholar
  7. 7.
    Casner S (2014) Human factors of automation in the airline cockpit. In: Presented at the 2014 automated vehicles symposium, human factors breakout Session, San Francisco, CAGoogle Scholar
  8. 8.
    Endsley MR (1995) Toward a theory of situation awareness in dynamic systems. Hum Factors 37(1):32–64CrossRefGoogle Scholar
  9. 9.
    Parasuraman R, Riley V (1997) Humans and automation: use, misuse, disuse, abuse. Hum Factors 39(2):230–253CrossRefGoogle Scholar
  10. 10.
    Fitch GM, Soccolich SA, Guo F, McClafferty J, Fang Y, Olson RL, Perez MA, Hanowski RJ, Hankey JM, Dingus TA (2013) The impact of hand-held and hands-free cell phone use on driving performance and safety-critical event risk (Report No. DOT HS 811 757). National Highway Traffic Safety Administration, Washington, DCGoogle Scholar
  11. 11.
    Klauer SG, Dingus TA, Neale VL, Sudweeks JD, Ramsey DJ (2006) The impact of driver inattention on near-crash/crash risk: an analysis using the 100-car naturalistic driving study data (No. DOT-HS-810-594). NHTSA, Washington, DCGoogle Scholar
  12. 12.
    Klauer SG, Guo F, Simons-Morton BG, Ouimet MC, Lee SE, Dingus TA (2014) Distracted driving and risk of road crashes among novice and experienced drivers. N Engl J Med 370(1):54–59. doi: 10.1056/NEJMsa1204142 CrossRefGoogle Scholar
  13. 13.
    Carsten O, Lai F, Barnard Y, Jamson AH, Merat N (2012) Control task substitution in semi-automated driving: Does it matter what aspects are automated? Hum Factors 54:747–761CrossRefGoogle Scholar
  14. 14.
    Creaser J, Seppelt B (2014) Situation awareness in transfer of control. In: Presented at the 2014 automated vehicles symposium, human factors breakout session, San Francisco, CAGoogle Scholar
  15. 15.
    Ju W (2014) Mental models for automated driving. In: Presented at the 2014 automated vehicles symposium, human factors breakout session, San Francisco, CAGoogle Scholar
  16. 16.
    Lee JD, McGeehee DV, Brown TL, Marshall D (2006) Effects of adaptive cruise control and alert modality on driver performance. Transp Res Rec 1980:49–56CrossRefGoogle Scholar
  17. 17.
    Merat N, Jamson AH (2009) How do drivers behave in a highly automated car? In: Proceedings of the Fifth international driving symposium on human factors in driver assessment, training and vehicle design. University of Iowa School of Public Policy, Iowa City, pp 514–521Google Scholar
  18. 18.
    Seppelt BD, Lee JD (2007) Making adaptive cruise control (ACC) limits visible. Int J Hum Comput Stud 65:192–205CrossRefGoogle Scholar
  19. 19.
    Seppelt BD (2009) Supporting operator reliance on automation through continuous feedback (Unpublished doctoral dissertation). University of Iowa, Iowa CityGoogle Scholar
  20. 20.
    Stanton NA, Young MS (2000) A proposed psychological model of driving automation. Theoretical issues in ergonomic. Science 1(4):315–331Google Scholar
  21. 21.
    Stanton NA, Young MS (2005) Driver behaviour with adaptive cruise control. Ergonomics 48(10):1294–1313CrossRefGoogle Scholar
  22. 22.
    Lee JD, See KA (2004) Trust in automation: Designing for appropriate reliance. Hum Factors 46(1):50–80CrossRefGoogle Scholar
  23. 23.
    Itoh M (2012) Toward overtrust-free advanced driver assistance systems. Cogn Technol Work 14:51–60CrossRefGoogle Scholar
  24. 24.
    Fitch G, Schwarz C (2014) Misuse and abuse of automation. In: Presented at the 2014 automated vehicles symposium, human factors breakout session, San Francisco, CAGoogle Scholar
  25. 25.
    Centers for Disease Control and Prevention (2014) Motor vehicle safety: impaired driving. Retrieved from Accessed 23 Nov 2014
  26. 26.
    National Highway Traffic Safety Administration (2014) Traffic safety facts 2012 data: alcohol-impaired driving. Accessed 23 Nov 2014Google Scholar
  27. 27.
    Casner S, Geven M, Williams KT (2005) The effectiveness of airline pilot training for abnormal events. Hum Factors 55(3):477–485CrossRefGoogle Scholar
  28. 28.
    Norman DA (2002) The design of everyday things. Basic Books, New YorkGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.CE ConsultingNew BrightonUSA
  2. 2.Center for Automated Vehicle SystemsVirginia Tech Transportation InstituteBlacksburgUSA

Personalised recommendations