English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Modern advanced driver-assistance systems analyze the driving performance togather information about the driver's state. Such systems are able, forexample, to detect signs of drowsiness by evaluating the steering or lanekeeping behavior and to alert the driver when the drowsiness state reaches acritical level. However, these kinds of systems have no access to direct cuesabout the driver's state. Hence, the aim of this work is to extend the driverdrowsiness detection in vehicles using signals of a driver monitoring camera.For this purpose, 35 features related to the driver's eye blinking behavior andhead movements are extracted in driving simulator experiments. Based on thatlarge dataset, we developed and evaluated a feature selection method based onthe k-Nearest Neighbor algorithm for the driver's state classification. Aconcluding analysis of the best performing feature sets yields valuableinsights about the influence of drowsiness on the driver's blink behavior andhead movements. These findings will help in the future development of robustand reliable driver drowsiness monitoring systems to prevent fatigue-inducedaccidents.

Driver Drowsiness Classification Based on Eye Blink and Head Movement Features Using the k-NN Algorithm