Restricted accessResearch articleFirst published online 2024-4
How Can the Trust-Change Direction be Measured and Identified During Takeover Transitions in Conditionally Automated Driving? Using Physiological Responses and Takeover-Related Factors
This paper proposes an objective method to measure and identify trust-change directions during takeover transitions (TTs) in conditionally automated vehicles (AVs).
Background
Takeover requests (TORs) will be recurring events in conditionally automated driving that could undermine trust, and then lead to inappropriate reliance on conditionally AVs, such as misuse and disuse.
Method
34 drivers engaged in the non-driving-related task were involved in a sequence of takeover events in a driving simulator. The relationships and effects between drivers’ physiological responses, takeover-related factors, and trust-change directions during TTs were explored by the combination of an unsupervised learning algorithm and statistical analyses. Furthermore, different typical machine learning methods were applied to establish recognition models of trust-change directions during TTs based on takeover-related factors and physiological parameters.
Result
Combining the change values in the subjective trust rating and monitoring behavior before and after takeover can reliably measure trust-change directions during TTs. The statistical analysis results showed that physiological parameters (i.e., skin conductance and heart rate) during TTs are negatively linked with the trust-change directions. And drivers were more likely to increase trust during TTs when they were in longer TOR lead time, with more takeover frequencies, and dealing with the stationary vehicle scenario. More importantly, the F1-score of the random forest (RF) model is nearly 77.3%.
Conclusion
The features investigated and the RF model developed can identify trust-change directions during TTs accurately.
Application
Those findings can provide additional support for developing trust monitoring systems to mitigate both drivers’ overtrust and undertrust in conditionally AVs.
AbeG.SatoK.ItohM. (2018). Driver trust in automated driving systems: The case of overtaking and passing. IEEE Transactions on Human-Machine Systems, 48(1), 85–94. https://10.1109/THMS.2017.2781619.
2.
AyoubJ.YangX. J.ZhouF. (2021). Modeling dispositional and initial learned trust in automated vehicles with predictability and explainability. Transportation research part F: traffic psychology and behaviour, 77, 102–116. https://doi.org/10.1016/j.trf.2020.12.015
3.
Azevedo-SaH.JayaramanS. K.EsterwoodC. T.YangX. J.RobertL. P.TilburyD. M. (2020). Real-time estimation of drivers’ trust in automated driving systems. International Journal of Social Robotics, 13, 1–17. https://doi.org/10.1007/s12369-020-00694-1.
4.
Azevedo-SaH.ZhaoH.EsterwoodC.YangX. J.TilburyD. M.RobertL. P.Jr (2021). How internal and external risks affect the relationships between trust and driver behavior in automated driving systems. Transportation research part C: emerging technologies, 123, 102973. https://doi.org/10.1016/j.trc.2021.102973
BernisonG. G.QuigleyK. S.LozanoD. (2007). Cardiovascular psychophysiology. In CacioppoJ. T.TassinaryL. G.BerntsonG. G. (Eds.) Handbook of Psychophysiology (pp. 182–210). Cambridge: Cambridge University Press.
7.
ChoiJ. K.JiY. G.(2015). Investigating the importance of trust on adopting an autonomous vehicle. International Journal of Human-Computer Interaction,31(10), 692–702. https://doi.org/10.1080/10447318.2015.1070549
8.
ChungW. Y.ChongT. W.LeeB. G. (2019). Methods to detect and reduce driver stress: a review. International journal of automotive technology, 20(5), 1051–1063. https://doi.org/10.1007/s12239-019-0099-3
9.
ColletC.MusicantO. (2019). Associating vehicles automation with drivers functional state assessment systems: A challenge for road safety in the future. Frontiers in human neuroscience, Neurosci, 13, 131. https://doi.org/10.3389/fnhum.2019.00131
10.
DawsonM.SchellA.FilionD. (2016). The Electrodermal System. In CacioppoJ.TassinaryL.BerntsonG. (Eds.) Handbook of Psychophysiology (pp. 217–243). Cambridge: Cambridge University Press. https://doi.org/10.1017/9781107415782.010
11.
DoberenzS.RothW. T.WollburgE.MaslowskiN. I.KimS. (2011). Methodological considerations in ambulatory skin conductance monitoring. International Journal of Psychophysiology, 80(2), 87–95. https://doi.org/10.1016/j.trf.2020.12.015
12.
