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Camera-based sleepiness detection: final report of the project SleepEYE
Swedish National Road and Transport Research Institute, Traffic and road users, Human-vehicle-transport system interaction.ORCID iD: 0000-0002-2061-5817
Swedish National Road and Transport Research Institute, Traffic and road users, Human-vehicle-transport system interaction.ORCID iD: 0000-0003-4134-0303
Smart eye.
Volvo cars.
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2011 (English)Report (Other academic)
Abstract [en]

Two literature reviews were conducted in order to identify indicators of driver sleepiness and distraction. Three sleepiness indicators – blink duration, blink frequency and Perclos – were implemented in the camera system.

The aims of the study were firstly to develop and evaluate a low cost 1-camera unit for detection of driver impairment, and secondly to identify indicators of driver sleepiness and to create a sleepiness classifier for driving simulators.

The project included two experiments. The first was a field test where 18 participants conducted one alert and one sleepy driving session on a motorway. 16 of the 18 participants also participated in the second experiment which was a simulator study similar to the field test.

The field test data was used for evaluation of the 1-camera system, with respect to the sleepiness indicators. Blink parameters from the 1-camera system was compared to blink parameters obtained from a reference 3-camera system and from the EOG. It was found that the 1-camera system missed many blinks and that the blink duration was not in agreement with the blink duration obtained from the EOG and from the reference 3-camera system. However, the results also indicated that it should be possible to improve the blink detection algorithm since the raw data looked well in many cases where the algorithm failed to identify blinks.

The sleepiness classifier was created using data from the simulator experiment. In the first step, the indicators identified in the literature review were implemented and evaluated. The indicators also included driving and context related parameters in addition to the blink related ones. The most promising indicators were then used as inputs to the classifier.

Place, publisher, year, edition, pages
Linköping: Statens väg- och transportforskningsinstitut, 2011. , 62 p.
Series
ViP publication: ViP - Virtual Prototyping and Assessment by Simulation, 2011-6
Keyword [en]
Fatigue (human), Driver, Detection, Measurement, Classification, Eye movement, Camera
Keyword [sv]
Trötthet, Förare, Detektering, Mätning, Klassificering, Ögonrörelser, Kameror
National Category
Applied Psychology
Research subject
Road: Traffic safety and accidents, Road: Road user behaviour
Identifiers
URN: urn:nbn:se:vti:diva-5270OAI: oai:DiVA.org:vti-5270DiVA: diva2:674103
Available from: 2013-12-03 Created: 2013-12-03 Last updated: 2016-02-25Bibliographically approved

Open Access in DiVA

fulltext(1382 kB)152 downloads
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Type fulltextMimetype application/pdf

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Fors, CarinaAhlström, ChristerKircher, KatjaAnund, Anna
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CiteExportLink to record
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