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Sökning: onr:"swepub:oai:DiVA.org:vti-278" > Predicting visual d...

  • Kircher, KatjaStatens väg- och transportforskningsinstitut,Samspel människa, fordon, transportsystem, MFT (författare)

Predicting visual distraction using driving performance data

  • Artikel/kapitelEngelska2010

Förlag, utgivningsår, omfång ...

  • 2010
  • printrdacarrier

Nummerbeteckningar

  • LIBRIS-ID:oai:DiVA.org:vti-278
  • https://urn.kb.se/resolve?urn=urn:nbn:se:vti:diva-278URI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:ref swepub-contenttype
  • Ämneskategori:art swepub-publicationtype

Anmärkningar

  • 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.

Ämnesord och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Ahlström, ChristerStatens väg- och transportforskningsinstitut,Samspel människa, fordon, transportsystem, MFT(Swepub:vti)christer.ahlstrom@vti.se (författare)
  • Statens väg- och transportforskningsinstitutSamspel människa, fordon, transportsystem, MFT (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:Annals of advances in automotive medicine54, s. 333-3421943-2461

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