Hostname: page-component-84b7d79bbc-2l2gl Total loading time: 0 Render date: 2024-07-31T07:08:00.039Z Has data issue: false hasContentIssue false
  • English
  • Français

ASSESSING THE DRIVER'S RISK PERCEPTION DURING AUTONOMOUS DRIVING

Part of: Human Behaviour and Design

Published online by Cambridge University Press:  11 June 2020

C. Gandrez ,
F. Mantelet ,
A. Aoussat ,
F. Jeremie  and
E. Landel
C. Gandrez*
Affiliation:
Arts et Métiers ParisTech, France Renault, France
F. Mantelet
Affiliation:
Arts et Métiers ParisTech, France
A. Aoussat
Affiliation:
Arts et Métiers ParisTech, France
F. Jeremie
Affiliation:
Renault, France
E. Landel
Affiliation:
Renault, France
*
*clara.gandrez@ensam.eu

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Advanced Driver-Assistance Systems were created to address the driver's failures. All these ADAS are a part of the evolution of the vehicles towards whole automation. To complete its launch in the automotive market, autonomous vehicles have to pass safety tests to acquire the consumers’ trust. To receive the approval of the public, the self-driving car has to take into account the human feeling. The risk perceived by the driver is one of the new emotional form to integrate at the validation plan. The purpose of this study is to examine the perception of the risk of a self-driving car's driver.

Keywords

autonomous vehiclerisk feelingcognitive capabilitieshuman behaviouruser experience
Type
Article
Information
Proceedings of the Design Society: DESIGN Conference , Volume 1 , May 2020 , pp. 1435 - 1444
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

