The phenomenon of behavioral adaptation induced by continuous use of vehicular automation is a very intriguing subject for researchers in the field of Human-Automation Interaction. Some authors believe that better tailoring of system design to driver needs and to the task requirements can improve human performance and mitigate the process of loss of vigilance. Nevertheless, the literature on this subject is far from reaching a consensus, so that the causal or aggravating factors for such process are not yet fully known. Based on this issue, the goal of this study was to list the most prevalent factors in the literature for the loss of driver’s vigilance capabilities during the use of autonomous vehicles (HAVs - Highly Automated Vehicles). To achieve the proposed goal, a meta-analytical study was conducted from a systematic literature review, followed by statistical significance tests. The results reinforce the idea that elements for communication and control of attention stimuli directed to the driver are of paramount importance for the occurrence of the studied phenomenon, so, we suggest further deeper studies on particular HMI application for HAVs.
Get full access to this article
View all access options for this article.
References
1.
AllahyarM.BecicE.ChappellS.FisherD.LohrenzM.MonkC.PhilipsB. (2016). The Evolving Role of Automation in Transportation: Human Factors Lessons Learned from the Different Modes. Proceedings of the Human Factors and Ergonomics SocietyAnnual Meeting, 60(1), 1971–1975.
2.
AzizT.HoriguchiY.SawaragiT. (2013). An empirical investigation of the development of driver’s mental model of a Lane Departure Warning system while driving. IFAC Proceedings Volumes, 46(15), 461–468.
3.
BanksV. A.StantonN. A. (2015). Discovering Driver-vehicle Coordination Problems in Future Automated Control Systems: Evidence from Verbal Commentaries. Procedia Manufacturing, 3, 2497–2504.
4.
BanksV. A.StantonN. A. (2016). Keep the driver in control: Automating automobiles of the future. Applied Ergonomics, 53, 389–395.
5.
BanksV. A.StantonN. A.HarveyC. (2014). Sub-systems on the road to vehicle automation: Hands and feet free but not “mind” free driving. Safety Science, 62, 505–514.
6.
BardinL. (1977). L’analyse de contenu (Vol. 69). Presses universitaires de France.
7.
BashiriB.MannD. D. (2013). Drivers’Mental Workload In Agricultural Semi-Autonomous Vehicles. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 57(1), 1795–1799.
8.
BeggiatoM.PereiraM.PetzoldtT.KremsJ. (2015). Learning and development of trust, acceptance and the mental model of ACC. Alongitudinal on-road study. Transportation Research Part F: Traffic Psychology and Behaviour, 35, 75–84.
9.
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.
10.
BlairK.SandryJ.RiceS. (2012). An Expansion of System Wide Trust Theory Using In-Vehicle Automation. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 56(1), 1679–1683.
11.
BrookhuisK. A.de WaardD. (2010). Monitoring drivers’mental workload in driving simulators using physiological measures. Accident Analysis & Prevention, 42(3), 898–903.
ChienS.-Y.LewisM.HergethS.Semnani-AzadZ.SycaraK. (2015). Cross- Country Validation of a Cultural Scale in Measuring Trust in Automation. Proceedings of the Human Factors and Ergonomics SocietyAnnual Meeting, 59(1), 686–690.
14.
ConoverW. J. (1971). Practical Nonparametric Statistics. Wiley (Vol. 22). Wiley.
15.
de WinterJ. C. F.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.
DesmondP. A.MatthewsG. (1997). Implications of task-induced fatigue effects for in-vehicle countermeasures to driver fatigue. Accident; Analysis and Prevention, 29(4), 515–23. Retrieved from
18.
FletcherL.ZelinskyA. (2009). Driver Inattention Detection based on Eye Gaze—Road Event Correlation. The International Journal of Robotics Research, 28(6), 774–801.
19.
GoldC.KörberM.HohenbergerC.LechnerD.BenglerK. (2015). Trust in Automation – Before and After the Experience of Take-over Scenarios in a Highly Automated Vehicle. Procedia Manufacturing, 3, 3025–3032.
