Fatigue driving detection model based on multi-feature fusion and semi-supervised active learning

Fatigue driving is one of the main factors of traffic accidents and there are many research efforts focusing on fatigue driving detection. With the extensive use of on-board sensors, a huge number of unlabelled driving data can be easily collected, however, it is a costly and laborious work to annot...

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Veröffentlicht in:	IET intelligent transport systems Jg. 13; H. 9; S. 1401 - 1409
Hauptverfasser:	Li, Xu, Hong, Lin, Wang, Jian-chun, Liu, Xiang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 01.09.2019
Schlagworte:	authors costly work efficient fatigue driving detection model facial features fatigue laborious work learning (artificial intelligence) novel fatigue Research Article rest data road safety semantic labels semisupervised active learning algorithm steering features unlabelled driving data laborious work efficient fatigue driving detection model road safety facial features semantic labels semisupervised active learning algorithm fatigue costly work novel fatigue steering features rest data learning (artificial intelligence) unlabelled driving data authors
ISSN:	1751-956X, 1751-9578
Online-Zugang:	Volltext
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