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Classification of Driver's Head Posture by using Unsupervised Neural Networks

Authors:
Momoyo Ito
Kazuhito Sato
Minoru Fukumi

Keywords: driving behavior, head motion, SOMs, fuzzy ART

Abstract:
Many car accidents are caused by deviations in driver behavior. We aim to construct a driver assistance system that is able to detect such driver deviations. The system detects deviation using time-series head motion information. We analyze driver's head posture during safety verification at an unsignalized intersection with poor visibility and propose a method for classifying head posture using two types of unsupervised neural networks: Self-Organizing Maps (SOMs) and fuzzy Adaptive Resonance Theory (ART). The proposed method has a feature based on the hybridization of two unsupervised neural networks with a seamless mapping procedure comprising the following two steps. The first step is to classify the input patterns in feature space using one-dimensional SOMs with a non-circular mapping layer. The second step is to integrate the weight vectors of the one-dimensional SOMs into appropriate categories using fuzzy ART networks. The proposed method can generate the optimal number of cluster-generated labels for the target problem. We experimentally assess the effectiveness of the proposed method by adjusting the fuzzy ART network vigilance parameters. In addition, we indicate that driver's head posture during safety verification can be categorized according to their individual properties.

Pages: 50 to 57

Copyright: Copyright (c) IARIA, 2013

Publication date: September 29, 2013

Published in: conference

ISSN: 2326-9324

ISBN: 978-1-61208-309-4

Location: Porto, Portugal

Dates: from September 29, 2013 to October 3, 2013