Non-intrusive drowsiness detection system - Design,analysis and evaluation of non-intrusive driver drowsiness system using a Support Vector Machine and fault diagnosis system

Ashardi Abas

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Type :	Thesis
Subject :	TL Motor vehicles. Aeronautics. Astronautics
Main Author :	Ashardi Abas
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
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Place of Production :	Tanjong Malim
Publisher :	Fakulti Komputeran dan META-Teknologi
Year of Publication :	2011
Corporate Name :	Perpustakaan Tuanku Bainun
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Abstract : Perpustakaan Tuanku Bainun

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.

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