Verkehrsunfallanalyse

Hell, Wolfram; Schick, Sylvia; Gilg, T.; Graw, Matthias; Schönpflug, Markus; Adamec, Jiri; Schuller, Erich; Peldschus, Steffen

doi:10.1007/978-3-642-20225-4_3

Begutachtung Im Verkehrsrecht 2012

DOI: 10.1007/978-3-642-20225-4_3

|View full text |Cite

Verkehrsunfallanalyse

Wolfram Hell

Sylvia Schick

T. Gilg

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2017

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Simulator study Drowsiness affects humans' driving ability. This is why drowsy driving accounts for approximately 19%-24% of all fatal crashes in manual driving (Hell et al, 2012). To ameliorate the risk of drowsy driving, car manufacturers and scientists have been working on reliable systems to detect, warn of, and ultimately prevent this critical state before it begins.…”

mentioning

confidence: 99%

Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera

et al. 2017

View full text Add to dashboard Cite

In this article, we examine the performance of different eye blink detection algorithms under various constraints. The goal of the present study was to evaluate the performance of an electrooculogram- and camera-based blink detection process in both manually and conditionally automated driving phases. A further comparison between alert and drowsy drivers was performed in order to evaluate the impact of drowsiness on the performance of blink detection algorithms in both driving modes. Data snippets from 14 monotonous manually driven sessions (mean 2 h 46 min) and 16 monotonous conditionally automated driven sessions (mean 2 h 45 min) were used. In addition to comparing two data-sampling frequencies for the electrooculogram measures (50 vs. 25 Hz) and four different signal-processing algorithms for the camera videos, we compared the blink detection performance of 24 reference groups. The analysis of the videos was based on very detailed definitions of eyelid closure events. The correct detection rates for the alert and manual driving phases (maximum 94%) decreased significantly in the drowsy (minus 2% or more) and conditionally automated (minus 9% or more) phases. Blinking behavior is therefore significantly impacted by drowsiness as well as by automated driving, resulting in less accurate blink detection.

show abstract

mentioning

confidence: 99%

Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera

et al. 2017

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Verkehrsunfallanalyse

Cited by 1 publication

References 0 publications

Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera

Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera

Contact Info

Product

Resources

About