Classification of Driver Distraction Risk Levels: Based on Driver’s Gaze and Secondary Driving Tasks
Driver distraction is one of the significant causes of traffic accidents. To improve the accuracy of accident occurrence prediction under driver distraction and to provide graded warnings, it is necessary to classify the level of driver distraction. Based on naturalistic driving study data, distract...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/10/24/4806 |
| _version_ | 1797456447077875712 |
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| author | Lili Zheng Yanlin Zhang Tongqiang Ding Fanyun Meng Yanlin Li Shiyu Cao |
| author_facet | Lili Zheng Yanlin Zhang Tongqiang Ding Fanyun Meng Yanlin Li Shiyu Cao |
| author_sort | Lili Zheng |
| collection | DOAJ |
| description | Driver distraction is one of the significant causes of traffic accidents. To improve the accuracy of accident occurrence prediction under driver distraction and to provide graded warnings, it is necessary to classify the level of driver distraction. Based on naturalistic driving study data, distraction risk levels are classified using the driver’s gaze and secondary driving tasks in this paper. The classification results are then combined with road environment factors for accident occurrence prediction. Two ways are suggested to classify driver distraction risk levels in this study: one is to divide it into three levels based on the driver’s gaze and the AttenD algorithm, and the other is to divide it into six levels based on secondary driving tasks and odds ratio. Random Forest, AdaBoost, and XGBoost are used to predict accident occurrence by combining the classification results, driver characteristics, and road environment factors. The results show that the classification of distraction risk levels helps improve the model prediction accuracy. The classification based on the driver’s gaze is better than that based on secondary driving tasks. The classification method can be applied to accident risk prediction and further driving risk warning. |
| first_indexed | 2024-03-09T16:07:57Z |
| format | Article |
| id | doaj.art-4395bb9913714246a64a0feeeb110cd5 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T16:07:57Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-4395bb9913714246a64a0feeeb110cd52023-11-24T16:30:04ZengMDPI AGMathematics2227-73902022-12-011024480610.3390/math10244806Classification of Driver Distraction Risk Levels: Based on Driver’s Gaze and Secondary Driving TasksLili Zheng0Yanlin Zhang1Tongqiang Ding2Fanyun Meng3Yanlin Li4Shiyu Cao5Transportation College, Jilin University, No. 5988 Renmin Street, Changchun 130022, ChinaTransportation College, Jilin University, No. 5988 Renmin Street, Changchun 130022, ChinaTransportation College, Jilin University, No. 5988 Renmin Street, Changchun 130022, ChinaTransportation College, Jilin University, No. 5988 Renmin Street, Changchun 130022, ChinaTransportation College, Jilin University, No. 5988 Renmin Street, Changchun 130022, ChinaTransportation College, Jilin University, No. 5988 Renmin Street, Changchun 130022, ChinaDriver distraction is one of the significant causes of traffic accidents. To improve the accuracy of accident occurrence prediction under driver distraction and to provide graded warnings, it is necessary to classify the level of driver distraction. Based on naturalistic driving study data, distraction risk levels are classified using the driver’s gaze and secondary driving tasks in this paper. The classification results are then combined with road environment factors for accident occurrence prediction. Two ways are suggested to classify driver distraction risk levels in this study: one is to divide it into three levels based on the driver’s gaze and the AttenD algorithm, and the other is to divide it into six levels based on secondary driving tasks and odds ratio. Random Forest, AdaBoost, and XGBoost are used to predict accident occurrence by combining the classification results, driver characteristics, and road environment factors. The results show that the classification of distraction risk levels helps improve the model prediction accuracy. The classification based on the driver’s gaze is better than that based on secondary driving tasks. The classification method can be applied to accident risk prediction and further driving risk warning.https://www.mdpi.com/2227-7390/10/24/4806driver’s gazesecondary driving tasksAttenDodds ratiodistraction risk classificationaccident occurrence prediction |
| spellingShingle | Lili Zheng Yanlin Zhang Tongqiang Ding Fanyun Meng Yanlin Li Shiyu Cao Classification of Driver Distraction Risk Levels: Based on Driver’s Gaze and Secondary Driving Tasks Mathematics driver’s gaze secondary driving tasks AttenD odds ratio distraction risk classification accident occurrence prediction |
| title | Classification of Driver Distraction Risk Levels: Based on Driver’s Gaze and Secondary Driving Tasks |
| title_full | Classification of Driver Distraction Risk Levels: Based on Driver’s Gaze and Secondary Driving Tasks |
| title_fullStr | Classification of Driver Distraction Risk Levels: Based on Driver’s Gaze and Secondary Driving Tasks |
| title_full_unstemmed | Classification of Driver Distraction Risk Levels: Based on Driver’s Gaze and Secondary Driving Tasks |
| title_short | Classification of Driver Distraction Risk Levels: Based on Driver’s Gaze and Secondary Driving Tasks |
| title_sort | classification of driver distraction risk levels based on driver s gaze and secondary driving tasks |
| topic | driver’s gaze secondary driving tasks AttenD odds ratio distraction risk classification accident occurrence prediction |
| url | https://www.mdpi.com/2227-7390/10/24/4806 |
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