Driver impairment detection using decision tree based feature selection and classification
The driver's control authority plays a crucial role at commanding safely a vehicle that is the technical system. And the driver monitoring systems need to detect and identify whether the driver is impaired and able to percept and react possible hazards. In this paper, the driver's impairme...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-06-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123023001524 |
| _version_ | 1797802790989332480 |
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| author | Mert Çetinkaya Tankut Acarman |
| author_facet | Mert Çetinkaya Tankut Acarman |
| author_sort | Mert Çetinkaya |
| collection | DOAJ |
| description | The driver's control authority plays a crucial role at commanding safely a vehicle that is the technical system. And the driver monitoring systems need to detect and identify whether the driver is impaired and able to percept and react possible hazards. In this paper, the driver's impairment detection system is presented. The procedure of fusing driving data constituted by a large set of vehicle sensors in addition to driver's eye glance location is elaborated. A batch data to train, validate and test the impairment detection system is constituted by co-existing 14 location information and 76 different sensors for trips that contain a crash or near crash instances. The procedure of aligning vehicle motion sensor data logged at fixed sampling rate with eye glance location information is presented by applying a sliding window approach. The window is slided when eye glance location is changed and mean of sensor values is used to present the batch data in this window. Each instance of the batch data is classified. A two stage detection algorithm is tested and evaluated. Each instance is classified as impair or non-impair using Extreme Gradient (xg) boosted trees and a voting mechanism for the instances is used to decide whether driver is impaired or not. The results illustrate the effectiveness of the impairment detection system. |
| first_indexed | 2024-03-13T05:11:01Z |
| format | Article |
| id | doaj.art-7343b9fd0e2e4a51beeccd6b1bc44a49 |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-03-13T05:11:01Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-7343b9fd0e2e4a51beeccd6b1bc44a492023-06-16T05:10:39ZengElsevierResults in Engineering2590-12302023-06-0118101025Driver impairment detection using decision tree based feature selection and classificationMert Çetinkaya0Tankut Acarman1Galatasaray University, Computer Engineering Dept, Ciragan Cad., 36, Ortakoy, 34349, Istanbul, TürkiyeCorresponding author.; Galatasaray University, Computer Engineering Dept, Ciragan Cad., 36, Ortakoy, 34349, Istanbul, TürkiyeThe driver's control authority plays a crucial role at commanding safely a vehicle that is the technical system. And the driver monitoring systems need to detect and identify whether the driver is impaired and able to percept and react possible hazards. In this paper, the driver's impairment detection system is presented. The procedure of fusing driving data constituted by a large set of vehicle sensors in addition to driver's eye glance location is elaborated. A batch data to train, validate and test the impairment detection system is constituted by co-existing 14 location information and 76 different sensors for trips that contain a crash or near crash instances. The procedure of aligning vehicle motion sensor data logged at fixed sampling rate with eye glance location information is presented by applying a sliding window approach. The window is slided when eye glance location is changed and mean of sensor values is used to present the batch data in this window. Each instance of the batch data is classified. A two stage detection algorithm is tested and evaluated. Each instance is classified as impair or non-impair using Extreme Gradient (xg) boosted trees and a voting mechanism for the instances is used to decide whether driver is impaired or not. The results illustrate the effectiveness of the impairment detection system.http://www.sciencedirect.com/science/article/pii/S2590123023001524Impairment detectionDriver dataCar crashBoosted trees |
| spellingShingle | Mert Çetinkaya Tankut Acarman Driver impairment detection using decision tree based feature selection and classification Results in Engineering Impairment detection Driver data Car crash Boosted trees |
| title | Driver impairment detection using decision tree based feature selection and classification |
| title_full | Driver impairment detection using decision tree based feature selection and classification |
| title_fullStr | Driver impairment detection using decision tree based feature selection and classification |
| title_full_unstemmed | Driver impairment detection using decision tree based feature selection and classification |
| title_short | Driver impairment detection using decision tree based feature selection and classification |
| title_sort | driver impairment detection using decision tree based feature selection and classification |
| topic | Impairment detection Driver data Car crash Boosted trees |
| url | http://www.sciencedirect.com/science/article/pii/S2590123023001524 |
| work_keys_str_mv | AT mertcetinkaya driverimpairmentdetectionusingdecisiontreebasedfeatureselectionandclassification AT tankutacarman driverimpairmentdetectionusingdecisiontreebasedfeatureselectionandclassification |