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Driver’s Age and Automated Vehicle Explanations

dc.contributor.authorZhang, Qiaoning
dc.contributor.authorYang, X. Jessie
dc.contributor.authorRobert, Lionel + "Jr"
dc.date.accessioned2021-02-05T12:33:33Z
dc.date.available2021-02-05T12:33:33Z
dc.date.issued2021-02-05
dc.identifier.citationZhang, Q., Yang, X. J. and Robert, L. P. (2021). Driver’s Age and Automated Vehicle Explanations, Sustainability, accepteden_US
dc.identifier.urihttps://hdl.handle.net/2027.42/166295en
dc.identifier.urihttps://doi.org/10.3390/su13041948
dc.description.abstractAutomated Vehicles (AV) have the potential to benefit our society. However, a lack of trust is a major barrier to the adoption of AVs. Providing explanations is one approach to facilitating AV trust by decreasing uncertainty about AV's decision-making and action. However, explanations might increase drivers’ cognitive effort and anxiety. Because of differences in cognitive ability across age groups, it is not clear whether explanations are equally beneficial for drivers across age groups in terms of trust, effort, 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 suggest that the explanations provided before AV take actions produced the highest trust and lowest effort for all drivers regardless of age group. The request-for-permission condition led to the highest trust and lowest effort only for older drivers. Younger drivers had the lowest anxiety and effort under the AV-explanation-after-action condition; however, this condition produced the highest level of anxiety and effort in middle-age and older drivers, respectively. These results have important implications in designing AV explanations and promoting trust.en_US
dc.description.sponsorshipUniversity of Michigan Mcityen_US
dc.language.isoen_USen_US
dc.publisherSustainabilityen_US
dc.subjectAutomated Vehiclesen_US
dc.subjectexplanationsen_US
dc.subjectAutomated Vehicles explanationsen_US
dc.subjectautonomous vehicle explanationsen_US
dc.subjectautonomous vehicleen_US
dc.subjectautonomous vehicle trusten_US
dc.subjectAutomated Vehicles trusten_US
dc.subjectHuman-Machine Interfaceen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectArtificial Intelligence Transparencyen_US
dc.subjectOlder Driversen_US
dc.subjectAutomated Drivingen_US
dc.subjectArtificial Intelligence Explainabilityen_US
dc.subjectDriver's Ageen_US
dc.subjectAutomationen_US
dc.subjectself driving caren_US
dc.subjectExplainable Artificial Intelligenceen_US
dc.subjectArtificial Intelligence trusten_US
dc.subjectadvance driving automationen_US
dc.subjectAdvanced Driver Assistance Systemsen_US
dc.subjectAutomated Driving Systemsen_US
dc.subjecthuman computer interactionen_US
dc.subjecthuman robot interactionen_US
dc.titleDriver’s Age and Automated Vehicle Explanationsen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelInformation and Library Science
dc.subject.hlbtoplevelSocial Sciences
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumInformation, School ofen_US
dc.contributor.affiliationumCollege of Engineeringen_US
dc.contributor.affiliationumRobotics Instituteen_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/166295/1/Zhang et al. 2021 [Final paper]-sustainability-0202.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/166295/3/Zhang et al. 2021.pdfen
dc.identifier.doihttps://dx.doi.org/10.7302/218
dc.identifier.doi10.3390/su13041948
dc.identifier.sourceSustainabilityen_US
dc.identifier.orcid0000-0002-1410-2601en_US
dc.description.filedescriptionDescription of Zhang et al. 2021 [Final paper]-sustainability-0202.pdf : Preprint
dc.description.depositorSELFen_US
dc.identifier.name-orcidRobert, Lionel P.; 0000-0002-1410-2601en_US
dc.working.doi10.7302/218en_US
dc.owningcollnameInformation, School of (SI)


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