DuN.YangX. J.ZhouF.(2020a). Psychophysiological responses to takeover requests in conditionally automated driving. Accident Analysis & Prevention, 148, 105804. https://doi.org/10.1016/j.aap.2020.105804
DupreA.VincentS.IaizzoP.A. (2005). Basic ECG Theory, Recordings, and Interpretation. In: IaizzoP.A. (eds) Handbook of Cardiac Anatomy, Physiology, and Devices. Humana Press. https://doi.org/10.1007/978-1-59259-835-9_15
15.
EkmanF.JohanssonM.BligårdL. O.KarlssonM.StrömbergH. (2019). Exploring automated vehicle driving styles as a source of trust information. Transportation research part F: traffic psychology and behaviour,65, 268–279. https://doi.org/10.1016/j.trf.2019.07.026
16.
FagnantD. J.KockelmanK. (2015). Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77, 167–181. https://doi.org/10.1016/j.tra.2015.04.003
17.
GavazzeniJ.WiensS.FischerH. (2008). Age effects to negative arousal differ for self‐report and electrodermal activity. Psychophysiology. 45(1), 148–151. https://doi.org/10.1111/j.1469-8986.2007.00596.x
18.
GoldC.KörberM.LechnerD.BenglerK. (2016). Taking over control from highly automated vehicles in complex traffic situations: the role of traffic density. Human Factors, 58(4),642–652. https://doi.org/10.1177/0018720816634226
19.
GoldC.NaujoksF.RadlmayrJ.BellemH.JaroschO. (2018a). Testing scenarios for human factors research in level 3 automated vehicles. Advances in Intelligent Systems and Computing, 597, 551–559. https://link.springer.com/chapter/10.1007/978-3-319-60441-1_54
GuptaK.HajikaR.PaiY. S.DuenserA.LochnerM.BillinghurstM. (2019). In ai we trust: Investigating the relationship between biosignals, trust and cognitive load in vr. Proceedings of the 25th ACM Symposium on Virtual Reality Software and Technology, 1–10. https://doi.org/10.1145/3359996.3364276
23.
HayesA. F. (2012). PROCESS: A versatile computational tool for observed variable mediation, moderation, and conditional process modeling. http://www.afhayes.com/public/process2012.pdf.
24.
HealeyJ. A.PicardR. W.(2005). Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on intelligent transportation systems, 6(2), 156–166. https://doi.org/10.1109/TITS.2005.848368
25.
HergethS.LorenzL.KremsJ. F.(2017). Prior familiarization with takeover requests affects drivers’ takeover performance and automation trust. Human Factors, 59(3), 457–470. https://doi.org/10.1177/0018720816678714
26.
HergethS.LorenzL.VilimekR.KremsJ. F. (2016). Keep your scanners peeled: Gaze behavior as a measure of automation trust during highly automated driving. Human Factors,58(3), 509–519. https://doi.org/10.1177/0018720815625744
27.
HoffK. A.BashirM. (2015). Trust in automation: Integrating empirical evidence on factors that influence trust. Human Factors,57(3), 407–434. https://doi.org/10.1177/0018720814547570
28.
HolthausenB. E.WintersbergerP.WalkerB. N.RienerA. (2020). Situational Trust Scale for Automated Driving (STS-AD): Development and Initial Validation. In Proceedings of the 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 40–47. https://doi.org/10.1145/3409120.3410637
29.
HowellsF. M.RauchH. L.Ives-DeliperiV. L.HornN. R.SteinD. J. (2014). Mindfulness based cognitive therapy may improve emotional processing in bipolar disorder: pilot ERP and HRV study. Metabolic brain disease, 29(2), 367–375. https://doi.org/10.1007/s11011-013-9462-7
30.
JapkowiczN.ShahM. (2011). Evaluating learning algorithms: a classification perspective. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511921803
31.
JianJ. Y.BisantzA. M.DruryC. G. (2000). Foundations for an empirically determined scale of trust in automated systems. International journal of cognitive ergonomics, 4(1), 53–71. https://doi.org/10.1207/S15327566IJCE0401_04
32.
JinM.LuG.ChenF.ShiX. (2020). How Driving Experience Affect Trust in Automation from Level 3 Automated Vehicles? An Experimental Analysis. Proceedings of the IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 1–6. https://doi.org/10.1109/ITSC45102.2020.9294247
33.
KellerJ. M.GrayM. R.GivensJ. A. (1985). A fuzzy k-nearest neighbor algorithm. IEEE transactions on systems, man, and cybernetics, 15(4), 580–585. https://doi.org/10.1109/TSMC.1985.6313426.
34.
KhawajiA.ZhouJ.ChenF.MarcusN. (2015). Using galvanic skin response (GSR) to measure trust and cognitive load in the text-chat environment. Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, 1989-1994. https://doi.org/10.1145/2702613.2732766
35.
