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Using fNIRS to verify trust in highly automated driving
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Perelló-March, Jaume R., Burns, Christopher G., Woodman, Roger, Elliott, Mark T. and Birrell, Stewart A. (2022) Using fNIRS to verify trust in highly automated driving. IEEE Transactions on Intelligent Transportation Systems . doi:10.1109/TITS.2022.3211089 ISSN 1524-9050. [ 🗎 Public]. [ (✓) hoa:511 ] (In Press)
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WRAP-using-fNIRS-verify-trust-highly-automated-driving-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (2505Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TITS.2022.3211089
Abstract
Trust in automation is crucial for the safe and appropriate adoption of automated driving technology. Current research methods to measure trust mainly rely on subjective scales, with several intrinsic limitations. This empirical experiment proposes a novel method to measure trust objectively, using functional near-infrared spectroscopy (fNIRS). Through manipulating participants’ expectations regarding driving automation credibility, we have induced and successfully measured opposing levels of trust in automation. Most notably, our results evidence two separate yet interrelated cortical mechanisms for trust and distrust. Trust is demonstrably linked to decreased monitoring and working memory, whereas distrust is event-related and strongly tied to affective (or emotional) mechanisms. This paper evidence that trust in automation and situation awareness are strongly interrelated during driving automation usage. Our findings are crucial for developing future driver state monitoring technology that mitigates the impact of inappropriate reliance, or over trust, in automated driving systems.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | B Philosophy. Psychology. Religion > BF Psychology Q Science > QC Physics T Technology > TA Engineering (General). Civil engineering (General) T Technology > TE Highway engineering. Roads and pavements T Technology > TL Motor vehicles. Aeronautics. Astronautics |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||
| Library of Congress Subject Headings (LCSH): | Spectrum analysis, Automated vehicles, Intelligent transportation systems, Automated vehicles -- Safety measures, Automobile drivers -- Attitudes, Trust | ||||||
| Journal or Publication Title: | IEEE Transactions on Intelligent Transportation Systems | ||||||
| Publisher: | IEEE | ||||||
| ISSN: | 1524-9050 | ||||||
| Official Date: | 11 October 2022 | ||||||
| Dates: |
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| DOI: | 10.1109/TITS.2022.3211089 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | In Press | ||||||
| Reuse Statement (publisher, data, author rights): | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 13 October 2022 | ||||||
| Date of first compliant Open Access: | 17 October 2022 |
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