Driver identification based on hidden feature extraction by using adaptive nonnegativity-constrained autoencoder

In this paper, we propose a new driver identification method using deep learning. Existing driver identification methods have the disadvantages that the size of the sliding time window is too large and the feature extraction is relatively subjective, which leads to low identification accuracy and lo...

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Veröffentlicht in:	Applied soft computing Jg. 74; S. 1 - 9
Hauptverfasser:	Chen, Jie, Wu, ZhongCheng, Zhang, Jun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.01.2019
Schlagworte:	Adaptive search Deep learning Driver identification Feature extraction Nonnegativity-constrained autoencoder Driver identification Deep learning Adaptive search Feature extraction Nonnegativity-constrained autoencoder
ISSN:	1568-4946, 1872-9681
Online-Zugang:	Volltext
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