KohnS. C.De VisserE. J.WieseE.LeeY. C.ShawT. H. (2021). Measurement of Trust in Automation: A Narrative Review and Reference Guide. Frontiers in psychology, 12, 604977. https://doi.org/10.3389/fpsyg.2021.604977.
36.
KörberM.(2018a). Theoretical considerations and development of a questionnaire to measure trust in automation. BagnaraS.TartagliaR.AlbolinoS.AlexanderT.FujitaY. (Eds) Proceedings of the 20th Congress of the International Ergonomics Association, 13–30. https://doi.org/10.1007/978-3-319-96074-6_2
37.
KörberM.BaselerE.BenglerK. (2018b). Introduction matters: Manipulating trust in automation and reliance in automated driving. Applied ergonomics, 66, 18–31. https://doi.org/10.1016/j.apergo.2017.07.006
38.
KörberM.PraschL.BenglerK. (2018c). Why do I have to drive now? Post hoc explanations of takeover requests. Human Factors, 60(3), 305–323. https://doi.org/10.1177/0018720817747730
39.
KrausJ.ScholzD.StiegemeierD.BaumannM. (2020). The more you know: trust dynamics and calibration in highly automated driving and the effects of take-overs, system malfunction, and system transparency. Human Factors, 62(5), 718–736. https://doi.org/10.1177/0018720819853
40.
LangP. J.GreenwaldM. K.BradleyM. M.HammA. O. (1993). Looking at pictures: Affective, facial, visceral, and behavioral reactions. Psychophysiology, 30(3), 261–273. https://doi.org/10.1111/j.1469-8986.1993.tb03352.x
41.
Lascurain-AguirrebeñaI.NewhamD. J.Galindez-IbarbengoetxeaX.Casado-ZumetaX.LertxundiA.CritchleyD. J.(2019). Association between sympathoexcitatory changes and symptomatic improvement following cervical mobilisations in participants with neck pain. A double blind placebo controlled trial. Musculoskelet. Musculoskeletal Science and Practice, 42, 90–97. https://doi.org/10.1016/j.msksp.2019.05.001
LiM.HolthausenB. E.StuckR. E.WalkerB. N. (2019). No risk no trust: Investigating perceived risk in highly automated driving. Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 177–185. https://doi.org/10.1145/3342197.3344525
45.
LiuJ.AkashK.MisuT.WuX. (2021). Clustering Human Trust Dynamics for Customized Real-time Prediction. Proceedings of 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 1705–1712. https://doi.org/10.1109/ITSC48978.2021.9565016
46.
MauleA. J.HockeyG. R. J. (1993). State, Stress, and Time Pressure. In: SvensonO.MauleA.J. (eds) Time Pressure and Stress in Human Judgment and Decision Making. Boston: Springer. https://doi.org/10.1007/978-1-4757-6846-6_6
47.
McBrideS. E.RogersW. A.FiskA. D. (2010). Do Younger and Older Adults Differentially Depend on an Automated System?Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 54(2),175–179. https://doi.org/10.1177/154193121005400208
48.
MorrisD. M.ErnoJ. M.PilcherJ. J. (2017). Electrodermal Response and Automation Trust during Simulated Self-Driving Car Use. Proceedings of the Human Factors and Ergonomics Society Annual Meeting,61(1), 1759–1762. https://doi.org/10.1177/1541931213601921
49.
MühlK.StrauchC.GrabmaierC.ReithingerS.HuckaufA.BaumannM. (2020). Get ready for being chauffeured: Passenger’s preferences and trust while being driven by human and automation. Human Factors, 62(8), 1322–1338. https://doi.org/10.1177/0018720819872893
50.
NaujoksF.PuruckerC.NeukumA.WolterS.SteigerR. (2015), Controllability of partially automated driving functions–does it matter whether drivers are allowed to take their hands off the steering wheel?Transportation research part F: traffic psychology and behaviour, 35, 185–198. https://doi.org/10.1016/j.trf.2015.10.022
Perello-MarchJ. R.BurnsC. G.WoodmanR.ElliottM. T.BirrellS. A. (2021). Driver state monitoring: Manipulating reliability expectations in simulated automated driving scenarios. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2021.3050518
53.
