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A DISCRETE-EVENT SIMULATION MODEL FOR DRIVER PERFORMANCE ASSESSMENT: APPLICATION TO AUTONOMOUS VEHICLE COCKPIT DESIGN OPTIMIZATION

Part of: Engineering Design Practice

Published online by Cambridge University Press:  11 June 2020

I. Iuskevich ,
A. M. Hein ,
K. Amokrane-Ferka ,
A. Doufene  and
M. Jankovic
I. Iuskevich*
Affiliation:
CentraleSupélec, France IRT SystemX, France
A. M. Hein
Affiliation:
CentraleSupélec, France
K. Amokrane-Ferka
Affiliation:
IRT SystemX, France
A. Doufene
Affiliation:
IRT SystemX, France
M. Jankovic
Affiliation:
CentraleSupélec, France
*
*[email protected]

Abstract

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The latest advances in the design of vehicles with the adaptive level of automation pose new challenges in the vehicle-driver interaction. Safety requirements underline the need to explore optimal cockpit architectures with regard to driver cognitive and perceptual workload, eyes-off-the-road time and situation awareness. We propose to integrate existing task analysis approaches into system architecture evaluation for the early-stage design optimization. We built the discrete-event simulation tool and applied it within the multi-sensory (sight, sound, touch) cockpit design industrial project.

Keywords

model-based engineeringdesign optimisationergonomicshuman-machine interfaceautonomous vehicle
Type
Article
Information
Proceedings of the Design Society: DESIGN Conference , Volume 1 , May 2020 , pp. 2521 - 2530
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2020. Published by Cambridge University Press

References

Aldrich, T.B., Szabo, S.M. and Bierbaum, C.R. (1989), “The Development and Application of Models to Predict Operator Workload During System Design”, In: McMillan, G.R., Beevis, D., Salas, E., Strub, M.H., Sutton, R. and Van Breda, L. (Eds.), Applications of Human Performance Models to System Design, Springer US, Boston, MA, pp. 65–80. https://doi.org/10.1007/978-1-4757-9244-7_5CrossRefGoogle Scholar
Bellet, T. et al. (2011), “Human Driver Modelling and Simulation into a Virtual Road Environment”, In: Cacciabue, P.C., Hjälmdahl, M., Luedtke, A. and Riccioli, C. (Eds.), Human Modelling in Assisted Transportation, Springer Milan, Milano, pp. 251–262. https://doi.org/10.1007/978-88-470-1821-1_27CrossRefGoogle Scholar
Boy, G.A. (1998), “Cognitive function analysis for human-centered automation of safety-critical systems”, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems – CHI ‘98, presented at the the SIGCHI conference, ACM Press, Los Angeles, California, United States, pp. 265–272. https://doi.org/10.1145/274644.274682CrossRefGoogle Scholar
Feuerstack, S., Lüdtke, A. and Osterloh, J.-P. (2007), “A Tool for Easing the Cognitive Analysis of Design Prototypes of Aircraft Cockpit Instruments: The Human Efficiency Evaluator”, Proceedings of the European Conference on Cognitive Ergonomics 2015, ACM, New York, NY, USA, pp. 22:1–22:8. https://doi.org/10.1145/2788412.2788434CrossRefGoogle Scholar
Flemisch, F.O. et al. (2014), “Towards cooperative guidance and control of highly automated vehicles: H-Mode and Conduct-by-Wire”, Ergonomics, Vol. 57 No. 3, pp. 343–360. https://doi.org/10.1080/00140139.2013.869355CrossRefGoogle ScholarPubMed
Green, P.A. (2017), “Motor Vehicle Driver Interfaces”, In: The Wiley Handbook of Human Computer Interaction, John Wiley & Sons, Ltd, pp. 843–888. https://doi.org/10.1002/9781118976005.ch40CrossRefGoogle Scholar
Kontogiannis, T. (2005), “Integration of task networks and cognitive user models using coloured Petri nets and its application to job design for safety and productivity”, Cognition, Technology & Work, Vol. 7 No. 4, pp. 241–261. https://doi.org/10.1007/s10111-005-0010-zCrossRefGoogle Scholar
Korosec, K. (2019), “Waymo to customers: ‘Completely driverless Waymo cars are on the way’”, TechCrunch, 10 October, Available at: http://social.techcrunch.com/2019/10/09/waymo-to-customers-completely-driverless-waymo-cars-are-on-the-way/ (accessed 15.11.2019).Google Scholar
Laughery, R. et al. (2000), “Task Network Modeling and the Micro Saint Family of Tools”, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Vol. 44 No. 6, pp. 721–724. https://doi.org/10.1177/154193120004400649CrossRefGoogle Scholar
North, R.A. and Riley, V.A. (1989), “W/INDEX: A Predictive Model of Operator Workload”, In: McMillan, G.R., Beevis, D., Salas, E., Strub, M.H., Sutton, R. and Van Breda, L. (Eds.), Applications of Human Performance Models to System Design, Springer US, Boston, MA, pp. 81–89. https://doi.org/10.1007/978-1-4757-9244-7_6CrossRefGoogle Scholar
Parasuraman, R. and Wickens, C.D. (2008), “Humans: Still Vital After All These Years of Automation”, Human Factors, Vol. 50 No. 3, pp. 511–520. https://doi.org/10.1007/978-88-470-1821-1_27CrossRefGoogle ScholarPubMed
Pettitt, M., Burnett, G.E. and Stevens, A. (2007), “An extended keystroke level model (KLM) for predicting the visual demand of in-vehicle information systems”, CHI, available at: https://doi.org/10.1145/1240624.1240852CrossRefGoogle Scholar
Scerbo, M. (2008), “Adaptive automation”, In: Parasuraman, R. and Rizzo, M. (Eds.), Neuroergonomics: The Brain at Work, Vol. 3, Oxford University Press, p. 239.Google Scholar
Wang, J.-S., Knipling, R.R. and Goodman, M.J. (1996), “The role of driver inattention in crashes: New statistics from the 1995 Crashworthiness Data System”, 40th Annual Proceedings of the Association for the Advancement of Automotive Medicine, Vol. 377, p. 392.Google Scholar
Wickens, C.D. (2002), “Multiple resources and performance prediction”, Theoretical Issues in Ergonomics Science, Vol. 3 No. 2, pp. 159–177. https://doi.org/10.1080/14639220210123806CrossRefGoogle Scholar
Wittmann, M. et al. (2006), “Effects of display position of a visual in-vehicle task on simulated driving”, Applied Ergonomics, Vol. 37 No. 2, pp. 187–199. https://doi.org/10.1016/j.apergo.2005.06.002CrossRefGoogle ScholarPubMed
Wortelen, B., Unni, A., Rieger, J.W. and Lüdtke, A. (2016), “Towards the integration and evaluation of online workload measures in a cognitive architecture”, 2016 7th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), pp. 11–16. https://doi.org/10.1109/CogInfoCom.2016.7804516CrossRefGoogle Scholar