Comfort with varying levels of human supervision in self-driving cars: determining factors in Europe

While numerous studies have investigated attitudes towards self-driving cars in general, less research attention has been focused on individuals' comfort with the presence (or absence) of third-party human supervision of this automation, and its potential correlates. In the present study we perform a secondary analysis of pre-existing data from The European Commission’s Eurobarometer 92.1, a large-scale European survey (n = 27565) of expectations and concerns of connected and automated driving. By comparing responses to three levels of human supervision in self-driving cars, the authors aim to identify changes in the importance of predictors of comfort with automation. The authors find considerable heterogeneity in both individual attitudes, as well as in country-level attitudes in their descriptive analysis. The authors find a trend of decreasing comfort as external human supervision is reduced, although this effect differs between countries. The authors then investigate potential drivers of self-reported comfort with varying levels of external human supervision in a regression framework. Gender differences get stronger with decreasing supervision, suggesting a possible resolution to conflicting evidence in previous studies. Following this, the authors fit an ordinal random forest model to derive variable importance metrics, which enable the authors to compare the changing role predictor variables might play in shaping self-reported comfort, depending on varying levels of third-party supervision. Data privacy is highlighted as an important variable, regardless of level of supervision. The authors’ findings provide confirmation for previous literature in a large sample, while also uncovering a number of novel associations, providing guidance for future policy-making and research efforts.

• Record URL:
  https://doi.org/10.1016/j.ijtst.2022.08.001
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S2046043022000764
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/20460430
• Supplemental Notes:
  • © 2022 Tongji University and Tongji University Press. Publishing Services by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Kaszas, Daniel
  • Roberts, Adam Charles
• Publication Date: 2023-9

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; Maps; References; Tables;
• Pagination: pp 809-821
• Serial:
  • International Journal of Transportation Science and Technology
  • Volume: 12
  • Issue Number: 3
  • Publisher: Elsevier
  • ISSN: 2046-0430
  • Serial URL: http://www.sciencedirect.com/science/journal/20460430

Subject/Index Terms

• TRT Terms: Attitudes; Autonomous vehicles; Data privacy; Driving automation levels; Psychological trust; Remote control
• Geographic Terms: European Union
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01858335
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 20 2022 2:33PM