20.
GonçalvesJ.BenglerK. (2015). Driver State Monitoring Systems– Transferable Knowledge Manual Driving to HAD. Procedia Manufacturing, 3, 3011–3016.
21.
HergethS.LorenzL.VilimekR.KremsJ. F. (2016). Keep Your Scanners Peeled. Human Factors, 58(3), 509–519.
22.
JamsonS.Rudin-brownC. M. (2013). Behavioural Adaptation and Road Safety: Theory, Evidence and Action. Ergonomics, (Book, Whole), 467.
23.
JonesL. M. (1992). Effect of Repeated Function Allocation and Reliability on Automation-Induced Monitoring Inefficiency. University of Central Florida.
24.
LarssonA. F. L.KircherK.Andersson HultgrenJ. (2014). Learning from experience:Familiarity with ACC and responding to a cut-in situation in automated driving. Transportation Research Part F: Traffic Psychology and Behaviour, 27, 229–237.
25.
LouwT.KountouriotisG.CarstenO.MeratN. (2015). DRIVER INATTENTION DURING VEHICLE AUTOMATION: HOW DOES DRIVER ENGAGEMENT AFFECT RESUMPTION OF CONTROL? In 4thInternational Driver Distraction and Inattention Conference (pp. 9–11). Sidney. Retrieved from http://eprints.whiterose.ac.uk/91858/
26.
LuZ.de WinterJ. C. F. (2015). AReview and Framework of Control Authority Transitions in Automated Driving. Procedia Manufacturing, 3, 2510–2517.
27.
LyonsJ. B.HoN. T.KoltaiK. S.MasequesmayG.SkoogM.CacanindinA.JohnsonW. W. (2016). Trust-Based Analysis of an Air Force Collision Avoidance System. Ergonomics in Design: The Quarterly of Human Factors Applications, 24(1), 9–12.
28.
MaR.KaberD. B. (2007). Situation awareness and driving performance in a simulated navigation task. Ergonomics, 50(8), 1351–1364.
29.
MadiganR.LouwT.DziennusM.GraindorgeT.OrtegaE.GraindorgeM.MeratN. (2016). Acceptance of Automated Road Transport Systems (ARTS): An Adaptation ofthe UTAUT Model. Transportation Research Procedia, 14, 2217–2226.
30.
MaltzM.ShinarD. (2007). Imperfect in-vehicle collision avoidance warning systems can aid distracted drivers. Transportation Research Part F: Traffic Psychology and Behaviour, 10(4), 345–357.
31.
MeratN.WaardD.Human factors implications of vehicle automation: current understanding and future directions. Research & Transportation part F, v. 27, 2014.
32.
MulderM.AbbinkD. A.BoerE. R. (2012). Sharing control with haptics: seamless driver support from manual to automatic control. Human Factors, 54(5), 786–98. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/23156623
33.
National Highway Traffic SafetyAdministration.Federal Automated Vehicles Policy: Accelerating the Next Revolution in Roadway Safey. Washington. 2016.
34.
NeubauerC.MatthewsG.SaxbyD. (2012). The Effects of Cell Phone Use andAutomation on Driver Performance and Subjective State in Simulated Driving. Proceedings of the Human Factors and Ergonomics SocietyAnnual Meeting, 56(1), 1987–1991.
35.
ParasuramanR.ManzeyD. H. (2010). Complacency and bias in human use of automation: an attentional integration. Human Factors, 52(3), 381–410. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/21077562
36.
PayreW.CestacJ.DelhommeP. (2016). Fully Automated Driving. Human Factors, 58(2), 229–241.
37.
RitterF. E.BaxterG. D.ChurchillE. F. (2014). Cognition: Memory, Attention, and Learning. In Foundations for Designing User-Centered Systems (pp. 123–164). London: Springer London.
38.
Rudin-BrownC. M.ParkerH. A. (2004). Behavioural adaptation to adaptive cruise control (ACC): implications for preventive strategies. Transportation Research Part F: Traffic Psychology and Behaviour, 7(2), 59–76.
