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Online identification of drowsy driver using expert system / Ruzelita Ngadiran … [et al.]

This paper discusses the use of Expert System for identification of drowsy driver. To investigate the effectiveness of the usage of Expert System in identifying drowsy driver, a system consisting of data acquisition system and a Forward Chaining reasoning engine was developed in this study. The Know...

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Main Authors: Ngadiran, Ruzelita, Mohd Nor, Mohd Jailani, Md Saad, Mohd Hanif, Koh, Wei Kiat
Format: Conference or Workshop Item
Language:English
Published: 2004
Subjects:
Transportation engineering
Communication systems. Including intelligent transportation systems
TL Motor vehicles. Aeronautics. Astronautics
Online Access:https://ir.uitm.edu.my/id/eprint/52744/1/52744.PDF
https://ir.uitm.edu.my/id/eprint/52744/
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spelling my.uitm.ir.527442021-11-30T09:12:43Z https://ir.uitm.edu.my/id/eprint/52744/ Online identification of drowsy driver using expert system / Ruzelita Ngadiran … [et al.] Ngadiran, Ruzelita Mohd Nor, Mohd Jailani Md Saad, Mohd Hanif Koh, Wei Kiat Transportation engineering Communication systems. Including intelligent transportation systems TL Motor vehicles. Aeronautics. Astronautics This paper discusses the use of Expert System for identification of drowsy driver. To investigate the effectiveness of the usage of Expert System in identifying drowsy driver, a system consisting of data acquisition system and a Forward Chaining reasoning engine was developed in this study. The Knowledge Base (KE) for this system was developed based on previous study of drowsy driver. A K.B development tool, the Simple Expert System Development Tool (SESDT) , was used to generate the rules. Altogether, the system that was developed in this study consists of 98 rules. The rules combines' effects from five parameters to identify drowsiness (time of driving, period of driving, head movement along Y-axis, steering wheel reversal rate and Percentage of Eye Closure (PERCLOS). Preliminary results were promising and it was observed that The Expert System was able to sufficiently identify drowsy driver using the above reasoning engine and KB. 2004 Conference or Workshop Item PeerReviewed text en https://ir.uitm.edu.my/id/eprint/52744/1/52744.PDF ID52744 Ngadiran, Ruzelita and Mohd Nor, Mohd Jailani and Md Saad, Mohd Hanif and Koh, Wei Kiat (2004) Online identification of drowsy driver using expert system / Ruzelita Ngadiran … [et al.]. In: STSS 2004 : Sains Teknologi Jilid 1, 31 Mei – 1 Jun 2004, Hotel Vistana, Kuantan, Pahang.
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Transportation engineering
Communication systems. Including intelligent transportation systems
TL Motor vehicles. Aeronautics. Astronautics
spellingShingle Transportation engineering
Communication systems. Including intelligent transportation systems
TL Motor vehicles. Aeronautics. Astronautics
Ngadiran, Ruzelita
Mohd Nor, Mohd Jailani
Md Saad, Mohd Hanif
Koh, Wei Kiat
Online identification of drowsy driver using expert system / Ruzelita Ngadiran … [et al.]
description This paper discusses the use of Expert System for identification of drowsy driver. To investigate the effectiveness of the usage of Expert System in identifying drowsy driver, a system consisting of data acquisition system and a Forward Chaining reasoning engine was developed in this study. The Knowledge Base (KE) for this system was developed based on previous study of drowsy driver. A K.B development tool, the Simple Expert System Development Tool (SESDT) , was used to generate the rules. Altogether, the system that was developed in this study consists of 98 rules. The rules combines' effects from five parameters to identify drowsiness (time of driving, period of driving, head movement along Y-axis, steering wheel reversal rate and Percentage of Eye Closure (PERCLOS). Preliminary results were promising and it was observed that The Expert System was able to sufficiently identify drowsy driver using the above reasoning engine and KB.
format Conference or Workshop Item
author Ngadiran, Ruzelita
Mohd Nor, Mohd Jailani
Md Saad, Mohd Hanif
Koh, Wei Kiat
author_facet Ngadiran, Ruzelita
Mohd Nor, Mohd Jailani
Md Saad, Mohd Hanif
Koh, Wei Kiat
author_sort Ngadiran, Ruzelita
title Online identification of drowsy driver using expert system / Ruzelita Ngadiran … [et al.]
title_short Online identification of drowsy driver using expert system / Ruzelita Ngadiran … [et al.]
title_full Online identification of drowsy driver using expert system / Ruzelita Ngadiran … [et al.]
title_fullStr Online identification of drowsy driver using expert system / Ruzelita Ngadiran … [et al.]
title_full_unstemmed Online identification of drowsy driver using expert system / Ruzelita Ngadiran … [et al.]
title_sort online identification of drowsy driver using expert system / ruzelita ngadiran … [et al.]
publishDate 2004
url https://ir.uitm.edu.my/id/eprint/52744/1/52744.PDF
https://ir.uitm.edu.my/id/eprint/52744/
_version_ 1718929538183856128
score 13.211869

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