Sequential in-vehicle glance distributions: an alternative approach for analyzing glance data

Hum Factors. 2015 Jun;57(4):567-72. doi: 10.1177/0018720814560225. Epub 2014 Nov 13.

Abstract

Objective: The aim of this study was to illustrate how a consideration of glance sequences to in-vehicle tasks and their associated distributions can be informative.

Background: The rapid growth in the number of nomadic technologies and in-vehicle devices has the potential to create complex, visually intensive tasks for drivers that may incur long in-vehicle glances. Such glances place drivers at increased risk of a motor vehicle crash.

Method: We used eye-glance data from a study of distraction training programs to examine the change in glance duration distributions across consecutive glances during the performance of various in-vehicle tasks.

Results: The sequential analysis across trained and untrained drivers showed that the proportion of late-sequence glances longer than a 2-s threshold among untrained drivers was almost double the number of such glances for the trained drivers, that the third and later glances were particularly problematic, and that training reduced the proportion of early- and later-sequence glances.

Conclusion: Examining how the duration of off-road glances varies as a function of their order in a sequence of glances and the visual demands of the task can offer important insights into the change in the distracting potential of in-vehicle tasks across glances and the effects of training.

Application: The sequential analysis of in-vehicle glance data can be useful for researchers and practitioners and has implications for the development and evaluation of training programs as well as for task and interface design.

Keywords: attention maintenance; driving safety.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Adolescent
  • Attention / physiology*
  • Automobile Driving*
  • Eye Movements / physiology*
  • Female
  • Humans
  • Male
  • Safety
  • Task Performance and Analysis