Behavioral Changes to Repeated Takeovers in Highly Automated Driving: Effects of the Takeover-Request Design and the Nondriving-Related Task Modality

Abstract

Objective:

We investigated drivers’ behavior and subjective experience when repeatedly taking over their vehicles’ control depending on the design of the takeover request (TOR) and the modality of the nondriving-related task (NDRT).

Background:

Previous research has shown that taking over vehicle control after highly automated driving provides several problems for drivers. There is evidence that the TOR design and the NDRT modality may influence takeover behavior and that driver behavior changes with more experience.

Method:

Forty participants were requested to resume control of their simulated vehicle six times. The TOR design (auditory or visual-auditory) and the NDRT modality (auditory or visual) were varied. Drivers’ takeover behavior, gaze patterns, and subjective workload were recorded and analyzed.

Results:

Results suggest that drivers change their behavior to the repeated experience of takeover situations. An auditory TOR leads to safer takeover behavior than a visual-auditory TOR. And with an auditory TOR, the takeover behavior improves with experience. Engaging in the visually demanding NDRT leads to fewer gazes on the road than the auditory NDRT. Participants’ fixation duration on the road decreased over the three takeovers with the visually demanding NDRT.

Conclusions:

The results imply that (a) drivers change their behavior to repeated takeovers, (b) auditory TOR designs might be preferable over visual-auditory TOR designs, and (c) auditory demanding NDRTs allow drivers to focus more on the driving scene.

Application:

The results of the present study can be used to design TORs and determine allowed NDRTs in highly automated driving.

Keywords

auditory displays driver behavior eye tracking human-automation interaction interface evaluation vehicle automation visual displays

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References

Agrawal

Wright

T. J.

Samuel

Zilberstein

Fisher

D. L.

(2017). Effects of a change in environment on the minimum time to situation awareness in transfer of control scenarios. Transportation Research Record, 2663(1), 126–133. https://doi.org/10.3141/2663-16

Backhaus

Brandenburg

(2014). Temporal dynamics of emotional activation in man-machine interaction. i-com, 13(1), 63–69. https://doi.org/10.1515/icom-2014-0009

Bainbridge

(1983). Ironies of automation. Automatica, 19(6), 775–779. https://doi.org/10.1016/0005-1098(83)90046-8

Brandenburg

Backhaus

(2016). The dynamics of film-induced affect and its effect on the interaction with tablet PCs. Behaviour & Information Technology, 35(5), 410–421. https://doi.org/10.1080/0144929X.2016.1151076

Byers

J. C.

Bittner

A. C.

Hill

S. G.

(1989). Traditional and raw task load index (TLX) correlations: Are paired comparisons necessary? Advances in Industrial Ergonomics and Safety, 481–485.

Carsten

Lai

F. C. H.

Barnard

Jamson

A. H.

Merat

(2012). Control task substitution in semiautomated driving: Does it matter what aspects are automated? Human Factors: The Journal of the Human Factors and Ergonomics Society, 54(5), 747–761. https://doi.org/10.1177/0018720812460246

Chen

Choi

H. J.

(2008). Visual attention and eye movements, 1–14. Retrieved from https://www.ics.uci.edu/~majumder/vispercep/paper08/visualattention.pdf

Cohen

(1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.

Damböck

Farid

Tönert

Bengler

(2012). Übernahmezeiten beim hochautomatisierten Fahren [Takeover times for highly automated driving]. In Tagung Fahrerassistenz (p. 16). München.

10.

Endsley

M. 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.

11.

Endsley

M. R.

Kiris

E. O.

(1995). The out-of-the-loop performance problem and level of control in automation. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(2), 381–394. https://doi.org/10.1518/001872095779064555

12.

Epple

Roche

Brandenburg

(2018). The sooner the better: Drivers’ reactions to two-step take-over requests in highly automated driving. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 62(1), 1883–1887.

13.

Feldhütter

Gold

Schneider

Bengler

(2017). How the duration of automated driving influences take-over performance and gaze behavior. In Schlick

C. M.

Duckwitz

Flemisch

Frenz

Kuz

Mertens

Mütze-Niewöhner

(Eds.), Advances in ergonomic design of systems, products and processes (pp. 309–318). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-53305-5_22

14.

Forster

Naujoks

Neukum

Huestegge

(2017). Driver compliance to take-over requests with different auditory outputs in conditional automation. Accident Analysis & Prevention, 109, 18–28. https://doi.org/10.1016/j.aap.2017.09.019

15.

