Top

Published in:

2021 | OriginalPaper | Chapter

How Are Eye Tracking Patterns in Takeover Situations Related to Complexity, Takeover Quality and Cognitive Model Predictions?

Author : Marlene Susanne Lisa Scharfe-Scherf

Published in: Intelligent System Solutions for Auto Mobility and Beyond

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

In the development of highly automated driving, strong focus is laid on the takeover and the improvement of takeover quality. Some research has shown that the complexity of a traffic situation has an influence on the takeover. However, different approaches towards complexity in driving exist and the topic has so far not been addressed sufficiently. In this study, a differentiation between subjective- and objective complexity is drawn. Their impact on eye movement patterns is evaluated and compared to the resulting takeover quality. Results of a driving simulator study show that objective and subjective complexity have an influence on several eye movement patterns. These eye movement patterns serve as an indicator of the resulting takeover quality. Furthermore, traces of the eye movement patterns are compared to predicted traces of the cognitive model for the takeover task. It can be shown that the cognitive model predicts visual traces in different traffic situations well. In order to support individual drivers during a takeover, it is thus important to consider complexity measurements in the development of cognitive assistance systems. Based on information about the environment and the cognitive model for the takeover task, a cognitive assistance system can be developed. In addition to that, eye tracking information further improves cognitive assistance systems.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Autonomous Vehicle Shuttle in Smart City Testbed

next chapter Automatic Detection and Prediction of the Transition Between the Behavioural States of a Subject Through a Wearable CPS

Ampore, E., Beccuti, M., Collina, S., De Simone, F., Donatelli, S., Tango, F.: Cognitive systems in intelligent vehicles - a new frontier for autonomous driving. In: Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - ICINCO 2014 (2014). https://doi.org/10.5220/0005160808170822

Baumann, M., Krems, J.F.: Situation awareness and driving: a cognitive model. In: Modelling Driver Behaviour in Automotive Environments, pp. 253–265 (2007)

Braunagel, C., Rosenstiel, W., Kasneci, E.: Ready for take-over? A new driver assistance system for an automated classification of driver take-over readiness. IEEE Intell. Transp. Syst. Mag. 9(4), 10–22 (2017). https://doi.org/10.1109/MITS.2017.2743165CrossRef

Cao, J., Wang, H.: Vision navigation for driver cognitive model in ACT-R architecture. In: 9th IEEE International Conference on Cognitive Informatics (ICCI 2010). https://doi.org/10.1109/COGINF.2010.5599773

Crundall, D.E., Underwood, G., Chapman, P.R.: How much do novice drivers see? The effects of demand on visual search strategies in novice and experienced drivers. In: Eye Guidance in Reading and Scene Perception, pp. 395–417. Elsevier (1998)

Damböck, D., Bengler, K.: Übernahmezeiten beim hochautomatisierten fahren, In: 5. tagung fahrerassistenz (2012)

Damböck, D., Farid, M., Tönert, L., Bengler, K.: Übernahmezeiten beim hochautoma-tisierten fahren. Tagung Fahrerassistenz. München 15, 16 (2012)

Endsley, M.R.: From here to autonomy: lessons learned from human–automation research. Hum. Factors 59(1), 5–27 (2017)CrossRef

Eriksson, A., Stanton, N.A.: Takeover time in highly automated vehicles: noncritical transitions to and from manual control. Hum. Factors 59(4), 689–705 (2017)CrossRef

Estes, S., Burns, K., Helleberg, J., Long, K., Stein, J., Pollack, M.:. Digital copilot: cognitive assistance for pilots. In: Proceedings of 2016 AAAI Fall Symposium Series (2016)

Gross, U.: Bestimmung von Schwierigkeitsgraden in einer zu entwickelnden Versuchsumgebung, In: Diplomarbeit - Humbold Universität zu Berlin, Germany (2004)

Haerem, T., Rau, D.: The influence of degree of expertise and objective task complexity on perceived task complexity and performance. J. Appl. Psychol. 92(5), 1320 (2007)CrossRef

Hart, S.G.: NASA task load index (TLX). In: From NASA - Ames Research Center (1986)

Hayhoe, M.: Advances in relating eye movements and cognition. Infancy 6(2), 267–274 (2004)CrossRef

Hayhoe, M., Ballard, D.: Eye movements in natural behavior. Trends Cogn. Sci. 9(4), 188–194 (2004)CrossRef

Horrey, W.J., Wickens, C.D., Consalus, K.P.: Modeling drivers’ visual attention allocation while interacting with in-vehicle technologies. J. Exp. Psychol. Appl. 12(2), 67 (2006)CrossRef

