title: Were they in the loop during automated driving? Links between visual attention and crash potential.
abstract: A proposed advantage of vehicle automation is that it relieves drivers from the moment-to-moment demands of driving, to engage in other, non-driving related, tasks. However, it is important to gain an understanding of drivers' capacity to resume manual control, should such a need arise. As automation removes vehicle control-based measures as a performance indicator, other metrics must be explored.
  
  
  
  This driving simulator study, conducted under the European Commission (EC) funded AdaptIVe project, assessed drivers' gaze fixations during partially-automated (SAE Level 2) driving, on approach to critical and non-critical events. Using a between-participant design, 75 drivers experienced automation with one of five out-of-the-loop (OOTL) manipulations, which used different levels of screen visibility and secondary tasks to induce varying levels of engagement with the driving task: 1) no manipulation, 2) manipulation by light fog, 3) manipulation by heavy fog, 4) manipulation by heavy fog plus a visual task, 5) no manipulation plus an n-back task.
  
  
  
  The OOTL manipulations influenced drivers' first point of gaze fixation after they were asked to attend to an evolving event. Differences resolved within one second and visual attention allocation adapted with repeated events, yet crash outcome was not different between OOTL manipulation groups. Drivers who crashed in the first critical event showed an erratic pattern of eye fixations towards the road centre on approach to the event, while those who did not demonstrated a more stable pattern.
  
  
  
  Automated driving systems should be able to direct drivers' attention to hazards no less than 6 seconds in advance of an adverse outcome.
authors.affiliation: Institute for Transport Studies, University of Leeds, Leeds, UK.
authors.forename: Tyron
authors.identifier: 0000-0001-6577-6369
authors.initials: T
authors.lastname: Louw
authors.affiliation: Institute for Transport Studies, University of Leeds, Leeds, UK.
authors.forename: Ruth
authors.initials: R
authors.lastname: Madigan
authors.affiliation: Institute for Transport Studies, University of Leeds, Leeds, UK.
authors.forename: Oliver
authors.initials: O
authors.lastname: Carsten
authors.affiliation: Institute for Transport Studies, University of Leeds, Leeds, UK.
authors.forename: Natasha
authors.initials: N
authors.lastname: Merat
mesh_terms: D000328:Adult; D000368:Aged; D001288:Attention; D001331:Automation / Q000295:instrumentation; D001334:Automobile Driving; D003198:Computer Simulation / Q000639:trends; D005260:Female; D005403:Fixation, Ocular / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D011597:Psychomotor Performance; D011930:Reaction Time / Q000502:physiology*; D013647:Task Performance and Analysis; D006113:United Kingdom; D014796:Visual Perception; D055815:Young Adult