How to keep drivers engaged while supervising driving automation? A literature survey and categorisation of six solution areas

Christopher D.D. Cabrall, Alexander Eriksson, Felix Dreger, Riender Happee, Joost de Winter

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)
239 Downloads (Pure)

Abstract

This work aimed to organise recommendations for keeping people engaged during human supervision of driving automation, encouraging a safe and acceptable introduction of automated driving systems. First, heuristic knowledge of human factors, ergonomics, and psychological theory was used to propose solution areas to human supervisory control problems of sustained attention. Driving and non-driving research examples were drawn to substantiate the solution areas. Automotive manufacturers might (1) avoid this supervisory role altogether, (2) reduce it in objective ways or (3) alter its subjective experiences, (4) utilize conditioning learning principles such as with gamification and/or selection/training techniques, (5) support internal driver cognitive processes and mental models and/or (6) leverage externally situated information regarding relations between the driver, the driving task, and the driving environment. Second, a cross-domain literature survey of influential human-automation interaction research was conducted for how to keep engagement/attention in supervisory control. The solution areas (via numeric theme codes) were found to be reliably applied from independent rater categorisations of research recommendations. Areas (5) and (6) were addressed by around 70% or more of the studies, areas (2) and (4) in around 50% of the studies, and areas (3) and (1) in less than around 20% and 5%, respectively. The present contribution offers a guiding organisational framework towards improving human attention while supervising driving automation.

Original languageEnglish
Pages (from-to)332-365
JournalTheoretical Issues in Ergonomics Science
Volume20
Issue number3
DOIs
Publication statusPublished - 2019

Keywords

  • Attention
  • automated driving
  • engagement
  • human monitoring of automation
  • supervisory control

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