The effect of reliability on drivers’ trust and behavior in conditional automation
Cognition, Technology & Work - 2018
Tóm tắt
The development of automated vehicles continues unabated. The human factor challenges of designing safe automated driving systems are critical as the first several generations of automated vehicles are expected to be semi-autonomous, requiring frequent transfers of control between the driver and vehicle. Conditional automation raises particular concerns about drivers being out of the loop. A driving simulator study was performed with 20 participants to study driving with conditional automation. We observed driver performance and measured comfort as an indicator of the development of trust in the system. One scenario used a more capable automation system that was able to respond to most events by slowing or changing lanes on its own. The other scenario used a less capable automation system that issued takeover requests for all events. Participants drove both scenarios in counterbalanced order and experienced the different capabilities as changes in reliability. The automation would behave one way in the first work zone and a different way in the second. We observed three types of comfort profiles over the course of the drives. Several behavioral measures, notably gaze, showed effects of reliability variations. Trust calibrated during the first-driven scenario was seen to affect behavior during the second one, and this effect was more pronounced in the older age group, and most pronounced for women in that group.
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Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67(1):48. https://doi.org/10.18637/jss.v067.i01
Bellem H, Schönenberg T, Krems JF, Schrauf M (2016) Objective metrics of comfort: developing a driving style for highly automated vehicles. Transp Res Part F Traffic Psychol Behav 41:45–54. https://doi.org/10.1016/j.trf.2016.05.005
Blanco M, Atwood J, Vasquez HM, Trimble TE, Fitchett V, Radlbeck J, Fitch GM, Russell SM, Green CA, Cullinane B, Morgan JF (2015) Human factors evaluation of level 2 and level 3 automated driving concepts. Report DOT HS 812 182, Washington, D.C
Burnham KP, Anderson DR (2004) Multimodel inference: understanding AIC and BIC in model selection. Sociol Methods Res 33(2):261–304
Degani A, Goldman CV, Deutsch O, Tsimhoni O (2017) On human–machine relations. Cogn Technol Work 19(2):211–231. https://doi.org/10.1007/s10111-017-0417-3
Flemisch F, Heesen M, Hesse T, Kelsch J, Schieben A, Beller J (2012) Towards a dynamic balance between humans and automation: authority, ability, responsibility and control in shared and cooperative control situations. Cogn Technol Work 14(1):3–18. https://doi.org/10.1007/s10111-011-0191-6
Gold C, Körber M, Hohenberger C, Lechner D, Bengler K (2015) Trust in automation—before and after the experience of take-over scenarios in a highly automated vehicle. Procedia Manuf 3:3025–3032. https://doi.org/10.1016/j.promfg.2015.07.847
Hergeth S (2016) Automation trust in conditional automated driving systems: approaches to operationalization and design. Ph.D. Thesis. Saint Mary’s University, Technische Universitat Chemnitz
Hoff KA, Bashir M (2015) Trust in automation: integrating empirical evidence on factors that influence trust. Hum Factors 57(3):407–434. https://doi.org/10.1177/0018720814547570
Inagaki T (2010) Traffic systems as joint cognitive systems: issues to be solved for realizing human-technology coagency. Cogn Technol Work 12(2):153–162. https://doi.org/10.1007/s10111-010-0143-6
Inagaki T, Sheridan TB (2018) A critique of the SAE conditional driving automation definition, and analyses of options for improvement. Cogn Technol Work. https://doi.org/10.1007/s10111-018-0471-5
Johnson PC (2014) Extension of Nakagawa & Schielzeth’s R2GLMM to random slopes models. Methods Ecol Evol 5(9):944–946
Körber M, Baseler E, Bengler K (2018) Introduction matters: manipulating trust in automation and reliance in automated driving. Appl Ergon 66:18–31
Lee JD, See KA (2004) Trust in automation: designing for appropriate reliance. Hum Factors 46(1):50–80. https://doi.org/10.1518/hfes.46.1.50_30392
Lee JD, Moeckli J, Brown TL, Roberts SC, Schwarz C, Yekhshatyan L, Nadler E, Liang Y, Victor T, Marshall D, Davis C (2013) Distraction detection and mitigation through driver feedback. Report DOT HS 811 547A, Washington, D.C
Merat N, Jamson AH, Lai FCH, Daly M, Carsten OMJ (2014) Transition to manual: driver behaviour when resuming control from a highly automated vehicle. Transp Res Part F Traffic Psychol Behav 27:274–282. https://doi.org/10.1016/j.trf.2014.09.005
Millot P (2015) Situation awareness: is the glass half empty or half full? Cogn Technol Work 17(2):169–177. https://doi.org/10.1007/s10111-015-0322-6
Mirman D, Dixon JA, Magnuson JS (2008) Statistical and computational models of the visual world paradigm: growth curves and individual differences. J Mem Lang 59(4):475–494
Muir BM, Moray N (1996) Trust in automation. Part II. Experimental studies of trust and human intervention in a process control simulation. Ergonomics 39(3):429–460. https://doi.org/10.1080/00140139608964474
Nakagawa S, Schielzeth H (2013) A general and simple method for obtaining R 2 from generalized linear mixed-effects models. Methods Ecol Evol 4(2):133–142
NHTSA (2017) Automated driving systems 2.0. Retrieved from https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/13069a-ads2.0_090617_v9a_tag.pdf.. Accessed 10 Sept 2018
Parasuraman R, Riley V (1997) Humans and automation: use, misuse, disuse, abuse. Hum Factors J Hum Factors Ergon Soc 39(2):230–253. https://doi.org/10.1518/001872097778543886
Payre W, Cestac J, Delhomme P (2016) Fully automated driving: impact of trust and practice on manual control recovery. Hum Factors 58(2):229–241. https://doi.org/10.1177/0018720815612319
Price MA, Venkatraman V, Gibson M, Lee J, Mutlu B (2016) Psychophysics of trust in vehicle control algorithms. Paper presented at the SAE Technical Paper Series
R Core Team (2017) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
SAE (2016) Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles (surface vehicle recommended practice: superseding J3016 Jan 2014). In: J3016
Sanders T, Oleson KE, Billings DR, Chen JYC, Hancock PA (2011) A model of human–robot trust: theoretical model development. Proc Hum Factors Ergon Soc Annu Meet 55(1):1432–1436. https://doi.org/10.1177/1071181311551298
Schwarz C, Keum C, Brown T, Gaspar J (2016) Transfer from highly automated to manual control: performance & trust. Retrieved from Iowa City, IA. http://safersim.nads-sc.uiowa.edu/final_reports/UI_4_Y2_FinalReport.pdf. Accessed 10 Sept 2018
Schwarz C, Brown TL, Gaspar JG, Keum C (2017) Transfer from highly automated to manual control: performance and trust. Paper presented at the Enhanced Safety of Vehicles
Victor TW, Harbluk JL, Engström JA (2005) Sensitivity of eye-movement measures to in-vehicle task difficulty. Transp Res Part F Traffic Psychol Behav 8(2):167–190. https://doi.org/10.1016/j.trf.2005.04.014
Wickens CD, Xu X (2002) Automation trust, reliability and attention. Technical Report AHFD-02-14/MAAD-02-2, Urbana-Champaign
Wickens CD, Hollands JG, Banbury S, Parasuraman R (2015) Engineering psychology & human performance. Psychology Press, Hove