Driving automation is intended to improve safety while minimizing driver mental workload. Misunderstanding among drivers and inconsistencies among manufacturers regarding the nuanced functionalities and limitations of ADAS hinder this goal. Violation of driver expectations about how an ADAS performs, particularly during critical moments, could inadvertently increase workload at critical times. The aim of the present study was to assess mental workload, indexed by the P300 event-related potential (ERP) as drivers engaged novel driving automation features. P300 amplitude indicated significantly higher workload when monitoring two versus one ADAS components.
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References
1.
BaldwinCarryl L.FreemanFredrick G.CoyneJoseph T. (2004). Mental Workload as a Function of Road Type and Visibility: Comparison of Neurophysiological, Behavioral, and Subjective Indices. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 48(19), 2309-2313.
2.
BatesD.MächlerM.BolkerB.WalkerS. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1-48.
3.
BrainardD. H. (1997) The Psychophysics Toolbox, Spatial Vision10:433-436.
4.
CabrallC. D.ErikssonA.DregerF.HappeeR.de WinterJ. (2019). How to keep drivers engaged while supervising driving automation? A literature survey and categorisation of six solution areas. Theoretical issues in ergonomics science, 20(3), 332-365.
5.
ChangC. Y.HsuS. H.Pion-TonachiniL.JungT. P. (2018). Evaluation of artifact subspace reconstruction for automatic EEG artifact removal. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1242-1245). IEEE.
6.
DelormeA.MakeigS. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics. Journal of Neuroscience Methods, 134(1), 9-21.
7.
De WinterJ. C.HappeeR.MartensM. H.StantonN. A. (2014). Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence. Transportation research part F: traffic psychology and behaviour, 27, 196-217.
8.
FowlerB. (1994). P300 as a measure of workload during a simulated aircraft landing task. Human Factors, 36(4), 670-683.
9.
GoldsteinA.SpencerK. M.DonchinE. (2002). The influence of stimulus deviance and novelty on the P300 and novelty P3. Psychophysiology, 39(6), 781-790.
10.
HohnsbeinJoachimMichaelFalkensteinHoormannJorg. (1995). Effects of attention and time-pressure on P300 subcomponents and implications for mental workload research. Biological Psychology, 40(1-2), 73-81.
11.
JasperH.H. (1958) The Ten-Twenty Electrode System of the International Federation. Electroencephalography and ClinicalNeurophysiology, 10, 371-375
12.
KleinerMBrainardDPelliD2007, “What’s new in Psychtoolbox-3?” Perception 36 ECVP Abstract Supplement.
13.
KuznetsovaA.BrockhoffP. B.ChristensenR. H. B. (2016). lmerTest: Tests in linear mixed effects models. R package version 2.0-32. Retrieved from https://CRAN.R-project.org/package=lmerTest.
14.
LenthR. (2016). Least-Squares Means: The R Package lsmeans. Journal of Statistical Software, 69(1), 1-33.
15.
LiR.PrincipeJ. C. (2006). Blinking artifact removal in cognitive EEG data using ICA. In 2006 International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 5273-5276). IEEE.
16.
LohaniM.PayneB. R.StrayerD. L. (2019). A review of psychophysiological measures to assess cognitive states in real-world driving. Frontiers in human neuroscience, 13, 57.
17.
Lopez-CalderonJ.LuckS.2010. ERPLAB. UC-Davis Center for Mind & Brain. MATLAB. (2017). version 9.3.0.713579 (R2017b). Natick, Massachusetts: The MathWorks Inc.
NylenAshley B.ReyesMichelle L.RoeCheryl A.McGeheeDaniel V. (2019). Impacts on Driver Perceptions in Initial Exposure to ADAS Technologies. Transportation Research Record, 2673(10), 354-360.
20.
R Core Team (2015). R: A language and environment for statistical computing. Vienna, Austria: R Foundations for Statistical Computing. Retrieve from www.R-project.org/.
21.
UllspergerPeterGabrieleFreudeErdmannUdo. (2001). Auditory probe sensitivity to mental workload changes-an event-related potential study. International Journal of Psychophysiology, 40(3), 201-209.
22.
WickensC. D. (2008). Multiple resources and mental workload. Human factors, 50(3), 449-455.
23.
WickensC. D.LiH.SantamariaA.SebokA.SarterN. B. (2010). Stages and levels of automation: An integrated meta-analysis. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 54, No. 4, pp. 389-393). Sage CA: Los Angeles, CA: Sage Publications.
24.
WinterB. (2013). Linear models and linear mixed effects models in R with linguistic applications. arXiv preprint arXiv:1308.5499.
