Automation Detection of Driver Fatigue Using Visual Behavior Variables
To examine the correlation of driver visual behaviors and subjective levels of fatigue, a total of 36 commercial drivers were invited to participate in 2-h, 3-h, and 4-h naturalistic driving tests during which their eye fixation, saccade, blinking variables, and self-awareness of their fatigue level...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Polish Academy of Sciences
2018-12-01
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| Series: | Archives of Civil Engineering |
| Subjects: | |
| Online Access: | http://www.degruyter.com/view/j/ace.2018.64.issue-2/ace-2018-0023/ace-2018-0023.xml?format=INT |
| _version_ | 1828544493921501184 |
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| author | Wang Yonggang Ma Jingfeng |
| author_facet | Wang Yonggang Ma Jingfeng |
| author_sort | Wang Yonggang |
| collection | DOAJ |
| description | To examine the correlation of driver visual behaviors and subjective levels of fatigue, a total of 36 commercial drivers were invited to participate in 2-h, 3-h, and 4-h naturalistic driving tests during which their eye fixation, saccade, blinking variables, and self-awareness of their fatigue levels were recorded. Then, one-way ANOVA was applied to analyze the variations of each variable among different age groups over varying time periods. The statistical analysis revealed that driving duration had a significant effect on the variation of visual behaviors and feelings of fatigue. After 2h of driving, only the average closure duration value and subjective level of fatigue had an increase of one-fifth or more. After 4h of driving, however, all these variables had a significant change except for the number of saccades and pupil diameter measurements. Particularly, driver saccadic eye movement was more sensitive to driving fatigue, and the elderly were more likely to be affected by the duration of the drive. Finally, a predictor of driver fatigue was determined to detect the real-time level of fatigue and alert at the critical moment. |
| first_indexed | 2024-12-12T02:31:39Z |
| format | Article |
| id | doaj.art-0b67a7d15a38400aaf2b0f0fbba7f23f |
| institution | Directory Open Access Journal |
| issn | 1230-2945 |
| language | English |
| last_indexed | 2024-12-12T02:31:39Z |
| publishDate | 2018-12-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Civil Engineering |
| spelling | doaj.art-0b67a7d15a38400aaf2b0f0fbba7f23f2022-12-22T00:41:23ZengPolish Academy of SciencesArchives of Civil Engineering1230-29452018-12-0164217518510.2478/ace-2018-0023ace-2018-0023Automation Detection of Driver Fatigue Using Visual Behavior VariablesWang Yonggang0Ma Jingfeng1Asso. Prof., PhD., Chang'an University, School of Highway, P.O. Box 487, Middle Section of South 2 Ring Rd., Xi'an 710064,Shaanxi, ChinaMSc., Chang'an University, School of Highway, P.O. Box 487, Middle Section of South 2 Ring Rd., Xi'an 710064,Shaanxi, ChinaTo examine the correlation of driver visual behaviors and subjective levels of fatigue, a total of 36 commercial drivers were invited to participate in 2-h, 3-h, and 4-h naturalistic driving tests during which their eye fixation, saccade, blinking variables, and self-awareness of their fatigue levels were recorded. Then, one-way ANOVA was applied to analyze the variations of each variable among different age groups over varying time periods. The statistical analysis revealed that driving duration had a significant effect on the variation of visual behaviors and feelings of fatigue. After 2h of driving, only the average closure duration value and subjective level of fatigue had an increase of one-fifth or more. After 4h of driving, however, all these variables had a significant change except for the number of saccades and pupil diameter measurements. Particularly, driver saccadic eye movement was more sensitive to driving fatigue, and the elderly were more likely to be affected by the duration of the drive. Finally, a predictor of driver fatigue was determined to detect the real-time level of fatigue and alert at the critical moment.http://www.degruyter.com/view/j/ace.2018.64.issue-2/ace-2018-0023/ace-2018-0023.xml?format=INTdriving durationvisual behaviorsfatigue levelStanford Sleepiness Scalepredictor of driver fatigue |
| spellingShingle | Wang Yonggang Ma Jingfeng Automation Detection of Driver Fatigue Using Visual Behavior Variables Archives of Civil Engineering driving duration visual behaviors fatigue level Stanford Sleepiness Scale predictor of driver fatigue |
| title | Automation Detection of Driver Fatigue Using Visual Behavior Variables |
| title_full | Automation Detection of Driver Fatigue Using Visual Behavior Variables |
| title_fullStr | Automation Detection of Driver Fatigue Using Visual Behavior Variables |
| title_full_unstemmed | Automation Detection of Driver Fatigue Using Visual Behavior Variables |
| title_short | Automation Detection of Driver Fatigue Using Visual Behavior Variables |
| title_sort | automation detection of driver fatigue using visual behavior variables |
| topic | driving duration visual behaviors fatigue level Stanford Sleepiness Scale predictor of driver fatigue |
| url | http://www.degruyter.com/view/j/ace.2018.64.issue-2/ace-2018-0023/ace-2018-0023.xml?format=INT |
| work_keys_str_mv | AT wangyonggang automationdetectionofdriverfatigueusingvisualbehaviorvariables AT majingfeng automationdetectionofdriverfatigueusingvisualbehaviorvariables |