Driver Behavior Classification System Analysis Using Machine Learning Methods

Distraction while driving occurs when a driver is engaged in non-driving activities. These activities reduce the driver’s attention and focus on the road, therefore increasing the risk of accidents. As a consequence, the number of accidents increases and infrastructure is damaged. Cars are now equip...

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Autores principales: Raymond Ghandour, Albert Jose Potams, Ilyes Boulkaibet, Bilel Neji, Zaher Al Barakeh
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
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Acceso en línea:https://doaj.org/article/61984f4fff414cda8a655277d5e3fbe2
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spelling oai:doaj.org-article:61984f4fff414cda8a655277d5e3fbe22021-11-25T16:31:33ZDriver Behavior Classification System Analysis Using Machine Learning Methods10.3390/app1122105622076-3417https://doaj.org/article/61984f4fff414cda8a655277d5e3fbe22021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10562https://doaj.org/toc/2076-3417Distraction while driving occurs when a driver is engaged in non-driving activities. These activities reduce the driver’s attention and focus on the road, therefore increasing the risk of accidents. As a consequence, the number of accidents increases and infrastructure is damaged. Cars are now equipped with different safety precautions that ensure driver awareness and attention at all times. The first step for such systems is to define whether the driver is distracted or not. Different methods are proposed to detect such distractions, but they lack efficiency when tested in real-life situations. In this paper, four machine learning classification methods are implemented and compared to identify drivers’ behavior and distraction situations based on real data corresponding to different behaviors such as aggressive, drowsy and normal. The data were randomized for a better application of the methods. We demonstrate that the gradient boosting method outperforms the other used classifiers.Raymond GhandourAlbert Jose PotamsIlyes BoulkaibetBilel NejiZaher Al BarakehMDPI AGarticlemachine learningdriver behaviorclassificationANNlogistic regressionrandom forestTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10562, p 10562 (2021)
institution DOAJ
collection DOAJ
language EN
topic machine learning
driver behavior
classification
ANN
logistic regression
random forest
Technology
T
Engineering (General). Civil engineering (General)
TA1-2040
Biology (General)
QH301-705.5
Physics
QC1-999
Chemistry
QD1-999
spellingShingle machine learning
driver behavior
classification
ANN
logistic regression
random forest
Technology
T
Engineering (General). Civil engineering (General)
TA1-2040
Biology (General)
QH301-705.5
Physics
QC1-999
Chemistry
QD1-999
Raymond Ghandour
Albert Jose Potams
Ilyes Boulkaibet
Bilel Neji
Zaher Al Barakeh
Driver Behavior Classification System Analysis Using Machine Learning Methods
description Distraction while driving occurs when a driver is engaged in non-driving activities. These activities reduce the driver’s attention and focus on the road, therefore increasing the risk of accidents. As a consequence, the number of accidents increases and infrastructure is damaged. Cars are now equipped with different safety precautions that ensure driver awareness and attention at all times. The first step for such systems is to define whether the driver is distracted or not. Different methods are proposed to detect such distractions, but they lack efficiency when tested in real-life situations. In this paper, four machine learning classification methods are implemented and compared to identify drivers’ behavior and distraction situations based on real data corresponding to different behaviors such as aggressive, drowsy and normal. The data were randomized for a better application of the methods. We demonstrate that the gradient boosting method outperforms the other used classifiers.
format article
author Raymond Ghandour
Albert Jose Potams
Ilyes Boulkaibet
Bilel Neji
Zaher Al Barakeh
author_facet Raymond Ghandour
Albert Jose Potams
Ilyes Boulkaibet
Bilel Neji
Zaher Al Barakeh
author_sort Raymond Ghandour
title Driver Behavior Classification System Analysis Using Machine Learning Methods
title_short Driver Behavior Classification System Analysis Using Machine Learning Methods
title_full Driver Behavior Classification System Analysis Using Machine Learning Methods
title_fullStr Driver Behavior Classification System Analysis Using Machine Learning Methods
title_full_unstemmed Driver Behavior Classification System Analysis Using Machine Learning Methods
title_sort driver behavior classification system analysis using machine learning methods
publisher MDPI AG
publishDate 2021
url https://doaj.org/article/61984f4fff414cda8a655277d5e3fbe2
work_keys_str_mv AT raymondghandour driverbehaviorclassificationsystemanalysisusingmachinelearningmethods
AT albertjosepotams driverbehaviorclassificationsystemanalysisusingmachinelearningmethods
AT ilyesboulkaibet driverbehaviorclassificationsystemanalysisusingmachinelearningmethods
AT bilelneji driverbehaviorclassificationsystemanalysisusingmachinelearningmethods
AT zaheralbarakeh driverbehaviorclassificationsystemanalysisusingmachinelearningmethods
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