The next decade will see a rapid increase in the prevalence of partial vehicle automation, specifically conditional automation (i.e., SAE level 3; SAE, 2018). In conditional automation, the expectation is that the user is still receptive to takeover and can disengage while the automation is active, but as the automation approaches its operational limits, or the end of its operational design domain, it issues a request to intervene and the user is expected to retake control. A human–machine interface (HMI) that can safely and effectively transition control is therefore very important. This simulator study investigated how features of the HMI design, specifically feedback about the confidence (i.e., current capability) of the automation influenced transition of control. Participants were assigned to one of three conditions, which received varying amounts of visual and auditory feedback regarding the automation’s confidence. Findings suggest 3-stage auditory-visual feedback about the automation’s confidence may improve subsequent takeover performance compared to 3-stage visual and a control group without feedback. This research demonstrates the potential value of providing more insight into automated feature performance in conditional automation.
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References
1.
AllenS. (2016). Springfield: A case study for immersive driving simulation environments. Image 2015 Conference, 7-8 July, 2015, Dayton, OH.
2.
BagheriN.JamiesonG. A. (2004). The impact of context-related reliability on automation failure detection and scanning behaviour. In2004 IEEE International Conference on Systems, Man and Cybernetics, The Hague, Netherlands, 10–13 October, 2004, pp. 212–217. https://doi.org/10.1109/ICSMC.2004.1398299
3.
BellerJ.HeesenM.VollrathM. (2013). Improving the driver–automation interaction: an approach using automation uncertainty. Human Factors: The Journal of the Human Factors and Ergonomics Society, 55(6), 1130–1141. https://doi.org/10.1177/0018720813482327
4.
BiondiF.StrayerD. L.RossiR.GastaldiM.MulattiC. (2017). Advanced driver assistance systems: Using multimodal redundant warnings to enhance road safety. Applied Ergonomics, 58(2017), 238–244. https://doi.org/10.1016/j.apergo.2016.06.016
5.
BlancoM.AtwoodJ.VasquezH. M.TrimbleT. E.FitchettV. L.RadlbeckJMorganJ. F. (2015). Human factors evaluation of level 2 and level 3 automated driving concepts. Report No. DOT HS 812 182.
6.
EndsleyM. R. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(1), 32–64. https://doi.org/10.1518/001872095779049543
7.
ForsterY.NaujoksF.NeukumA. (2016). Your turn or my turn? Design of a human-machine interface for conditional automation. Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications - Automotive’UI ’16, Ann Arbor, 24-26 October, 2016, pp. 253–260. https://doi.org/10.1145/3003715.3005463
8.
HoffK. A.BashirM. (2015). Trust in automation: Integrating empirical evidence on factors that influence trust. Human Factors: The Journal of the Human Factors and Ergonomics Society, 57(3), 407–434. https://doi.org/10.1177/0018720814547570
9.
KunzeA.SummerskillS. J.MarshallR.FiltnessA. J. (2019). Automation transparency: Implications of uncertainty communication for human-automation interaction and interfaces. Ergonomics, 62(3), 345–360. https://doi.org/10.1080/00140139.2018.1547842
MATLAB (2018). 9.7.0.1190202 (R2019b). The MathWorks Inc.
12.
McGuirlJ. M.SarterN. B. (2006). Supporting trust calibration and the effective use of decision aids by presenting dynamic system confidence information. Human Factors, 48(4), 656–665. https://doi.org/10.1518/001872006779166334
13.
MeratN.SeppeltB.LouwT.EngströmJ.LeeJ. D.JohanssonE.GreenC. A.KatazakiS.MonkC.ItohM.McGeheeD.SundaT.UnouraK.VictorT.SchiebenA.KeinathA. (2019). The “out-of-the-loop” concept in automated driving: Proposed definition, measures and implications. Cognition, Technology & Work, 21(1), 87–98. https://doi.org/10.1007/s10111-018-0525-8
14.
NaujoksF.WiedemannK.SchömigN. (2017). The importance of interruption management for usefulness and acceptance of automated driving. In Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications - AutomotiveUI ’17, Oldenburg, 24-27 September, 2017, pp. 254–263. https://doi.org/10.1145/3122986.3123000
15.
ParasuramanR.ManzeyD. H. (2010). Complacency and bias in human use of automation: An attentional integration. Human Factors, 52(3), 381–410. https://doi.org/10.1177/0018720810376055
16.
ParasuramanR.MolloyR.SinghI. L. (1993). Performance consequences of automation-induced “complacency. The International Journal of Aviation Psychology, 3(1), 1–23. https://doi.org/10.1207/s15327108ijap0301_1
17.
PolitisI.BrewsterS.PollickF. (2015). Language-based multimodal displays for the handover of control in autonomous cars. In Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications - AutomotiveUI ’15, Nottingham, UK, 1–3 September, 2015, pp. 3–10. https://doi.org/10.1145/2799250.2799262
18.
R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing. URLhttps://www.R-project.org/.
19.
SAE, J3016 (2018). SAE international taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. SAE International.
20.
SeongY.BisantzA. M. (2008). The impact of cognitive feed-back on judgment performance and trust with decision aids. International Journal of Industrial Ergonomics, 38(7–8), 608–625. https://doi.org/10.1016/j.ergon.2008.01.007
21.
SeppeltB. D.LeeJ. D. (2007). Making adaptive cruise control (ACC) limits visible. International Journal of Human-Computer Studies, 65(3), 192–205. https://doi.org/10.1016/j.ijhcs.2006.10.001
22.
SeppeltB. D.LeeJ. D. (2019). Keeping the driver in the loop: Dynamic feedback to support appropriate use of imperfect vehicle control automation. International Journal of Human-Computer Studies, 125(1), 66–80. https://doi.org/10.1016/j.ijhcs.2018.12.009
23.
SklarA. E.SarterN. B. (1999). Good vibrations: tactile feedback in support of attention allocation and human-automation coordination in event-driven domains. Human Factors: The Journal of the Human Factors and Ergonomics Society, 41(4), 543–552. https://doi.org/10.1518/001872099779656716
24.
SpenceC.HoC. (2008). Multisensory warning signals for event perception and safe driving. Theoretical Issues in Ergonomics Science, 9(6), 523–554. https://doi.org/10.1080/14639220701816765.
25.
VictorT. W.TivestenE.GustavssonP.JohanssonJ.SangbergF.Ljung AustM. (2018). Automation expectation mismatch: Incorrect prediction despite eyes on threat and hands on wheel. Human Factors, 60(8), 1095–1116. https://doi.org/10.1177/0018720818788164.
26.
WiseJ. A. (1998). Aviation automation: The search for a human-centered approach by Charles E. Billings 1997, 355 pages, $36.00 (pbk.). $69.95 (hbk.) Mahwah, NJ: Lawrence Erlbaum Associates ISBN 0-8058-2127-9 (Vol. 6(4)). Ergonomics in Design, 31. https://doi.org/10.1177/106480469800600410.
