Training and Education: Human Factors Considerations for Automated Driving Systems

  • Anuj K. Pradhan
  • John Sullivan
  • Chris Schwarz
  • Fred Feng
  • Shan Bao
Conference paper
Part of the Lecture Notes in Mobility book series (LNMOB)


Vehicles with partial automation, forerunners to those with higher levels of automation, are already being deployed by automakers. These current deployments, although incremental, have the potential to disrupt how people interact with vehicles. This chapter reports on a discussion of related issues that was held as part of the Human Factors Breakout session at the 2017 Automated Vehicle Symposium. The session, titled “Automated Vehicle Challenges: How can Human Factors Research Help Inform Designers, Road Users, and Policy Makers?”, included discussions between industry experts and human factors researchers and professionals on immediate human factors issues surrounding deployment of vehicles with Automated Driving Systems (ADS).


  1. 1.
    SAE On-road Automated Vehicle Standards Committee (2014) Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. SAE InternationalGoogle Scholar
  2. 2.
    Lee JD, Moray N (1994) Trust, self-confidence, and operators’ adaptation to automation. Int J Hum Comput Stud 40(1):153–184CrossRefGoogle Scholar
  3. 3.
    Sheridan TB (1987) Supervisory control. In: Salvendy G (ed) Handbook of human factors. Wiley, New York, pp 1243–1268Google Scholar
  4. 4.
    Dickie DA, Boyle LN (2009) Drivers’ understanding of adaptive cruise control limitations. Proc Hum Factors Ergon Soc Annu Meet 53(23):1806–1810CrossRefGoogle Scholar
  5. 5.
    McDonald AB, McGehee DV, Chrysler ST, Askelson NM, Angell LS, Seppelt BD (2016) National survey identifying gaps in consumer knowledge of advanced vehicle safety systems. Transp Res Rec: J Transp Res Board 2559:1–6CrossRefGoogle Scholar
  6. 6.
    National Highway Traffic Safety Administration (2013) Preliminary statement of policy concerning automated vehicles. Washington, D.C.Google Scholar
  7. 7.
    Horswill MS, Taylor K, Newnam S, Wetton M, Hill A (2013) Even highly experienced drivers benefit from a brief hazard perception training intervention. Accid Anal Prev 52(28):100–110CrossRefGoogle Scholar
  8. 8.
    Pradhan AK, Pollatsek A, Knodler M, Fisher DL (2009) Can younger drivers be trained to scan for information that will reduce their risk in roadway traffic scenarios that are hard to identify as hazardous? Ergonomics 52(6):657–673CrossRefGoogle Scholar
  9. 9.
    McDonald AB, Reyes ML, Row CA, Friberg JE, Faust KS, McGehee DV (2016) University of Iowa Technology Demonstration StudyGoogle Scholar
  10. 10.
    Parasuraman R, Riley V (1997) Humans and automation: use, misuse, disuse abuse. Hum Factors 39(2):230–253CrossRefGoogle Scholar
  11. 11.
    Moray N, Inagaki T (2000) Attention and complacency. Theor Issues Ergon Sci 1:354–365CrossRefGoogle Scholar
  12. 12.
    Parasuraman R, Molloy R, Singh IL (1993) Performance consequences of automation-induced ‘‘complacency’’. Int J Aviat Psychol 3:1–23CrossRefGoogle Scholar
  13. 13.
    Wickens CD, Hollands JG (2000) Engineering psychology and human performance, 3rd edn. Prentice Hall, Englewood Cliffs, NJGoogle Scholar
  14. 14.
    Lee JD, See KA (2004) Trust in automation: designing for appropriate reliance. Hum Factors 46(1):50CrossRefGoogle Scholar
  15. 15.
    Carroll JM, Olson JR (1988) Mental models in human-computer interaction. In: Helander M (ed) Hand book of human-computer interaction, vol 2. Elsevier Science Publishers BV, North-Holland, pp 45–65Google Scholar
  16. 16.
    Skitka LJ, Mosier KL, Burdick M (2000) Accountability and automation bias. Int J Hum Comput Stud 52:701–717CrossRefGoogle Scholar
  17. 17.
    Bahner JE, Hüper AD, Manzey D (2008) Misuse of automated decision aids: complacency, automation bias and the impact of training experience. Int J Hum Comput Stud 66(9):688–699CrossRefGoogle Scholar
  18. 18.
    Sauer J, Chavaillaz A, Wastell D (2016) Experience of automation failures in training: effects on trust, automation bias, complacency and performance. Ergonomics 59(6):767–780CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Anuj K. Pradhan
    • 1
  • John Sullivan
    • 1
  • Chris Schwarz
    • 2
  • Fred Feng
    • 1
  • Shan Bao
    • 1
  1. 1.University of Michigan Transportation Research InstituteAnn ArborUSA
  2. 2.University of Iowa National Advanced Driving SimulatorIowa CityUSA

Personalised recommendations