Drivers’ Age and Automated Vehicle Explanations

Qiaoning Zhang, Xi Jessie Yang and Lionel P. Robert
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Qiaoning Zhang: School of Information, University of Michigan, Ann Arbor, MI 48109, USA
Xi Jessie Yang: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Lionel P. Robert: School of Information, University of Michigan, Ann Arbor, MI 48109, USA

Sustainability, 2021, vol. 13, issue 4, 1-19

Abstract: Automated vehicles (AV) have the potential to benefit our society. Providing explanations is one approach to facilitating AV trust by decreasing uncertainty about automated decision-making. However, it is not clear whether explanations are equally beneficial for drivers across age groups in terms of trust and anxiety. To examine this, we conducted a mixed-design experiment with 40 participants divided into three age groups (i.e., younger, middle-age, and older). Participants were presented with: (1) no explanation, or (2) explanation given before or (3) after the AV took action, or (4) explanation along with a request for permission to take action. Results highlight both commonalities and differences between age groups. These results have important implications in designing AV explanations and promoting trust.

Keywords: trust in AVs; human-machine interface; artificial intelligence transparency; older drivers; automated driving; AV explanation; explainable artificial intelligence; driver’s age (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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