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Design of (Semi-)Autonomous Vehicles: Perceptions of the People in Sweden

Published online by Cambridge University Press:  26 May 2022

L. Rosenholm ,
P. Goswami  and
S. Jagtap
L. Rosenholm
Affiliation:
Blekinge Institute of Technology, Sweden
P. Goswami*
Affiliation:
Blekinge Institute of Technology, Sweden
S. Jagtap
Affiliation:
Blekinge Institute of Technology, Sweden
*
prashant.goswami@bth.se

Abstract

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The field of autonomous vehicles is gaining wide recognition in the industry, academia as well as social media. However, there is a lack of knowledge on expectations of people regarding this topic. To this end, this paper analyses extant research on perceptions of people in various countries about semi-autonomous and autonomous vehicles. Secondly, based on the findings of this analysis, we developed a questionnaire to gauge the perceptions of the people in Sweden regarding such vehicles. The findings have important implications for the design of AVs in Sweden, and possibly other countries.

Keywords

autonomous vehiclesartificial intelligence (AI)ethicsuser-centred design
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1719 - 1726
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), 2022.

References

Anderson, James M., Kalra, Nidhi, Stanley, Karlyn D., Sorensen, Paul, Samaras, Constantine, and Oluwatola, Tobi A., Autonomous Vehicle Technology: A Guide for Policymakers. Santa Monica, CA: RAND Corporation, 2016. https://www.rand.org/pubs/research_reports/RR443-2.htmlGoogle Scholar
Bjelfvenstam, J. (2018). Vägen till självkörande fordon-introduktion. Slutbetänkande av Utredningen om självkörande fordon på väg [The road to self-driving vehicles-Introduction. Final report of The inquiry about self-driving vehicles on the road].Google Scholar
Arrias, Björn, Elmqvist, Anna-Lena, Skymning, Anna Ferner, Larsson, Peter, Malmstig, Jonas, Mörsell, Annacarin, Nilsson, Niclas, Olars, Henrik, Stenlund, Olof, Wärnfeldt, Yvonne, and Öhgren, Per. (2014), “Autonom körning”.Google Scholar
Bowden, A., Fox-Rushby, J. A., Nyandieka, L., & Wanjau, J. (2002). Methods for pre-testing and piloting survey questions: illustrations from the KENQOL survey of health-related quality of life. Health policy and planning, 17(3), 322–330.CrossRefGoogle ScholarPubMed
Robson, Colin and McCartan, Kieran. (2017), “Real world research 4th edition”, pp. 248–249.Google Scholar
Michaelsen, David and Torkelsson, Emil. (2019), “Ethical perspectives legislations influence on the system development process of autonomous vehicles”.Google Scholar
Waclawski, Eugene. (2012), “How I use it: Survey monkey”Google Scholar
Rajna, George. (2015), “Weak AI, strong AI and superintelligence”.Google Scholar
Graham, P. A. (2017). Thomson's Trolley Problem. J. Ethics & Soc. Phil., 12, 168.Google Scholar
Howard, D., & Dai, D. (2014, January). Public perceptions of self-driving cars: The case of Berkeley, California. In Transportation research board 93rd annual meeting (Vol. 14, No. 4502, pp. 1–16).Google Scholar
Hulse, L. M., Xie, H., & Galea, E. R. (2018). Perceptions of autonomous vehicles: Relationships with road users, risk, gender and age. Safety science, 102, 1–13.Google Scholar
Mijwil, Maad M.. (2015), “History of artificial intelligence”Google Scholar
Male, T. (2016). Analysing qualitative data. Doing research in education: Theory and practice, 177–191.Google Scholar
Morales-Alvarez, W., Sipele, O., Léberon, R., Tadjine, H. H., & Olaverri-Monreal, C. (2020). Automated driving: a literature review of the take over request in conditional automation. Electronics, 9(12), 2087.CrossRefGoogle Scholar
Ng, G. W., & Leung, W. C. (2020). Strong artificial intelligence and consciousness. Journal of Artificial Intelligence and Consciousness, 7(01), 63–72.Google Scholar
Penmetsa, P., Adanu, E. K., Wood, D., Wang, T., & Jones, S. L. (2019). Perceptions and expectations of autonomous vehicles–A snapshot of vulnerable road user opinion. Technological Forecasting and Social Change, 143, 9–13.Google Scholar
Pieroni, A., Scarpato, N., & Brilli, M. (2018). Industry 4.0 revolution in autonomous and connected vehicle a non-conventional approach to manage big data.Google Scholar
Ponto, J. (2015). Understanding and evaluating survey research. Journal of the advanced practitioner in oncology, 6(2), 168.Google ScholarPubMed
Randolph, J. (2009). A guide to writing the dissertation literature review. Practical Assessment, Research, and Evaluation, 14(1), 13.Google Scholar
Schoettle, B., & Sivak, M. (2014). A survey of public opinion about autonomous and self-driving vehicles in the US, the UK, and Australia. University of Michigan, Ann Arbor, Transportation Research Institute.Google Scholar
Talpes, E., Sarma, D. D., Venkataramanan, G., Bannon, P., McGee, B., Floering, B., et al. . (2020). Compute solution for tesla's full self-driving computer. IEEE Micro, 40(2), 25–35.CrossRefGoogle Scholar
Waser, M. R. (2009, June). What is artificial general intelligence? Clarifying the goal for engineering and evaluation. In Proceedings of the Second Conference on Artificial General Intelligence (pp. 186–191).Google Scholar
Xu, W. (2020). From automation to autonomy and autonomous vehicles: Challenges and opportunities for human-computer interaction. Interactions, 28(1), 48–53.Google Scholar