A Wizard of Oz Field Study to Understand Non-Driving-Related Activities, Trust, and Acceptance of Automated Vehicles

Henrik Detjen, Bastian Pfleging, Stefan Schneegass

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Understanding user needs and behavior in automated vehicles (AVs) while traveling is essential for future in-vehicle interface and service design. Since AVs are not yet market-ready, current knowledge about AV use and perception is based on observations in other transportation modes, interviews, or surveys about the hypothetical situation. In this paper, we close this gap by presenting real-world insights into the attitude towards highly automated driving and non-driving-related activities (NDRAs). Using a Wizard of Oz AV, we conducted a real-world driving study (N = 12) with six rides per participant during multiple days. We provide insights into the users’ perceptions and behavior. We found that (1) the users’ trust a human driver more than a system, (2) safety is the main acceptance factor, and (3) the most popular NDRAs were being idle and the use of the smartphone.
Original languageEnglish
Title of host publication12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery, Inc
Pages19–29
ISBN (Print)9781450380652
DOIs
Publication statusPublished - 21 Sep 2020

Keywords

  • Automation Trust
  • Travel Time Use
  • Autonomous Vehicles
  • Field Study
  • User Acceptance
  • Travel-Based Multitasking
  • Wizard of Oz Experiment
  • Highly Automated Driving
  • Non-Driving-Related Activities
  • Real-World Driving
  • Robo-Taxis

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  • Cite this

    Detjen, H., Pfleging, B., & Schneegass, S. (2020). A Wizard of Oz Field Study to Understand Non-Driving-Related Activities, Trust, and Acceptance of Automated Vehicles. In 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 19–29). Association for Computing Machinery, Inc. https://doi.org/10.1145/3409120.3410662