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Estimating Vehicle Movement Direction from Smartphone Accelerometers Using Deep Neural Networks

Characterization of driving maneuvers or driving styles through motion sensors has become a field of great interest. Before now, this characterization used to be carried out with signals coming from extra equipment installed inside the vehicle, such as On-Board Diagnostic (OBD) devices or sensors in...

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Autores principales: Hernández Sánchez, Sara, Fernández Pozo, Rubén, Hernández Gómez, Luis A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111255/
https://www.ncbi.nlm.nih.gov/pubmed/30103422
http://dx.doi.org/10.3390/s18082624
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author Hernández Sánchez, Sara
Fernández Pozo, Rubén
Hernández Gómez, Luis A.
author_facet Hernández Sánchez, Sara
Fernández Pozo, Rubén
Hernández Gómez, Luis A.
author_sort Hernández Sánchez, Sara
collection PubMed
description Characterization of driving maneuvers or driving styles through motion sensors has become a field of great interest. Before now, this characterization used to be carried out with signals coming from extra equipment installed inside the vehicle, such as On-Board Diagnostic (OBD) devices or sensors in pedals. Nowadays, with the evolution and scope of smartphones, these have become the devices for recording mobile signals in many driving characterization applications. Normally multiple available sensors are used, such as accelerometers, gyroscopes, magnetometers or the Global Positioning System (GPS). However, using sensors such as GPS increase significantly battery consumption and, additionally, many current phones do not include gyroscopes. Therefore, we propose the characterization of driving style through only the use of smartphone accelerometers. We propose a deep neural network (DNN) architecture that combines convolutional and recurrent networks to estimate the vehicle movement direction (VMD), which is the forward movement directional vector captured in a phone’s coordinates. Once VMD is obtained, multiple applications such as characterizing driving styles or detecting dangerous events can be developed. In the development of the proposed DNN architecture, two different methods are compared. The first one is based on the detection and classification of significant acceleration driving forces, while the second one relies on longitudinal and transversal signals derived from the raw accelerometers. The final success rate of VMD estimation for the best method is of 90.07%.
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spelling pubmed-61112552018-08-30 Estimating Vehicle Movement Direction from Smartphone Accelerometers Using Deep Neural Networks Hernández Sánchez, Sara Fernández Pozo, Rubén Hernández Gómez, Luis A. Sensors (Basel) Article Characterization of driving maneuvers or driving styles through motion sensors has become a field of great interest. Before now, this characterization used to be carried out with signals coming from extra equipment installed inside the vehicle, such as On-Board Diagnostic (OBD) devices or sensors in pedals. Nowadays, with the evolution and scope of smartphones, these have become the devices for recording mobile signals in many driving characterization applications. Normally multiple available sensors are used, such as accelerometers, gyroscopes, magnetometers or the Global Positioning System (GPS). However, using sensors such as GPS increase significantly battery consumption and, additionally, many current phones do not include gyroscopes. Therefore, we propose the characterization of driving style through only the use of smartphone accelerometers. We propose a deep neural network (DNN) architecture that combines convolutional and recurrent networks to estimate the vehicle movement direction (VMD), which is the forward movement directional vector captured in a phone’s coordinates. Once VMD is obtained, multiple applications such as characterizing driving styles or detecting dangerous events can be developed. In the development of the proposed DNN architecture, two different methods are compared. The first one is based on the detection and classification of significant acceleration driving forces, while the second one relies on longitudinal and transversal signals derived from the raw accelerometers. The final success rate of VMD estimation for the best method is of 90.07%. MDPI 2018-08-10 /pmc/articles/PMC6111255/ /pubmed/30103422 http://dx.doi.org/10.3390/s18082624 Text en © 2018 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
Hernández Sánchez, Sara
Fernández Pozo, Rubén
Hernández Gómez, Luis A.
Estimating Vehicle Movement Direction from Smartphone Accelerometers Using Deep Neural Networks
title Estimating Vehicle Movement Direction from Smartphone Accelerometers Using Deep Neural Networks
title_full Estimating Vehicle Movement Direction from Smartphone Accelerometers Using Deep Neural Networks
title_fullStr Estimating Vehicle Movement Direction from Smartphone Accelerometers Using Deep Neural Networks
title_full_unstemmed Estimating Vehicle Movement Direction from Smartphone Accelerometers Using Deep Neural Networks
title_short Estimating Vehicle Movement Direction from Smartphone Accelerometers Using Deep Neural Networks
title_sort estimating vehicle movement direction from smartphone accelerometers using deep neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111255/
https://www.ncbi.nlm.nih.gov/pubmed/30103422
http://dx.doi.org/10.3390/s18082624
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