Using fNIRS to Verify Trust in Highly Automated Driving

Jaume R. Perello-March, Christopher G. Burns, Roger Woodman, Mark T. Elliott, Stewart A. Birrell

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
87 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)739-751
Number of pages13
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number1
Early online date11 Oct 2022
Publication statusPublished - 1 Jan 2023

Bibliographical note

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.. (CC BY)


  • Functional near-infrared spectroscopy
  • Automation
  • Reliability
  • Particle measurements ,
  • Monitoring
  • Electroencephalography
  • Atmospheric measurements


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