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A Deep Learning Framework for Driving Behavior Identification on In-Vehicle CAN-BUS Sensor Data
Human driving behaviors are personalized and unique, and the automobile fingerprint of drivers could be helpful to automatically identify different driving behaviors and further be applied in fields such as auto-theft systems. Current research suggests that in-vehicle Controller Area Network-BUS (CA...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6471704/ https://www.ncbi.nlm.nih.gov/pubmed/30889917 http://dx.doi.org/10.3390/s19061356 |
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author | Zhang, Jun Wu, ZhongCheng Li, Fang Xie, Chengjun Ren, Tingting Chen, Jie Liu, Liu |
author_facet | Zhang, Jun Wu, ZhongCheng Li, Fang Xie, Chengjun Ren, Tingting Chen, Jie Liu, Liu |
author_sort | Zhang, Jun |
collection | PubMed |
description | Human driving behaviors are personalized and unique, and the automobile fingerprint of drivers could be helpful to automatically identify different driving behaviors and further be applied in fields such as auto-theft systems. Current research suggests that in-vehicle Controller Area Network-BUS (CAN-BUS) data can be used as an effective representation of driving behavior for recognizing different drivers. However, it is difficult to capture complex temporal features of driving behaviors in traditional methods. This paper proposes an end-to-end deep learning framework by fusing convolutional neural networks and recurrent neural networks with an attention mechanism, which is more suitable for time series CAN-BUS sensor data. The proposed method can automatically learn features of driving behaviors and model temporal features without professional knowledge in features modeling. Moreover, the method can capture salient structure features of high-dimensional sensor data and explore the correlations among multi-sensor data for rich feature representations of driving behaviors. Experimental results show that the proposed framework performs well in the real world driving behavior identification task, outperforming the state-of-the-art methods. |
format | Online Article Text |
id | pubmed-6471704 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64717042019-04-26 A Deep Learning Framework for Driving Behavior Identification on In-Vehicle CAN-BUS Sensor Data Zhang, Jun Wu, ZhongCheng Li, Fang Xie, Chengjun Ren, Tingting Chen, Jie Liu, Liu Sensors (Basel) Article Human driving behaviors are personalized and unique, and the automobile fingerprint of drivers could be helpful to automatically identify different driving behaviors and further be applied in fields such as auto-theft systems. Current research suggests that in-vehicle Controller Area Network-BUS (CAN-BUS) data can be used as an effective representation of driving behavior for recognizing different drivers. However, it is difficult to capture complex temporal features of driving behaviors in traditional methods. This paper proposes an end-to-end deep learning framework by fusing convolutional neural networks and recurrent neural networks with an attention mechanism, which is more suitable for time series CAN-BUS sensor data. The proposed method can automatically learn features of driving behaviors and model temporal features without professional knowledge in features modeling. Moreover, the method can capture salient structure features of high-dimensional sensor data and explore the correlations among multi-sensor data for rich feature representations of driving behaviors. Experimental results show that the proposed framework performs well in the real world driving behavior identification task, outperforming the state-of-the-art methods. MDPI 2019-03-18 /pmc/articles/PMC6471704/ /pubmed/30889917 http://dx.doi.org/10.3390/s19061356 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhang, Jun Wu, ZhongCheng Li, Fang Xie, Chengjun Ren, Tingting Chen, Jie Liu, Liu A Deep Learning Framework for Driving Behavior Identification on In-Vehicle CAN-BUS Sensor Data |
title | A Deep Learning Framework for Driving Behavior Identification on In-Vehicle CAN-BUS Sensor Data |
title_full | A Deep Learning Framework for Driving Behavior Identification on In-Vehicle CAN-BUS Sensor Data |
title_fullStr | A Deep Learning Framework for Driving Behavior Identification on In-Vehicle CAN-BUS Sensor Data |
title_full_unstemmed | A Deep Learning Framework for Driving Behavior Identification on In-Vehicle CAN-BUS Sensor Data |
title_short | A Deep Learning Framework for Driving Behavior Identification on In-Vehicle CAN-BUS Sensor Data |
title_sort | deep learning framework for driving behavior identification on in-vehicle can-bus sensor data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6471704/ https://www.ncbi.nlm.nih.gov/pubmed/30889917 http://dx.doi.org/10.3390/s19061356 |
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