Lightweight Driver Behavior Identification Model with Sparse Learning on In-Vehicle CAN-BUS Sensor Data

This study focuses on driver-behavior identification and its application to finding embedded solutions in a connected car environment. We present a lightweight, end-to-end deep-learning framework for performing driver-behavior identification using in-vehicle controller area network (CAN-BUS) sensor...

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Detalles Bibliográficos

Principais autores:	Shan Ullah, Deok-Hwan Kim
Formato:	Artigo
Idioma:	Inglês
Publicado:	MDPI AG 2020-09-01
Series:	Sensors
Assuntos:	driver-behavior identification deep learning Jetson Xavier network pruning sparse learning convolutional neural network (CNN)
Acceso en liña:	https://www.mdpi.com/1424-8220/20/18/5030
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