Kerschbaum, P., Lorenz, L., Bengler, K.: A transforming steering wheel for highly automated cars, In: 2015 IEEE Intelligent Vehicles Symposium (IV) (2015)

Klimke, J., Themann, P., Klas, C., Eckstein, L.: Definition of an embedded driver model for driving behavior prediction within the deserve platform, In: Proceedings of 2014 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS XIV) (2014). https://doi.org/10.1109/SAMOS.2014.6893231

Lasota, P.A., Shah, J.A.: Analyzing the effects of human-aware motion planning on close-proximity human–robot collaboration [PMID: 25790568]. Hum. Factors 57(1), 21–33 (2015). https://doi.org/10.1177/0018720814565188

Louw, T., Merat, N., Jamson, A.: Engaging with highly automated driving: to be or not to be in the loop? In: Proceedings of 8th International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, At Salt Lake City, Utah, USA (2015). https://doi.org/10.13140/RG.2.1.2788.9760

Markkula, G., Boer, E., Romano, R., Merat, N.: Sustained sensorimotor control as intermittent decisions about prediction errors: computational framework and application to ground vehicle steering. Biol. Cybern. 112(3), 181–207 (2018)CrossRef

Melcher, V., Rauh, S., Diederichs, F., Widlroither, H., Bauer, W.: Take-over requests for automated driving. Procedia Manuf. 3, 2867–2873 (2015)CrossRef

Moore, K., Gugerty, L.: Development of a novel measure of situation awareness: the case for eye movement analysis, In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting. SAGE Publications Sage, Los Angeles (2010)

Naujoks, F., Wiedemann, K., Schömig, N., Jarosch, O., Gold, C.: Expert-based control-lability assessment of control transitions from automated to manual driving. MethodsX 5, 579–592 (2018)CrossRef

Paxion, J., Galy, E., Berthelon, C.: Overload depending on driving experience and situation complexity: which strategies faced with a pedestrian crossing? Appl. Ergon. 51, 343–349 (2015)CrossRef

Radlmayr, J., Gold, C., Lorenz, L., Farid, M., Bengler, K.: How traffic situations and non-driving related tasks affect the take-over quality in highly automated driving. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 58(1), 2063–2067 (2014), https://doi.org/10.1177/1541931214581434

SAE-International: Surface vehicle recommended practice. Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. In: SAE International (2018)

Salvucci, D.D.: Modeling driver behavior in a cognitive architecture. Hum. Factors 48(2), 362–380 (2006)CrossRef

Scharfe, M., Wiese, S., Russwinkel, N.: A cognitive model of the takeover procedure in highly automated driving [Manuscript submitted for publication at Human Factors] (2020)

Scharfe, M., Russwinkel, N.: A cognitive model for understanding the takeover in highly automated driving depending on the objective complexity of non-driving related tasks and the traffic environment. In: Proceedings of the 41th Annual Cognitive Science Society Meeting (2019a). https://cognitivesciencesociety.org/wp-content/uploads/2019/07/cogsci19_proceedings-8July2019-compressed.pdf

Scharfe, M., Russwinkel, N.: The individual in the loop - the influence of familiarity and complexity during a takeover in highly automated driving [Manuscript submitted for publication at Transportation Research Part F.] (2019b)

Scharfe, M., Zeeb, K., Russwinkel, N.: The impact of situational complexity and familiarity on takeover quality in uncritical highly automated driving scenarios. Information 11, 115 (2020)CrossRef

Schlindwein, S.L., Ison, R.: Human knowing and perceived complexity: implications for systems practice. Emerg. Complex. Organ. 6(3), 27–32 (2004)

Sheridan, T.B.: Human–robot interaction: status and challenges. Hum. Factors 58(4), 525–532 (2016)CrossRef

TobiiAB: How do Tobii eye trackers work? - learn more with Tobii Pro (2015). https://www.tobiipro.com/learn-and-support/learn/eye-tracking-essentials/how-do-tobii-eye-trackers-work/. Accessed 11 Feb 2020

WIVW-GmbH: Driving simulation and SILAB (2014). https://wivw.de/en/silab. Accessed 9 July 2019

Zeeb, K., Buchner, A., Schrauf, M.: What determines the take-over time? An integrated model approach of driver take-over after automated driving. Accid. Anal. Prev. 78, 212–221 (2015)CrossRef

Title: How Are Eye Tracking Patterns in Takeover Situations Related to Complexity, Takeover Quality and Cognitive Model Predictions?
Author: Marlene Susanne Lisa Scharfe-Scherf
Publisher: Springer International Publishing
Book: Intelligent System Solutions for Auto Mobility and Beyond
Print ISBN: 978-3-030-65870-0

Electronic ISBN: 978-3-030-65871-7

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-65871-7_12

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner