Error Type, Risk, Performance, and Trust: Investigating the Different Impacts of false alarms and misses on Trust and Performance

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dc.contributor.author Zhao, Huajing
dc.contributor.author Azevedo-Sa, Hebert
dc.contributor.author Esterwood, Connor
dc.contributor.author Yang, X. Jessie
dc.contributor.author Robert, Lionel
dc.contributor.author Tilbury, Dawn
dc.date.accessioned 2019-06-28T22:56:39Z
dc.date.available 2019-06-28T22:56:39Z
dc.date.issued 2019-06-28
dc.identifier.citation Zhao, H., Azevedo-Sa, H., Esterwood, C., Yang, X. J., Robert, L. P., Tilbury, D. 2019. Error Type, Risk, Performance, and Trust: Investigating the Different Impacts of false alarms and misses on Trust and Performance, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS 2019), NDIA, Novi, MI, Aug. 13-15, 2019. en_US
dc.identifier.uri http://hdl.handle.net/2027.42/149648
dc.description.abstract Semi-autonomous vehicles are intended to give drivers multitasking flexibility and to improve driving safety. Yet, drivers have to trust the vehicle’s autonomy to fully leverage the vehicle’s capability. Prior research on driver’s trust in a vehicle’s autonomy has normally assumed that the autonomy was without error. Unfortunately, this may be at times an unrealistic assumption. To address this shortcoming, we seek to examine the impacts of automation errors on the relationship between drivers’ trust in automation and their performance on a non-driving secondary task. More specifically, we plan to investigate false alarms and misses in both low and high risk conditions. To accomplish this, we plan to utilize a 2 (risk conditions) × 4 (alarm conditions) mixed design. The findings of this study are intended to inform Autonomous Driving Systems (ADS) designers by permitting them to appropriately tune the sensitivity of alert systems by understanding the impacts of error type and varying risk conditions. en_US
dc.description.sponsorship This research is supported in part by the Automotive Research Center (ARC) at the University of Michigan, with funding from government contract DoD-DoA W56HZV-14-2-0001, through the U.S. Army Combat Capabilities Development Command (CCDC)/Ground Vehicle Systems Center (GVSC). en_US
dc.language.iso en_US en_US
dc.publisher GVSETS 2019 en_US
dc.subject semi-autonomous vehicles en_US
dc.subject autonomous vehicles en_US
dc.subject automated driving en_US
dc.subject automated vehicles en_US
dc.subject automation en_US
dc.subject automation trust en_US
dc.subject autonomous vehicles trust en_US
dc.subject vehicle autonomy en_US
dc.subject non-driving secondary task en_US
dc.subject driver's trust en_US
dc.subject Autonomous Driving Systems en_US
dc.subject false alarms en_US
dc.subject automation errors en_US
dc.subject misses en_US
dc.subject technology risk en_US
dc.subject vehicle alert systems en_US
dc.subject drivers multitasking en_US
dc.subject vehicles en_US
dc.subject human vehicle interactions en_US
dc.subject human machine interaction en_US
dc.subject human automation interaction en_US
dc.subject vehicle trust en_US
dc.subject self-driving car en_US
dc.subject driving alter systems en_US
dc.title Error Type, Risk, Performance, and Trust: Investigating the Different Impacts of false alarms and misses on Trust and Performance 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 Robotics Institute en_US
dc.contributor.affiliationum Department of Industrial and Operations Engineering en_US
dc.contributor.affiliationum Department of Mechanical Engineering en_US
dc.contributor.affiliationumcampus Ann Arbor en_US
dc.description.bitstreamurl https://deepblue.lib.umich.edu/bitstream/2027.42/149648/1/GVSETS 2019_FinalPaper.pdf
dc.identifier.source Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium en_US
dc.identifier.orcid 0000-0002-1410-2601 en_US
dc.description.filedescription Description of GVSETS 2019_FinalPaper.pdf : Main File
dc.identifier.name-orcid Robert, Lionel P.; 0000-0002-1410-2601 en_US
dc.owningcollname Information, School of (SI)
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