DROWSY DRIVER DETECTION SYSTEM – VIA FACIAL RECOGNITION AND DRIVING DATA
According to the National Highway Traffic Safety Administration, an estimated 17.6% of all fatal crashes in the years 2017–2021 involved a drowsy driver. This study proposes a drowsy driver detection system that uses both facial recognition and vehicular data to detect if a driver is feeling sleepy...
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| Format: | Article |
| Language: | English |
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UiTM Press
2024-10-01
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| Series: | Malaysian Journal of Computing |
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| Online Access: | https://ir.uitm.edu.my/id/eprint/105182 |
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| _version_ | 1846164205774831616 |
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| author | Nur Iman Kamila Azharudin Hafizah Mansor Shaila Sharmin |
| author_facet | Nur Iman Kamila Azharudin Hafizah Mansor Shaila Sharmin |
| author_sort | Nur Iman Kamila Azharudin |
| collection | DOAJ |
| description | According to the National Highway Traffic Safety Administration, an estimated 17.6% of all fatal crashes in the years 2017–2021 involved a drowsy driver. This study proposes a drowsy driver detection system that uses both facial recognition and vehicular data to detect if a driver is feeling sleepy behind the wheel. We aim to address a lack of works in the literature that combine data measured from the driver (image or biological data) and vehicular data for drowsy driver detection. Our primary data was collected from simulated driving sessions in which a camera was used to record test drivers’ faces while driving a virtual car in the CARLA simulator in both drowsy and non-drowsy states. The collected data consists of video of test drivers' faces from the camera and vehicular data from the simulated car. The video data was used to obtain facial features such as Mouth Over Eyes (MOE), Eyes Aspect Ratio (EAR), and Mouth Aspect Ratio (MAR), while the vehicle data yielded features such as speed, steering wheel movement and pedal readings. These features were used to train Support Vector Machine (SVM) and Random Forest (RF) models to detect drowsy drivers. The results indicate that RF is a better model to be used as compared to SVM in predictions of drowsiness in drivers with an accuracy of 96.24% and 86.85% respectively |
| format | Article |
| id | doaj-art-cbc72bdf18124e65bf35da0f130a541d |
| institution | Kabale University |
| issn | 2600-8238 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | UiTM Press |
| record_format | Article |
| series | Malaysian Journal of Computing |
| spelling | doaj-art-cbc72bdf18124e65bf35da0f130a541d2024-11-18T14:00:03ZengUiTM PressMalaysian Journal of Computing2600-82382024-10-01921852186610.24191/mjoc.v9i2.25694DROWSY DRIVER DETECTION SYSTEM – VIA FACIAL RECOGNITION AND DRIVING DATANur Iman Kamila Azharudin0Hafizah Mansor1Shaila Sharmin2Department of Computer Science, Kulliyyah of ICT, International Islamic University MalaysiaDepartment of Computer Science, Kulliyyah of ICT, International Islamic University MalaysiaDepartment of Computer Science, Kulliyyah of ICT, International Islamic University MalaysiaAccording to the National Highway Traffic Safety Administration, an estimated 17.6% of all fatal crashes in the years 2017–2021 involved a drowsy driver. This study proposes a drowsy driver detection system that uses both facial recognition and vehicular data to detect if a driver is feeling sleepy behind the wheel. We aim to address a lack of works in the literature that combine data measured from the driver (image or biological data) and vehicular data for drowsy driver detection. Our primary data was collected from simulated driving sessions in which a camera was used to record test drivers’ faces while driving a virtual car in the CARLA simulator in both drowsy and non-drowsy states. The collected data consists of video of test drivers' faces from the camera and vehicular data from the simulated car. The video data was used to obtain facial features such as Mouth Over Eyes (MOE), Eyes Aspect Ratio (EAR), and Mouth Aspect Ratio (MAR), while the vehicle data yielded features such as speed, steering wheel movement and pedal readings. These features were used to train Support Vector Machine (SVM) and Random Forest (RF) models to detect drowsy drivers. The results indicate that RF is a better model to be used as compared to SVM in predictions of drowsiness in drivers with an accuracy of 96.24% and 86.85% respectivelyhttps://ir.uitm.edu.my/id/eprint/105182driving datadrowsy driver detectionfacial recognitionmachine learning technique |
| spellingShingle | Nur Iman Kamila Azharudin Hafizah Mansor Shaila Sharmin DROWSY DRIVER DETECTION SYSTEM – VIA FACIAL RECOGNITION AND DRIVING DATA Malaysian Journal of Computing driving data drowsy driver detection facial recognition machine learning technique |
| title | DROWSY DRIVER DETECTION SYSTEM – VIA FACIAL RECOGNITION AND DRIVING DATA |
| title_full | DROWSY DRIVER DETECTION SYSTEM – VIA FACIAL RECOGNITION AND DRIVING DATA |
| title_fullStr | DROWSY DRIVER DETECTION SYSTEM – VIA FACIAL RECOGNITION AND DRIVING DATA |
| title_full_unstemmed | DROWSY DRIVER DETECTION SYSTEM – VIA FACIAL RECOGNITION AND DRIVING DATA |
| title_short | DROWSY DRIVER DETECTION SYSTEM – VIA FACIAL RECOGNITION AND DRIVING DATA |
| title_sort | drowsy driver detection system via facial recognition and driving data |
| topic | driving data drowsy driver detection facial recognition machine learning technique |
| url | https://ir.uitm.edu.my/id/eprint/105182 |
| work_keys_str_mv | AT nurimankamilaazharudin drowsydriverdetectionsystemviafacialrecognitionanddrivingdata AT hafizahmansor drowsydriverdetectionsystemviafacialrecognitionanddrivingdata AT shailasharmin drowsydriverdetectionsystemviafacialrecognitionanddrivingdata |