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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MDPI AG
2021
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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) |
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DOAJ |
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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 |
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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 |
_version_ |
1718413138025512960 |