PetersenL.RobertL.YangJ.TilburyD. (2019). Situational awareness, driver’s trust in automated driving systems and secondary task performance. SAE International Journal of Connected and Autonomous Vehicles, Forthcoming. http://dx.doi.org/10.2139/ssrn.3345543
RastgooM. N.NakisaB.RakotonirainyA.ChandranV.TjondronegoroD. (2018). A critical review of proactive detection of driver stress levels based on multimodal measurements. ACM Computing Surveys, 51(5), 1–35. https://doi.org/10.1145/3186585
56.
RishI. (2001). An empirical study of the naive bayes classifier. IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence. 3 (22), 41–46.
57.
RosenmanR.TennekoonV.HillL. G. (2011). Measuring bias in self-reported data. International Journal of Behavioural and Healthcare Research, 2(4), 320–332. https://doi.org/10.1504/IJBHR.2011.043414
58.
ScerboA. S.FreedmanL. W.RaineA.DawsonM. E.VenablesP. H. (1992). A major effect of recording site on measurement of electrodermal activity. Psychophysiology, 29(2), 241–246.https://doi.org/10.1111/j.1469-8986.1992.tb01693.x
59.
SchwarzC.GasparJ.BrownT. (2019). The effect of reliability on drivers’ trust and behavior in conditional automation. Cognition, Technology & Work, 21(1), 41–54. https://doi.org/10.1007/s10111-018-0522-y
60.
SeetM.HarvyJ.BoseR.DragomirA.BezerianosA.ThakorN. (2020). Differential Impact of Autonomous Vehicle Malfunctions on Human Trust. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2020.3013278
61.
Society of Automotive Engineers (2018). Taxonomy and Definitions for terms related to on-road motor vehicle automated driving systems for on-road motor vehicles(J3016). SAE International.
62.
TeohE. R.KiddD. G. (2017). Rage against the machine? Google's self-driving cars versus human drivers. Journal of safety research, 63, 57–60. https://doi.org/10.1016/j.jsr.2017.08.008
63.
VogelpohlT.KühnM.HummelT.GehlertT.VollrathM. (2018). Transitioning to manual driving requires additional time after automation deactivation. Transportation research part F: traffic psychology and behaviour, 55, 464–482. https://doi.org/10.1016/j.trf.2018.03.019
64.
WalkerF.WangJ.MartensM. H.VerweyW. B. (2019). Gaze behaviour and electrodermal activity: Objective measures of drivers’ trust in automated vehicles. Transportation research part F: traffic psychology and behaviour, 64, 401–412. https://doi.org/10.1016/j.trf.2019.05.021
65.
WandtnerB.SchömigN.SchmidtG. (2018). Secondary task engagement and disengagement in the context of highly automated driving. Transportation research part F: traffic psychology and behaviour, 58, 253–263. https://doi.org/10.1016/j.trf.2018.06.001
66.
WaytzA.HeafnerJ.EpleyN. (2014). The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle. Journal of Experimental Social Psychology, 52, 113–117. https://doi.org/10.1016/j.jesp.2014.01.005
67.
WintersbergerP.von SawitzkyT.FrisonA. K.RienerA. (2017). Traffic augmentation as a means to increase trust in automated driving systems. Proceedings of the 12th biannual conference on italian sigchi chapter, 1–7. https://doi.org/10.1145/3125571.3125600
68.
YiB.CaoH.SongX.ZhaoS.GuoW.LiM. (2022). How to identify the take-over criticality in conditionally automated driving? An examination using drivers’ physiological parameters and situational factors. Transportation research part F: traffic psychology and behaviour, 85, 161–178. https://doi.org/10.1016/j.trf.2021.12.007
69.
YuB.BaoS.FengF.SayerJ.(2019a). Examination and prediction of drivers’ reaction when provided with V2I communication-based intersection maneuver strategies. Transportation research part C: emerging technologies, 106, 17–28. https://doi.org/10.1016/j.trc.2019.07.007
70.
YuB.ChenY.BaoS.(2019b). Quantifying visual road environment to establish a speeding prediction model: an examination using naturalistic driving data. Accident Analysis & Prevention, 129, 289–298. https://doi.org/10.1016/j.aap.2019.05.011
71.
YuB.BaoS.ZhangY.SullivanJ.FlannaganM. (2021). Measurement and prediction of driver trust in automated vehicle technologies: An application of hand position transition probability matrix. Transportation research part C: emerging technologies, 124, 102957. https://doi.org/10.1016/j.trc.2020.102957
72.
ZhangT.TaoD.QuX.ZhangX.LinR.ZhangW. (2019). The roles of initial trust and perceived risk in public’s acceptance of automated vehicles. Transportation research part C: emerging technologies, 98, 207–220. https://doi.org/10.1016/j.trc.2018.11.018
