Driver Behavior Soft-Sensor Based on Neurofuzzy Systems and Weighted Projection on Principal Components

This work has as main objective the development of a soft-sensor to classify, in real time, the behaviors of drivers when they are at the controls of a vehicle. Efficient classification of drivers’ behavior while driving, using only the measurements of the sensors already incorporated in the vehicle...

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Detalles Bibliográficos
Autores: Escaño González, Juan Manuel, Ridao-Olivar, Miguel A., Ierardi, Carmelina, Sánchez, Adolfo J., Rouzbehi, Kumars
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universidad Loyola Andalucía
Repositorio:Brújula
OAI Identifier:oai:repositorio.uloyola.es:20.500.12412/6291
Acceso en línea:https://hdl.handle.net/20.500.12412/6291
Access Level:acceso abierto
Palabra clave:Driver behaviour
Classifier
Soft-sensor
Neurofuzzy systems
Principal component analysis
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spelling Driver Behavior Soft-Sensor Based on Neurofuzzy Systems and Weighted Projection on Principal ComponentsEscaño González, Juan ManuelRidao-Olivar, Miguel A.Ierardi, CarmelinaSánchez, Adolfo J.Rouzbehi, KumarsDriver behaviourClassifierSoft-sensorNeurofuzzy systemsPrincipal component analysisThis work has as main objective the development of a soft-sensor to classify, in real time, the behaviors of drivers when they are at the controls of a vehicle. Efficient classification of drivers’ behavior while driving, using only the measurements of the sensors already incorporated in the vehicles and without the need to add extra hardware (smartphones, cameras, etc.), is a challenge. The main advantage of using only the data center signals of modern vehicles is economical. The classification of the driving behavior and the warning to the driver of dangerous behaviors without the need to add extra hardware (and their software) to the vehicle, would allow the direct integration of these classifiers into the current vehicles without incurring a greater cost in the manufacture of the vehicles and therefore be an added value. In this work, the classification is obtained based only on speed, acceleration and inertial measurements which are already present in many modern vehicles. The proposed algorithm is based on a structure made by several Neurofuzzy systems with the combination of projected data in componentsof various PrincipalComponent Analysis.A comparisonwith several typesof classicalclassifyingalgorithms has been made.Es la versión preprint del artículo. Se puede consultar la versión final en https://doi.org/10.1109/JSEN.2020.29959212020info:eu-repo/semantics/articlehttps://hdl.handle.net/20.500.12412/6291reponame:Brújulainstname:Universidad Loyola AndalucíaInglésVI Plan of Research and Transfer of the Universidad de Sevilla (VI PPIT-US) bajo los Contratos de acceso al Sistema Español de Ciencia, Tecnología e Innovación para el desarrollo del programa propio de I+D+i de la Universidad de Sevillahttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:repositorio.uloyola.es:20.500.12412/62912026-06-24T12:48:37Z
dc.title.none.fl_str_mv Driver Behavior Soft-Sensor Based on Neurofuzzy Systems and Weighted Projection on Principal Components
title Driver Behavior Soft-Sensor Based on Neurofuzzy Systems and Weighted Projection on Principal Components
spellingShingle Driver Behavior Soft-Sensor Based on Neurofuzzy Systems and Weighted Projection on Principal Components
Escaño González, Juan Manuel
Driver behaviour
Classifier
Soft-sensor
Neurofuzzy systems
Principal component analysis
title_short Driver Behavior Soft-Sensor Based on Neurofuzzy Systems and Weighted Projection on Principal Components
title_full Driver Behavior Soft-Sensor Based on Neurofuzzy Systems and Weighted Projection on Principal Components
title_fullStr Driver Behavior Soft-Sensor Based on Neurofuzzy Systems and Weighted Projection on Principal Components
title_full_unstemmed Driver Behavior Soft-Sensor Based on Neurofuzzy Systems and Weighted Projection on Principal Components
title_sort Driver Behavior Soft-Sensor Based on Neurofuzzy Systems and Weighted Projection on Principal Components
dc.creator.none.fl_str_mv Escaño González, Juan Manuel
Ridao-Olivar, Miguel A.
Ierardi, Carmelina
Sánchez, Adolfo J.
Rouzbehi, Kumars
author Escaño González, Juan Manuel
author_facet Escaño González, Juan Manuel
Ridao-Olivar, Miguel A.
Ierardi, Carmelina
Sánchez, Adolfo J.
Rouzbehi, Kumars
author_role author
author2 Ridao-Olivar, Miguel A.
Ierardi, Carmelina
Sánchez, Adolfo J.
Rouzbehi, Kumars
author2_role author
author
author
author
dc.subject.none.fl_str_mv Driver behaviour
Classifier
Soft-sensor
Neurofuzzy systems
Principal component analysis
topic Driver behaviour
Classifier
Soft-sensor
Neurofuzzy systems
Principal component analysis
description This work has as main objective the development of a soft-sensor to classify, in real time, the behaviors of drivers when they are at the controls of a vehicle. Efficient classification of drivers’ behavior while driving, using only the measurements of the sensors already incorporated in the vehicles and without the need to add extra hardware (smartphones, cameras, etc.), is a challenge. The main advantage of using only the data center signals of modern vehicles is economical. The classification of the driving behavior and the warning to the driver of dangerous behaviors without the need to add extra hardware (and their software) to the vehicle, would allow the direct integration of these classifiers into the current vehicles without incurring a greater cost in the manufacture of the vehicles and therefore be an added value. In this work, the classification is obtained based only on speed, acceleration and inertial measurements which are already present in many modern vehicles. The proposed algorithm is based on a structure made by several Neurofuzzy systems with the combination of projected data in componentsof various PrincipalComponent Analysis.A comparisonwith several typesof classicalclassifyingalgorithms has been made.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.12412/6291
url https://hdl.handle.net/20.500.12412/6291
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv VI Plan of Research and Transfer of the Universidad de Sevilla (VI PPIT-US) bajo los Contratos de acceso al Sistema Español de Ciencia, Tecnología e Innovación para el desarrollo del programa propio de I+D+i de la Universidad de Sevilla
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Brújula
instname:Universidad Loyola Andalucía
instname_str Universidad Loyola Andalucía
reponame_str Brújula
collection Brújula
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.81155

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