Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Predicting visual distraction using driving performance data
Swedish National Road and Transport Research Institute, Traffic and road users, Human-vehicle-transport system interaction.ORCID iD: 0000-0002-1849-9722
Swedish National Road and Transport Research Institute, Traffic and road users, Human-vehicle-transport system interaction.ORCID iD: 0000-0003-4134-0303
2010 (English)In: Annals of advances in automotive medicine, ISSN 1943-2461, Vol. 54, 333-342 p.Article in journal (Refereed) Published
Abstract [en]

Behavioral variables are often used as performance indicators (PIs) of visual or internal distraction induced by secondary tasks. The objective of this study is to investigate whether visual distraction can be predicted by driving performance PIs in a naturalistic setting. Visual distraction is here defined by a gaze based real-time distraction detection algorithm called AttenD. Seven drivers used an instrumented vehicle for one month each in a small scale field operational test. For each of the visual distraction events detected by AttenD, seven PIs such as steering wheel reversal rate and throttle hold were calculated. Corresponding data were also calculated for time periods during which the drivers were classified as attentive.

For each PI, means between distracted and attentive states were calculated using t-tests for different time-window sizes (2 - 40 s), and the window width with the smallest resulting p-value was selected as optimal. Based on the optimized PIs, logistic regression was used to predict whether the drivers were attentive or distracted. The logistic regression resulted in predictions which were 76 % correct (sensitivity = 77 % and specificity = 76 %).

The conclusion is that there is a relationship between behavioral variables and visual distraction, but the relationship is not strong enough to accurately predict visual driver distraction. Instead, behavioral PIs are probably best suited as complementary to eye tracking based algorithms in order to make them more accurate and robust.

Place, publisher, year, edition, pages
2010. Vol. 54, 333-342 p.
Keyword [en]
Attention, Vision, Driving, Performance, Measurement, Field (test), Prediction, Behaviour
National Category
Applied Psychology
Research subject
80 Road: Traffic safety and accidents, 841 Road: Road user behaviour
Identifiers
URN: urn:nbn:se:vti:diva-278PubMedID: 21050615Scopus ID: 2-s2.0-84979823716OAI: oai:DiVA.org:vti-278DiVA: diva2:662669
Conference
Annals of Advances in Automotive Medicine - 54th Annual Scientific Conference; Las Vegas, NV; United States; 17 October 2010 through 20 October 2010
Available from: 2013-11-08 Created: 2013-11-08 Last updated: 2016-10-13Bibliographically approved

Open Access in DiVA

No full text

PubMedScopus

Search in DiVA

By author/editor
Kircher, KatjaAhlström, Christer
By organisation
Human-vehicle-transport system interaction
Applied Psychology

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 53 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf