When will most cars be able to drive fully automatically? Projections of 18,970 survey respondents

When fully automated cars will be widespread is a question that has attracted considerable attention from futurists, car manufacturers, and academics. This paper aims to poll the public’s expectations regarding the deployment of fully automated cars. In 15 crowdsourcing surveys conducted between June 2014 and January 2019, the authors obtained answers from 18,970 people in 128 countries regarding when they think that most cars will be able to drive fully automatically in their country of residence. The median reported year was 2030. The later the survey date, the smaller the percentage of respondents who reported that most cars would be able to drive fully automatically by 2020, with 15–22% of the respondents providing this estimate in the surveys conducted between 2014 and 2016 versus 3–5% in the 2018 surveys. Respondents who completed multiple surveys were more likely to revise their estimate upward (39.4%) than downward (35.3%). Correlational analyses showed that people from more affluent countries and people who have heard of the Google Driverless Car (Waymo) or the Tesla Autopilot reported a significantly earlier year. Finally, the authors made a comparison between the crowdsourced respondents and respondents from a technical university who answered the same question; the median year reported by the latter group was 2040. The authors conclude that over the course of 4.5 years the public has moderated its expectations regarding the penetration of fully automated cars but remains optimistic compared to what experts currently believe.

• Record URL:
  https://doi.org/10.1016/j.trf.2019.05.008
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S1369847818307642
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13698478
• Supplemental Notes:
  • © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Bazilinskyy, Pavlo
  • 0000-0001-9565-8240
  • Kyriakidis, Miltos
  • Dodou, Dimitra
  • de Winter, Joost
• Publication Date: 2019-7

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: pp 184-195
• Serial:
  • Transportation Research Part F: Traffic Psychology and Behaviour
  • Volume: 64
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1369-8478
  • Serial URL: http://www.sciencedirect.com/science/journal/13698478

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Correlation analysis; Crowdsourcing; Forecasting; Level 5 driving automation; Surveys
• Subject Areas: Highways; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01707568
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 5 2019 2:20PM