Preventing Road Accidents through Early Detection of Driver Behavior using Smartphone Motion Sensor Data: An Ensemble Feature Engineering Approach
Driver behavior refers to the actions and attitudes of individuals behind the wheel of a vehicle. Poor driving behavior can have serious consequences, including accidents, injuries, and fatalities. One of the main disadvantages of poor driving behavior is the increased risk of road accidents, higher...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://depot.sorbonne.ae/handle/20.500.12458/1454 |
| الوسوم: |
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| _version_ | 1857415064837947392 |
|---|---|
| author | Raza, Ali |
| author2 | Akhtar, Iqra Abualigah, Laith Abu Zitar, Raed Sharaf, Mohamed Daoud, Mohammad Sh. Jia, Heming |
| author2_role | author author author author author author |
| author_facet | Raza, Ali Akhtar, Iqra Abualigah, Laith Abu Zitar, Raed Sharaf, Mohamed Daoud, Mohammad Sh. Jia, Heming |
| author_role | author |
| dc.creator.none.fl_str_mv | Raza, Ali Akhtar, Iqra Abualigah, Laith Abu Zitar, Raed Sharaf, Mohamed Daoud, Mohammad Sh. Jia, Heming |
| dc.date.none.fl_str_mv | 2023-12-08T06:53:19Z 2023-12-08T06:53:19Z 2023 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 2169-3536 https://depot.sorbonne.ae/handle/20.500.12458/1454 10.1109/ACCESS.2023.3340304 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | IEEE Access 2169-3536 |
| dc.subject.none.fl_str_mv | Machine learning driver behavior sensor data feature engineering ensemble learning |
| dc.title.none.fl_str_mv | Preventing Road Accidents through Early Detection of Driver Behavior using Smartphone Motion Sensor Data: An Ensemble Feature Engineering Approach |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal article |
| description | Driver behavior refers to the actions and attitudes of individuals behind the wheel of a vehicle. Poor driving behavior can have serious consequences, including accidents, injuries, and fatalities. One of the main disadvantages of poor driving behavior is the increased risk of road accidents, higher insurance premiums, fines, and even criminal charges. The primary aim of our study is to detect driver behavior early with high-performance scores. The publicly available smartphone motion sensor data is utilized to conduct our study experiments. A novel LR-RFC (Logistic Regression Random Forest Classifier) method is proposed for feature engineering. The proposed LR-RFC method combines the logistic regression and random forest classifier for feature engineering from the motion sensor data. The original smartphone motion sensor data is input into the LR-RFC method, generating new probabilistic features. The newly extracted probabilistic features are then input to the applied machine learning methods for predicting driver behavior. The study results show that the proposed LR-RFC approach achieves the highest performance score. Extensive study experiments demonstrate that the random forest achieved the highest performance score of 99% using the proposed LR-RFC method. The performance is validated using k-fold cross-validation and hyperparameter optimization. Our novel proposed study has the potential to revolutionize the early detection of driver behavior to avoid road accidents. |
| id | sorbonner_50d63822f26fa186d501829bbdaec950 |
| identifier_str_mv | 2169-3536 10.1109/ACCESS.2023.3340304 |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1454 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Preventing Road Accidents through Early Detection of Driver Behavior using Smartphone Motion Sensor Data: An Ensemble Feature Engineering ApproachRaza, AliAkhtar, IqraAbualigah, LaithAbu Zitar, RaedSharaf, MohamedDaoud, Mohammad Sh.Jia, HemingMachine learningdriver behaviorsensor datafeature engineeringensemble learningDriver behavior refers to the actions and attitudes of individuals behind the wheel of a vehicle. Poor driving behavior can have serious consequences, including accidents, injuries, and fatalities. One of the main disadvantages of poor driving behavior is the increased risk of road accidents, higher insurance premiums, fines, and even criminal charges. The primary aim of our study is to detect driver behavior early with high-performance scores. The publicly available smartphone motion sensor data is utilized to conduct our study experiments. A novel LR-RFC (Logistic Regression Random Forest Classifier) method is proposed for feature engineering. The proposed LR-RFC method combines the logistic regression and random forest classifier for feature engineering from the motion sensor data. The original smartphone motion sensor data is input into the LR-RFC method, generating new probabilistic features. The newly extracted probabilistic features are then input to the applied machine learning methods for predicting driver behavior. The study results show that the proposed LR-RFC approach achieves the highest performance score. Extensive study experiments demonstrate that the random forest achieved the highest performance score of 99% using the proposed LR-RFC method. The performance is validated using k-fold cross-validation and hyperparameter optimization. Our novel proposed study has the potential to revolutionize the early detection of driver behavior to avoid road accidents.2023-12-08T06:53:19Z2023-12-08T06:53:19Z2023Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal articleapplication/pdf2169-3536https://depot.sorbonne.ae/handle/20.500.12458/145410.1109/ACCESS.2023.3340304enIEEE Access2169-3536oai:depot.sorbonne.ae:20.500.12458/14542023-12-08T18:00:32Z |
| spellingShingle | Preventing Road Accidents through Early Detection of Driver Behavior using Smartphone Motion Sensor Data: An Ensemble Feature Engineering Approach Raza, Ali Machine learning driver behavior sensor data feature engineering ensemble learning |
| title | Preventing Road Accidents through Early Detection of Driver Behavior using Smartphone Motion Sensor Data: An Ensemble Feature Engineering Approach |
| title_full | Preventing Road Accidents through Early Detection of Driver Behavior using Smartphone Motion Sensor Data: An Ensemble Feature Engineering Approach |
| title_fullStr | Preventing Road Accidents through Early Detection of Driver Behavior using Smartphone Motion Sensor Data: An Ensemble Feature Engineering Approach |
| title_full_unstemmed | Preventing Road Accidents through Early Detection of Driver Behavior using Smartphone Motion Sensor Data: An Ensemble Feature Engineering Approach |
| title_short | Preventing Road Accidents through Early Detection of Driver Behavior using Smartphone Motion Sensor Data: An Ensemble Feature Engineering Approach |
| title_sort | Preventing Road Accidents through Early Detection of Driver Behavior using Smartphone Motion Sensor Data: An Ensemble Feature Engineering Approach |
| topic | Machine learning driver behavior sensor data feature engineering ensemble learning |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1454 |