IEICE TRANSACTIONS on Fundamentals
Multi-Hierarchical Modeling of Driving Behavior Using Dynamics-Based Mode Segmentation
Summary :
This paper presents a new hierarchical mode segmentation of the observed driving behavioral data based on the multi-level abstraction of the underlying dynamics. By synthesizing the ideas of a feature vector definition revealing the dynamical characteristics and an unsupervised clustering technique, the hierarchical mode segmentation is achieved. The identified mode can be regarded as a kind of symbol in the abstract model of the behavior. Second, the grammatical inference technique is introduced to develop the context-dependent grammar of the behavior, i.e., the symbolic dynamics of the human behavior. In addition, the behavior prediction based on the obtained symbolic model is performed. The proposed framework enables us to make a bridge between the signal space and the symbolic space in the understanding of the human behavior.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E92-A No.11 pp.2763-2771
- Publication Date
- 2009/11/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E92.A.2763
- Type of Manuscript
- Special Section PAPER (Special Section on Theory of Concurrent Systems and its Applications)
- Category
Authors
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Hiroyuki OKUDA, Tatsuya SUZUKI, Ato NAKANO, Shinkichi INAGAKI, Soichiro HAYAKAWA, "Multi-Hierarchical Modeling of Driving Behavior Using Dynamics-Based Mode Segmentation" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 11, pp. 2763-2771, November 2009, doi: 10.1587/transfun.E92.A.2763.
Abstract: This paper presents a new hierarchical mode segmentation of the observed driving behavioral data based on the multi-level abstraction of the underlying dynamics. By synthesizing the ideas of a feature vector definition revealing the dynamical characteristics and an unsupervised clustering technique, the hierarchical mode segmentation is achieved. The identified mode can be regarded as a kind of symbol in the abstract model of the behavior. Second, the grammatical inference technique is introduced to develop the context-dependent grammar of the behavior, i.e., the symbolic dynamics of the human behavior. In addition, the behavior prediction based on the obtained symbolic model is performed. The proposed framework enables us to make a bridge between the signal space and the symbolic space in the understanding of the human behavior.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.2763/_p
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@ARTICLE{e92-a_11_2763,
author={Hiroyuki OKUDA, Tatsuya SUZUKI, Ato NAKANO, Shinkichi INAGAKI, Soichiro HAYAKAWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Multi-Hierarchical Modeling of Driving Behavior Using Dynamics-Based Mode Segmentation},
year={2009},
volume={E92-A},
number={11},
pages={2763-2771},
abstract={This paper presents a new hierarchical mode segmentation of the observed driving behavioral data based on the multi-level abstraction of the underlying dynamics. By synthesizing the ideas of a feature vector definition revealing the dynamical characteristics and an unsupervised clustering technique, the hierarchical mode segmentation is achieved. The identified mode can be regarded as a kind of symbol in the abstract model of the behavior. Second, the grammatical inference technique is introduced to develop the context-dependent grammar of the behavior, i.e., the symbolic dynamics of the human behavior. In addition, the behavior prediction based on the obtained symbolic model is performed. The proposed framework enables us to make a bridge between the signal space and the symbolic space in the understanding of the human behavior.},
keywords={},
doi={10.1587/transfun.E92.A.2763},
ISSN={1745-1337},
month={November},}
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TY - JOUR
TI - Multi-Hierarchical Modeling of Driving Behavior Using Dynamics-Based Mode Segmentation
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2763
EP - 2771
AU - Hiroyuki OKUDA
AU - Tatsuya SUZUKI
AU - Ato NAKANO
AU - Shinkichi INAGAKI
AU - Soichiro HAYAKAWA
PY - 2009
DO - 10.1587/transfun.E92.A.2763
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2009
AB - This paper presents a new hierarchical mode segmentation of the observed driving behavioral data based on the multi-level abstraction of the underlying dynamics. By synthesizing the ideas of a feature vector definition revealing the dynamical characteristics and an unsupervised clustering technique, the hierarchical mode segmentation is achieved. The identified mode can be regarded as a kind of symbol in the abstract model of the behavior. Second, the grammatical inference technique is introduced to develop the context-dependent grammar of the behavior, i.e., the symbolic dynamics of the human behavior. In addition, the behavior prediction based on the obtained symbolic model is performed. The proposed framework enables us to make a bridge between the signal space and the symbolic space in the understanding of the human behavior.
ER -
English
