Advanced driver assistance systems (ADAS) have been increasingly incorporated in cars for nearly four decades and have changed the relationship of the driver to the driving task substantially. Over this period, original equipment manufacturers (OEMs) have developed similar ADAS functions (e.g., adaptive cruise control, lane keeping assist, forward collision warning), but these functions lack uniformity in their implementation such that there is the possibility of negative transfer of learning across different implementations of the same ADAS function. This brief theoretical paper aims to highlight issues around using some existing human-automation interaction (HAI) frameworks that have been used to classify vehicle automation and discuss considerations for better ADAS classification to inform their design and support safe and satisfactory use.
BenglerK.DietmeyerK.FärberB.MaurerM.StillerC.WinnerH. (2014). Three Decades of Driver Assistance Systems: Review and Future Perspectives. ITS Magazine, October. https://doi.org/10.1109/MITS.2014.2336271
3.
DixonL. (2020). Autonowashing: The greenwashing of vehicle automation. In Transportation Research Interdisciplinary Perspectives (Vol. 5, p. 100113). Elsevier Ltd. https://doi.org/10.1016/j.trip.2020.100113
4.
FrisonA. K.WintersbergerP.RienerA. (2019). Resurrecting the ghost in the shell: A need-centered development approach for optimizing user experience in highly automated vehicles. Transportation Research Part F: Traffic Psychology and Behaviour, 65, 439–456. https://doi.org/10.1016/j.trf.2019.08.001
5.
GasserT.WesthoffD. (2012). Definitions of automation and legal issues in Germany. TRB Road Vehicle Automation Workshop.
InagakiT.SheridanT. B. (2019). A critique of the SAE conditional driving automation definition, and analyses of options for improvement. Cognition, Technology and Work, 21(4), 569–578. https://doi.org/10.1007/s10111-018-0471-5
8.
KaberD. B. (2018). Issues in human–automation interaction modeling: presumptive aspects of frameworks of types and levels of automation. Journal of Cognitive Engineering and Decision Making, 12(1), 7–24. https://doi.org/10.1177/1555343417737203
9.
KellerD.RiceS. (2009). System-wide versus component-specific trust using multiple aids. Journal of General Psychology, 137(1), 114–128. https://doi.org/10.1080/00221300903266713
10.
LopezJ.PakR. (2020). Does one bad machine spoil the bunch?: A review of trust in multiple-component systems. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 64(1), 1546–1550. https://doi.org/10.1177/1071181320641370
11.
MillerC. A.ParasuramanR. (2003). Beyond levels of automation: An architecture for more flexible human-automation collaboration. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 47(1), 182–186. https://doi.org/10.1177/154193120304700138
12.
MindellD. A. (2015). Our Robots, Ourselves: Robotics and the Myths of Autonomy. Penguin Publishing Group.
13.
National Highway Traffic Safety Administration. (2013). National Highway Traffic Safety Administration Preliminary Statement of Policy Concerning Automated Vehicles. 14.
NaujoksF.WiedemannK.SchömigN.HergethS.KeinathA. (2019). Towards guidelines and verification methods for automated vehicle HMIs. Transportation Research Part F: Traffic Psychology and Behaviour, 60, 121–136. https://doi.org/10.1016/j.trf.2018.10.012
16.
ParasuramanR. (1987). Human-computer monitoring. Human Factors, 29(6), 695–706.
17.
ParasuramanR.RileyV. (1997). Humans and automation: Use, misuse, disuse, abuse. In Human Factors: The Journal of the Human Factors and Ergonomics Society (Vol. 39, Issue 2, pp. 230–253). https://doi.org/10.1518/001872097778543886
18.
ParasuramanR.SheridanT. B.WickensC. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics. Part A, Systems and Humans : A Publication of the IEEE Systems, Man, and Cybernetics Society, 30(3), 286–297. https://doi.org/10.1109/3468.844354
19.
Response3. (2006). Code of practice for the design and evaluation of ADAS. Preventive and Active Safety Applications Integrated Project, June.
20.
SAE International. (2018). Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicle. https://doi.org/P141661
21.
SchömigN.WiedemannK.HergethS.ForsterY.MuttartJ.ErikssonA.Mitropoulos-RundusD.GroveK.KremsJ.KeinathA.NeukumA.NaujoksF. (2020). Checklist for expert evaluation of HMIs of automated vehicles-discussions on its value and adaptions of the method within an expert workshop. Information (Switzerland), 11(4), 1–16. https://doi.org/10.3390/INFO11040233
22.
SeppeltB.ReimerB.RussoL.MehlerB.FisherJ.FriedmanD. (2019). Consumer confusion with levels of vehicle automation. Proceedings of the Tenth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, 391–397. https://doi.org/10.17077/drivingassessment.1723
23.
SeppeltB.VictorT. (2020). Driver’s mental model of vehicle automation. In Handbook of Human Factors for Automated, Connected, and Intelligent Vehicles (pp. 55–66).
24.
SheridanT. B.VerplankW. L. (1978). Human and Computer Control of Undersea Teleoperators.
