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Acceptance of Artificial Intelligence in Cars: A Survey Approach

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HCI International 2021 - Late Breaking Papers: Multimodality, eXtended Reality, and Artificial Intelligence (HCII 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13095))

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Abstract

This exploratory descriptive survey analyzes the acceptance of different automated systems used in partly and fully autonomous cars, and whether there is a difference between the level of acceptance for someone’s own use and desire for others to use them. The survey reports answers from 199 respondents to an online questionnaire run on Amazon Mechanical Turk (Amazon MTurk). The majority of respondents express high or very high acceptance of partly automated systems; however, when it comes to full automation, the acceptance rate drops significantly. Moreover, the acceptance rate for roughly half of the systems does not differ significantly for the respondent’s own use and use by others.

This document has been generated on 2021/11/02 13:47:01, with R version 3.4.1 (2017-06-30), on x86_64-w64-mingw32. We thank the Yale School of Management for financial support.

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Notes

  1. 1.

    For example, self-driving cars sense their environment by using a number of different sensor sets and localization techniques, as well as validation and verification systems. An advanced control system then interprets the information from those systems to identify the appropriate driving behavior of the car.

  2. 2.

    The NHTSA [35] defines different levels of autonomy from non-autonomous to fully autonomous cars. In level 0 to level 2 cars, drivers are fully in control of driving. Level 2 cars, however, already include marginally autonomous systems, such as adaptive cruise control, lane departure warning and traffic sign recognition. In level 3 cars, drivers do not need to monitor the road but have to intervene occasionally. For level 4 and level 5 cars, human interventions are not necessary. Driving decision processes are carried out independently by the car, which makes decisions on the basis of various sensory data and predetermined and self-learning algorithms.

  3. 3.

    Data, methods and questions are available upon request.

  4. 4.

    The number of participants holding a bachelor’s degree corresponds closely to the number reported for the population in the United States of America. According to the U.S. Census Bureau [48] Current Population Survey, about 35.0% of people 25 years and older have a bachelor’s degree.

  5. 5.

    We refrain from assigning the individual systems to the NHTSA automation levels. Categorizing the systems used in the survey according to specific levels of automation, is only of limited value as the level of automation of a vehicle depends on the combination of and collaboration between the systems. For example, a car classified as Level 1 by the NHTSA takes over either longitudinal or lateral control while a car classified as Level 2 takes over longitudinal and lateral control in specific use cases at the same time.

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Author information

Authors and Affiliations

  1. Institute for Digital Economics, Hamburg University of Technology, 21079, Hamburg, Germany

    Christina Strobel

  2. School of Management, Yale University, New Haven, CT, 06511, USA

    Jason Dana

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  1. Christina Strobel
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Correspondence to Christina Strobel .

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Editors and Affiliations

  1. University of Creteand Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

  2. The Open University of Japan, Chiba, Japan

    Masaaki Kurosu

  3. U.S. Army Research Laboratory, Aberdeen Proving Ground, MD, USA

    Jessie Y. C. Chen

  4. U.S. Army Combat Capabilities Development Command Soldier Center, Orlando, FL, USA

    Gino Fragomeni

  5. Smart Future Initiative, Frankfurt am Main, Hessen, Germany

    Norbert Streitz

  6. Faculty of Arts and Science, Kyushu University, Fukuoka, Japan

    Shin'ichi Konomi

  7. Siemens (United States), Princeton, NJ, USA

    Helmut Degen

  8. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Stavroula Ntoa

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Strobel, C., Dana, J. (2021). Acceptance of Artificial Intelligence in Cars: A Survey Approach. In: Stephanidis, C., et al. HCI International 2021 - Late Breaking Papers: Multimodality, eXtended Reality, and Artificial Intelligence. HCII 2021. Lecture Notes in Computer Science(), vol 13095. Springer, Cham. https://doi.org/10.1007/978-3-030-90963-5_42

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  • DOI: https://doi.org/10.1007/978-3-030-90963-5_42

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