Optimization of Categorizing Driver's Head Motion for Driving Assistance Systems

被引：0

作者：

Ito, Momoyo ^{[1
]}

Sato, Kazuhito ^{[2
]}

Fukumi, Minoru ^{[1
]}

机构：

[1] Univ Tokushima, Inst Sci & Technol, Tokushima 770, Japan

[2] Akita Prefectural Univ, Fac Syst Sci & Technol, Akita, Japan

来源：

2012 PROCEEDINGS OF SICE ANNUAL CONFERENCE (SICE) | 2012年

关键词：

Driving assistance system; SOMs; Fuzzy ART;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Many car accidents are caused by driver's deviation from normal condition like carelessness. We aim to construct a driving assist system that is able to detect driver's deviation signal from normal condition. The system detects the deviation signal using driver's time-series head motion information. In this paper, we optimize categorization of driver's head motion using two kinds of unsupervised neural networks: Self-Organizing Maps and Fuzzy Adaptive Resonance Theory, and discuss the relation between vigilance parameter and integrated categories.

引用

页码：471 / 474

页数：4

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