| _version_ |
1855626416889004033
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| author |
Ashardi Abas
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| author_facet |
Ashardi Abas
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| author_sort |
Ashardi Abas
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| description |
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| format |
Thesis
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| id |
upsi-13671
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| institution |
Universiti Pendidikan Sultan Idris
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| language |
English
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| publishDate |
2011
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| record_format |
sWADAH
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| record_pdf |
Restricted
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| spelling |
upsi-136712025-11-28 Non-intrusive drowsiness detection system- design,analysis and evaluation of non-intrusive driver drowsiness system using a Support Vector Machine and fault diagnosis system 2011 Ashardi Abas <p>The development of technologies for preventing drowsiness at the wheel is a major challenge in the_ field of accident avoidance systems. Preventing drowsiness during driving requires a method for_ accurately detecting a decline in driver alertness and a method for alerting and refreshing the_ driver. As a detection method, the authors have developed a system that uses image processing_ technology to analyse images of the road lane with a video camera integrated with steering wheel_ angle data collection from a car simulation system. The main contribution of this study is a novel_ algorithm_ for drowsiness detection and tracking, which is based on the incorporation of_ information from a road vision system and vehicle performance parameters. Refinement of the_ algorithm is more precisely detected the level of drowsiness by the implementation of a support_ vector machine classification for robust and accurate drowsiness warning system. The Support Vector_ Machine (SYM) classification technique diminished drowsiness level by using non intrusive systems,_ using standard equipment sensors, aim to reduce these road accidents caused by drowsiness drivers._ This detection system provides a non-contact technique for judging various levels of driver_ alertness and facilitates early detection of a decline in alertness during driving. The presented_ results are based on a selection of drowsiness database, which covers almost 60 hours of driving_ data collection measurements. All the parameters extracted from vehicle parameter data arc_ collected in a driving simulator. With all the features from a real vehicle, a SYM drowsiness_ detection model is constructed. After several improvements, the classification results showed a_ very good indication of drowsiness by using those systems. _</p> 2011 thesis https://ir.upsi.edu.my/detailsg.php?det=13671 https://ir.upsi.edu.my/detailsg.php?det=13671 text eng N/A openAccess Doctoral Perpustakaan Tuanku Bainun Fakulti Komputeran dan META-Teknologi N/A
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| spellingShingle |
Ashardi Abas
Non-intrusive drowsiness detection system- design,analysis and evaluation of non-intrusive driver drowsiness system using a Support Vector Machine and fault diagnosis system
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| thesis_level |
PhD
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| title |
Non-intrusive drowsiness detection system- design,analysis and evaluation of non-intrusive driver drowsiness system using a Support Vector Machine and fault diagnosis system
|
| title_full |
Non-intrusive drowsiness detection system- design,analysis and evaluation of non-intrusive driver drowsiness system using a Support Vector Machine and fault diagnosis system
|
| title_fullStr |
Non-intrusive drowsiness detection system- design,analysis and evaluation of non-intrusive driver drowsiness system using a Support Vector Machine and fault diagnosis system
|
| title_full_unstemmed |
Non-intrusive drowsiness detection system- design,analysis and evaluation of non-intrusive driver drowsiness system using a Support Vector Machine and fault diagnosis system
|
| title_short |
Non-intrusive drowsiness detection system- design,analysis and evaluation of non-intrusive driver drowsiness system using a Support Vector Machine and fault diagnosis system
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| title_sort |
non intrusive drowsiness detection system design analysis and evaluation of non intrusive driver drowsiness system using a support vector machine and fault diagnosis system
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| url |
https://ir.upsi.edu.my/detailsg.php?det=13671
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| work_keys_str_mv |
AT ashardiabas nonintrusivedrowsinessdetectionsystemdesignanalysisandevaluationofnonintrusivedriverdrowsinesssystemusingasupportvectormachineandfaultdiagnosissystem
|
