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
收藏
页码：471 / 474
页数：4
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