Gold

Körber

Lechner

Bengler

(2016). Taking over control from highly automated vehicles in complex traffic situations: The role of traffic density. Human Factors, 58(4), 642–652.

16.

Hart

S. G.

(2006). Nasa-Task Load Index (NASA-TLX); 20 Years Later. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 50(9), 904–908.

17.

Hart

S. G.

Staveland

L. E.

(1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In Hancock

Meshkati

(Eds.), Human Mental Workload (pp. 139–183). Amsterdam: Elsevier. Retrieved from http://linkinghub.elsevier.com/retrieve/pii/S0166411508623869

18.

Hendy

K. C.

Hamilton

K. M.

Landry

L. N.

(1993). Measuring subjective workload: When is one scale better than many? Human Factors, 35(4), 579–601.

19.

Hergeth

Lorenz

Vilimek

Krems

J. F.

(2016). Keep your scanners peeled: Gaze behavior as a measure of automation trust during highly automated driving. Human Factors: The Journal of the Human Factors and Ergonomics Society, 58(3), 509–519. https://doi.org/10.1177/0018720815625744

20.

Hoedemaeker

Brookhuis

K. A.

(1998). Behavioural adaptation to driving with an adaptive cruise control (ACC). Transportation Research Part F: Traffic Psychology and Behaviour, 1(2), 95–106. https://doi.org/10.1016/S1369-8478(98)00008-4

21.

ISO/TS. ISO/TS, 14198 § (2012).

22.

Kuehn

Vogelpohl

Vollrath

(2017). Takeover times in highly automated driving (Level 3; paper 17-0027). Washington, DC: National Highway Traffic Safety Administration.

23.

Lakens

(2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4. https://doi.org/10.3389/fpsyg.2013.00863

24.

Naujoks

Mai

Neukum

(2014, July). The effect of urgency of take-over requests during highly automated driving under distraction conditions. In Proceedings of the 5th International Conference on Applied Human Factors and Ergonomics. Krakow.

25.

Parasuraman

Sheridan

T. B.

Wickens

C. D.

(2000). A model for types and levels of human interaction with automation. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions On, 30(3), 286–297.

26.

Petermeijer

Bazilinskyy

Bengler

de Winter

(2017). Take-over again: Investigating multimodal and directional TORs to get the driver back into the loop. Applied Ergonomics, 62, 204–215. https://doi.org/10.1016/j.apergo.2017.02.023

27.

Politis

Brewster

Pollick

(2015a). 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 (pp. 3–10). Nottingham: ACM Press. https://doi.org/10.1145/2799250.2799262

28.

Politis

Brewster

Pollick

(2015b). To beep or not to beep? Comparing abstract versus language-based multimodal driver displays. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 3971–3980). Seoul, Korea: ACM Press. https://doi.org/10.1145/2702123.2702167

29.

Radlmayr

Gold

Lorenz

Farid

Bengler

(2014). How traffic situations and non-driving related tasks affect the take-over quality in highly automated driving. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 58(1), 2063–2067. https://doi.org/10.1177/1541931214581434

30.

SAE International. (2018). Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles (J3016).

31.

Walch

Lange

Baumann

Weber

(2015). Autonomous driving: Investigating the feasibility of car-driver handover assistance. In Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 11–18). Nottingham: ACM Press. https://doi.org/10.1145/2799250.2799268

32.

Wandtner

Schömig

Schmidt

(2018). Effects of non-driving related task modalities on takeover performance in highly automated driving. Human Factors: The Journal of the Human Factors and Ergonomics Society, 60(6), 870–881. https://doi.org/10.1177/0018720818768199

33.

Wickens

C. D.

(2002). Multiple resources and performance prediction. Theoretical Issues in Ergonomics Science, 3(2), 159–177. https://doi.org/10.1080/14639220210123806

34.

Wright

T. J.

Agrawal

Samuel

Wang

Zilberstein

Fisher

D. L.

(2017). Effects of alert cue specificity on situation awareness in transfer of control in Level 3 automation. Transportation Research Record: Journal of the Transportation Research Board, 2663, 27–33.

35.

Yamani

Horrey

W. J.

(2018). A theoretical model of human-automation interaction grounded in resource allocation policy during automated driving. Journal of Human Factors and Ergonomics, 5(3), 225–239.

36.

Zeeb

Buchner

Schrauf

(2015). What determines the take-over time? An integrated model approach of driver take-over after automated driving. Accident Analysis & Prevention, 78, 212–221. https://doi.org/10.1016/j.aap.2015.02.023

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