Development of the Questionnaire on the Acceptance of Automated Driving (QAAD): Data-driven models for Level 3 and Level 5 automated driving

Automated driving comes with many promises like zero traffic casualties that are, however, only realizable given their technological development and public acceptance for wide-spread deployment. To investigate the potential acceptance, the authors developed a new data-driven questionnaire focusing on drivers and barriers of the anticipated possible (non-)adoption of automated driving (AD). Therefore, the authors conducted a cross-sectional questionnaire study with 725 respondents (351 female, 374 male) ranging from 18 to 96 years. The authors applied exploratory and confirmatory factor analyses and structural equation modeling, to pursue the overarching goal to develop the QAAD questionnaire (short and long version for SAE Level 3 (L3) and 5 (L5) AD). Hence, the authors identified the three latent factors PRO (positive aspects), CON (negative aspects), and NDRTs (non-driving related tasks) of L3 (short: 9 items; long: 16) and L5 (short: 11, long: 17), respectively. Additionally, the authors queried general questions on AD (8 items) and extracted the two factors Early Adoption/Pro AD and Sustainability. The findings and the goodness-of-fit indices suggest data-driven models for L3 and L5 automated driving and on general aspects focusing on early adoption and sustainability in the context of AD. They can be applied in future research settings, in particular, in (quasi-)experimental L3 and L5 AD studies and in population surveys on AD. The evidence of the presented study should be validated and compared to other questionnaires on AD in different countries around the globe.

• Record URL:
  https://doi.org/10.1016/j.trf.2021.09.011
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S1369847821002151
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13698478
• Supplemental Notes:
  • © 2021 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Weigl, Klemens
  • Schartmüller, Clemens
  • Riener, Andreas
  • Steinhauser, Marco
• Publication Date: 2021-11

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 42-59
• Serial:
  • Transportation Research Part F: Traffic Psychology and Behaviour
  • Volume: 83
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1369-8478
  • Serial URL: http://www.sciencedirect.com/science/journal/13698478

Subject/Index Terms

• TRT Terms: Acceptance; Attitudes; Automobile drivers; Autonomous vehicles; Level 3 driving automation; Level 5 driving automation
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01788724
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 18 2021 12:12PM