Integrating trust in automation into driver state monitoring systems

Jaume Perello-March, Christopher Burns, Mark Elliott, Stewart Birrell

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

6 Citations (Scopus)
35 Downloads (Pure)


Inappropriate trust in highly automated vehicles (HAVs) has been identified as one of the causes in several accidents [1–3]. These accidents have evidenced the need to include a Driver State Monitoring System (DSMS) [4] into those HAVs which may require occasional manual driving. DSMS make use of several psychophysiological sensors to monitor the drivers’ state, and have already been included in current production vehicles to detect drowsiness, fatigue and distractions [5]. However, DSMS have never been used to monitor Trust in Automation (TiA) states within HAVs yet. Based on recent findings, this paper proposes a new methodology to integrate TiA state-classification into DSMSs for future vehicles.

Original languageEnglish
Title of host publicationHuman Interaction and Emerging Technologies - Proceedings of the 1st International Conference on Human Interaction and Emerging Technologies, IHIET 2019
EditorsTareq Ahram, Redha Taiar, Serge Colson, Arnaud Choplin
PublisherSpringer-Verlag London Ltd
Number of pages6
ISBN (Electronic)978-3-030-25629-6
ISBN (Print)978-3-030-25628-9
Publication statusE-pub ahead of print - 25 Jul 2019
Externally publishedYes
Event1st International Conference on Human Interaction and Emerging Technologies - Nice, France
Duration: 22 Aug 201924 Aug 2019

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


Conference1st International Conference on Human Interaction and Emerging Technologies
Abbreviated titleIHIET 2019

Bibliographical note

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  • Driver State Monitoring Systems
  • Highly automated vehicles
  • Machine learning
  • Trust in Automation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)


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