Blasiis, M.D. et al. (2017), “Risk perception assessment using a driving simulator: a gender analysis”, Conference Paper.Google Scholar
Borowsky, A., Shinar, D. and Oron-Gilad, T. (2007), “Age, skill, and hazard perception in driving”, Proceedings of the Fourth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design. https://doi.org/10.1016/j.aap.2010.02.001CrossRefGoogle Scholar
Brell, T., Philipsen, R. and Ziefle, M. (2019), “sCARy! Risk perceptions in autonomous driving: the influence of experience on perceived benefits and barriers”, Risk Analysis, pp 342–357. https://doi.org/10.1111/risa.13190CrossRefGoogle ScholarPubMed
Colombet, F. et al. (2008), “Motion cueing: What is the impact of the driver's behavior?”, Proceedings of the Driving Simulation Conference.Google Scholar
Constant, C. (1989), “La méthodologie Taguchi : introduction à la recherche technologique - gestion fonctionnelle de la qualité ou gestion « off-line »”, Revue de statistique appliquée, Vol. 37, pp. 47–77.Google Scholar
Crundall, D. et al. (2012), “Some hazards are more attractive than others: Drivers of varying experience respond differently to different types of hazard”, Accident Analysis and Prevention, pp. 600–609. https://doi.org/10.1016/j.aap.2011.09.049Google Scholar
Delignières, D. (1993), “Risque préférentiel, risque perçu et prise de risque”, Cognition et performance, pp. 79–102.10.4000/books.insep.1419CrossRefGoogle Scholar
Elander, J., West, R. and French, D. (1993), “Behavioral correlates of individual differences in road-traffic crash risk: an examination of methods and findings”, Psychol. Bull., pp. 279–294. https://doi.org/10.1037/0033-2909.113.2.279Google Scholar
European Commission (2018), Advanced Driver Assistance Systems, pp. 7–27.Google Scholar
Gabaude, C. et al. (2019), “Diagnostic et suivi des capacités visuo-attentionnelles des conducteurs âgés : Développement d'un programme de prévention”.Google Scholar
Glendon, A.I. et al. (1996), “Age and Gender Differences in Perceived Accident Likelihood and Driver Competences”, Risk Analysis, Vol. 16 No. 5, pp. 755–762. https://doi.org/10.1111/j.1539-6924.1996.tb00826.xGoogle Scholar
Harris, C.R. and Jenkins, M. (2006), “Gender Differences in Risk Assessment: Why do Women Take Fewer Risks than Men?”, Judgment and Decision Making, Vol. 1 No. 1.Google Scholar
Lee, C. et al. (2017), “Age differences in acceptance of self-driving cars: A survey of perceptions and attitudes”, International Conference on Human Aspects of IT for the Aged Population, pp. 3–13. https://doi.org/10.1007/978-3-319-58530-7_1CrossRefGoogle Scholar
Lim, P.C., Sheppard, E. and Crundall, D. (2013), “Cross-cultural effects on drivers’ hazard perception”, Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 21, pp. 194–206. https://doi.org/10.1016/j.trf.2013.09.016Google Scholar
Machado-Leon, J. et al. (2016), “Socio-economic and driving experience factors affecting drivers’ perceptions of traffic crash risk”, Transportation Research Part F: Traffic Psychology and Behaviour, pp. 41–51. https://doi.org/10.1016/j.trf.2015.11.010Google Scholar
Osswald, S. et al. (2012), “Predicting information technology usage in the car: towards a car technology acceptance Model”, International Conference on Automotive User Interfaces and Interactive Vehicular Applications. http://doi.org/10.1145/2390256.2390264Google Scholar
Raue, M. et al. (2019), “The Influence of Feelings While Driving Regular Cars on the Perception and Acceptance of Self-Driving Cars”, Risk Analysis, pp. 1–17. https://dx.doi.org/10.1111/risa.13267Google Scholar
Renn, O. (1992), “Concepts of risk: a classification”, Krimsky, S. and Golding, D. (Eds.) Social theories of risk. Westport, CT. London: Praeger, pp. 53–79. https://doi.org/10.18419/opus-7248Google Scholar
SAE (2018), Sae international releases updated visual chart for its “levels of driving automation” standard for self-driving vehicles.Google Scholar
Sassman, M. et al. (2018), “Effets de l'automatisation de la conduite en situations de conduite normales et critiques : de l’étude empirique sur simulateur à la modélisation cognitive”, ISTE OpenScience. https://doi.org/10.21494/ISTE.OP.2018.0294Google Scholar
SCANeR (2018), https://www.avsimulation.fr/solutions/. [Online; accessed April-2019].Google Scholar
Singh, S. (2015), “Critical reasons for crashes investigated in the National Motor Vehicle Crash Causation Survey”, Traffic Safety Facts Crash Stats Report, N° DOT HS 812 115.Google Scholar
Siren, A. and Kjaer, M. (2014), “How is the older road users’ perception of risk constructed?”, Accident Analysis and Prevention, pp. 63–78. https://doi.org/10.1016/j.trf.2011.01.002Google Scholar
Schmidt, M. (2004), Investigating risk perception: a short introduction, pp. 4–8.Google Scholar
Slovic, P., Fischhoff, B. and Liechtenstein, S. (1986), “The psychometric study of risk perceptions”, Covello, V.T., Menkes, J and Mumpower, J. (Eds.) Risk evaluation and management. New York, London: Plenum Press, pp. 3–24. https://doi.org/1794/22518CrossRefGoogle Scholar
Stein, A.C. and Allen, R.W. (1987), “The effects of alcohol on driver decision making and risk taking”, Alcohol, drugs and traffic safety—T86, pp. 177–181. https://doi.org/10.1016/0022-4375%2887%2990093-4Google Scholar
Van der Swaag, M. et al. (2012), “The influence of music on mood and performance while driving”, Ergonomics, pp. 12–22. https://doi.org/10.1080/00140139.2011.638403CrossRefGoogle Scholar
Venkatesh, V. et al. (2003), “User acceptance of information technology: Toward a unified view”, MIS Quarterly, pp. 425–478. https://doi.org/10.2307/30036540CrossRefGoogle Scholar