39.
RuscioD.CiceriM. R.BiassoniF. (2015). How does a collision warning system shape driver’s brake response time? The influence of expectancy and automation complacency on real-life emergency braking. Accident Analysis & Prevention, 77, 72–81.
40.
SaffarianM.de WinterJ. C. F.HappeeR. (2012). Automated Driving: Human-Factors Issues and Design Solutions. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 56(1), 2296–2300.
41.
SchaeferK. E.ChenJ. Y. C.SzalmaJ. L.HancockP. A. (2016). AMeta-Analysis of Factors Influencing the Development of Trust in Automation. Human Factors, 58(3), 377–400.
42.
SchiebenA.GriescheS.HesseT.FrickeN.BaumannM. (2014). Evaluation of three different interaction designs for an automatic steering intervention. Transportation Research Part F: Traffic Psychology and Behaviour, 27, 238–251.
43.
ShenS.NeyensD. M. (2014). Assessing drivers’performance when automated driver support systems fail with different levels of automation. Proceedings of the Human Factors and Ergonomics SocietyAnnual Meeting, 58(1), 2068–2072.
44.
SheridanT. B. (1999). Human supervisory control of aircraft, rail and highway vehicles. Transactions of the Institute of Measurement and Control, 21(4–5), 191–201.
45.
SheridanT. B.ParasuramanR. (2005). Human-Automation Interaction. Reviews of Human Factors and Ergonomics, 1(1), 89–129.
46.
Society of Automotive Engineers [SAE] (2014). Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems (Standard No. J3016). SAE International. Retrieved from: <http://standards.sae.org/j3016_201401/>.
47.
StockertS.RichardsonN. T.LienkampM. (2015). Driving in an Increasingly Automated World – Approaches to Improve the Driver-automation Interaction. Procedia Manufacturing, 3, 2889–2896.
48.
StrandN.NilssonJ.KarlssonI. C. M.NilssonL. (2014). Semi-automated versus highly automated driving in critical situations caused by automation failures. Transportation Research Part F: Traffic Psychology and Behaviour, 27, 218–228.
49.
TeraiH.OkudaH.HitomiK.BandoT.MiyajimaC.HirayamaT.… TakedaK. (2015). An Experimental Study on the Difference in Drivers’Decision-making Behavior During Manual and Supported Driving. Procedia Manufacturing, 3, 3136–3141.
50.
van LoonR. J.MartensM. H. (2015). Automated Driving and its Effect on the Safety Ecosystem: How do Compatibility Issues Affect the Transition Period?Procedia Manufacturing, 3, 3280–3285.
51.
WeyerJ.FinkR. D.AdeltF. (2015). Human–machine cooperation in smart cars. An empirical investigation of the loss-of-control thesis. Safety Science, 72, 199–208.
52.
WickensC. D.CleggB. A.VieaneA. Z.SebokA. L. (2015). Complacency and Automation Bias in the Use of Imperfect Automation. Human Factors, 57(5), 728–739.
53.
XiongH.BoyleL. N.MoeckliJ.DowB. R.BrownT. L. (2012). Use Patterns Among EarlyAdopters of Adaptive Cruise Control. Human Factors, 54(5), 722–733.
54.
YoungM. S.StantonN. A. (2000). Journey’s End: Will Vehicle Automation Make Skilled Drivers Redundant?Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 44(1), 89–92.
55.
YoungM. (2012). Ergonomics Issues with Advanced Driver Assistance Systems (ADAS). In Automotive Ergonomics (pp. 55–76). CRC Press.
56.
YoungM. S.StantonN. A. (2002). Malleable attentional resources theory: a new explanation for the effects of mental underload on performance. Human Factors, 44(3), 365–375.
57.
ZeebK.BuchnerA.SchraufM. (2015). What determines the take-over time? An integrated model approach of driver take-over after automated driving. Accident Analysis & Prevention, 78, 212–221.
