Reliable and transparent in-vehicle agents lead to higher behavioral trust in conditionally automated driving systems

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dc.contributor.authorTaylor, Skyeen
dc.contributor.authorWang, Manhuaen
dc.contributor.authorJeon, Myounghoonen
dc.date.accessioned2023-06-29T15:15:10Zen
dc.date.available2023-06-29T15:15:10Zen
dc.date.issued2023-05en
dc.description.abstractTrust is critical for human-automation collaboration, especially under safety-critical tasks such as driving. Providing explainable information on how the automation system reaches decisions and predictions can improve system transparency, which is believed to further facilitate driver trust and user evaluation of the automated vehicles. However, what the optimal level of transparency is and how the system communicates it to calibrate drivers' trust and improve their driving performance remain uncertain. Such uncertainty becomes even more unpredictable given that the system reliability remains dynamic due to current technological limitations. To address this issue in conditionally automated vehicles, a total of 30 participants were recruited in a driving simulator study and assigned to either a low or a high system reliability condition. They experienced two driving scenarios accompanied by two types of in-vehicle agents delivering information with different transparency types: "what"-then-wait (on-demand) and "what + why" (proactive). The on-demand agent provided some information about the upcoming event and delivered more information if prompted by the driver, whereas the proactive agent provided all information at once. Results indicated that the on-demand agent was more habitable, or naturalistic, to drivers and was perceived with faster system response speed compared to the proactive agent. Drivers under the high-reliability condition complied with the takeover request (TOR) more (if the agent was on-demand) and had shorter takeover times (in both agent conditions) compared to those under the low-reliability condition. These findings inspire how the automation system can deliver information to improve system transparency while adapting to system reliability and user evaluation, which further contributes to driver trust calibration and performance correction in future automated vehicles.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.3389/fpsyg.2023.1121622en
dc.identifier.issn1664-1078en
dc.identifier.other1121622en
dc.identifier.pmid37275735en
dc.identifier.urihttp://hdl.handle.net/10919/115578en
dc.identifier.volume14en
dc.language.isoenen
dc.publisherFrontiersen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjecttrusten
dc.subjecttransparencyen
dc.subjectautomated vehiclesen
dc.subjectin-vehicle agentsen
dc.subjectreliabilityen
dc.titleReliable and transparent in-vehicle agents lead to higher behavioral trust in conditionally automated driving systemsen
dc.title.serialFrontiers in Psychologyen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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