Deep unsupervised multi-modal fusion network for detecting driver distraction

•A state-of-the-art, unsupervised, end-to-end method to detect driver distraction.•Different network architectures to perform embedding subnetworks for multiple heterogeneous sensors.•Multi-scale feature fusion approach to aggregate multi-modal features.•A data collection system using multivariate s...

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Bibliographic Details

Published in:	Neurocomputing (Amsterdam) Vol. 421; pp. 26 - 38
Main Authors:	Zhang, Yuxin, Chen, Yiqiang, Gao, Chenlong
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 15.01.2021
ISSN:	0925-2312, 1872-8286
Online Access:	Get full text
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