Context-Adaptive Management of Drivers’ Trust in Automated Vehicles

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dc.contributor.author Azevedo-Sa, Hebert
dc.contributor.author Jayaraman, Suresh
dc.contributor.author Yang, X. Jessie
dc.contributor.author Robert, Lionel + "Jr"
dc.contributor.author Tilbury, Dawn
dc.date.accessioned 2020-09-19T09:50:06Z
dc.date.available 2020-09-19T09:50:06Z
dc.date.issued 2020-09-19
dc.identifier.citation Azevedo-Sa, H., Jayaraman, S., Esterwood, C., Yang, X. J., Robert, L. P. and Tilbury, D. (2020). Context-Adaptive Management of Drivers’ Trust in Automated Vehicles, IEEE Robotics and Automation Letters (RA-L), (pdf), DOI:10.1109/LRA.2020.3025736. en_US
dc.identifier.uri http://hdl.handle.net/2027.42/162571
dc.description.abstract Automated vehicles (AVs) that intelligently interact with drivers must build a trustworthy relationship with them. A calibrated level of trust is fundamental for the AV and the driver to collaborate as a team. Techniques that allow AVs to perceive drivers’ trust from drivers’ behaviors and react accordingly are, therefore, needed for context-aware systems designed to avoid trust miscalibrations. This letter proposes a framework for the management of drivers’ trust in AVs. The framework is based on the identification of trust miscalibrations (when drivers’ undertrust or overtrust the AV) and on the activation of different communication styles to encourage or warn the driver when deemed necessary. Our results show that the management framework is effective, increasing (decreasing) trust of undertrusting (overtrusting) drivers, and reducing the average trust miscalibration time periods by approximately 40%. The framework is applicable for the design of SAE Level 3 automated driving systems and has the potential to improve the performance and safety of driver–AV teams. en_US
dc.description.sponsorship U.S. Army CCDC/GVSC en_US
dc.description.sponsorship Automotive Research Center en_US
dc.description.sponsorship National Science Foundation en_US
dc.language.iso en_US en_US
dc.publisher IEEE RA-L en_US
dc.subject Intelligent Transportation Systems en_US
dc.subject Social Human-Robot Interaction en_US
dc.subject Human Factors and Human-in-the- Loop en_US
dc.subject Automated Vehicles en_US
dc.subject Automated vehicles trust en_US
dc.subject Automated cars en_US
dc.subject robot trust en_US
dc.subject self driving cars en_US
dc.subject real time trust estimation en_US
dc.subject trustworthy robotics en_US
dc.subject trust miscalibrations en_US
dc.subject overtrusting technology en_US
dc.subject overtrusting en_US
dc.subject undertrusting en_US
dc.subject automated driving en_US
dc.subject automated driving systems en_US
dc.subject SAE Level 3 en_US
dc.subject trust miscalibrations en_US
dc.subject undertrusting technology en_US
dc.subject technology trust en_US
dc.subject driver–AV teams en_US
dc.subject context-aware systems en_US
dc.subject driver trust en_US
dc.subject operator trust en_US
dc.subject autonomous cars en_US
dc.subject autonomous driving systems en_US
dc.subject autonomous vehicles en_US
dc.subject advanced driving systems en_US
dc.title Context-Adaptive Management of Drivers’ Trust in Automated Vehicles en_US
dc.type Article en_US
dc.subject.hlbsecondlevel Information and Library Science
dc.subject.hlbtoplevel Social Sciences
dc.description.peerreviewed Peer Reviewed en_US
dc.contributor.affiliationum Information, School of en_US
dc.contributor.affiliationum College of Engineering en_US
dc.contributor.affiliationum Robotics Institute en_US
dc.contributor.affiliationumcampus Ann Arbor en_US
dc.description.bitstreamurl http://deepblue.lib.umich.edu/bitstream/null/1/Azevedo-Sa et al. 2020 with doi.pdf
dc.identifier.doi 10.1109/LRA.2020.3025736
dc.identifier.source IEEE Robotics and Automation Letters en_US
dc.identifier.orcid 0000-0002-1410-2601 en_US
dc.description.depositor SELF en_US
dc.identifier.name-orcid Robert, Lionel P.; 0000-0002-1410-2601 en_US
dc.owningcollname Information, School of (SI)
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