IEICE TRANSACTIONS on Information
Driver Identification Using Driving Behavior Signals
Summary :
In this paper, we propose a driver identification method that is based on the driving behavior signals that are observed while the driver is following another vehicle. Driving behavior signals, such as the use of the accelerator pedal, brake pedal, vehicle velocity, and distance from the vehicle in front, were measured using a driving simulator. We compared the identification rate obtained using different identification models. As a result, we found the Gaussian Mixture Model to be superior to the Helly model and the optimal velocity model. Also, the driver's operation signals were found to be better than road environment signals and car behavior signals for the Gaussian Mixture Model. The identification rate for thirty driver using actual vehicle driving in a city area was 73%.
- Publication
- IEICE TRANSACTIONS on Information Vol.E89-D No.3 pp.1188-1194
- Publication Date
- 2006/03/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e89-d.3.1188
- Type of Manuscript
- PAPER
- Category
- Human-computer Interaction
Authors
Keyword
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Toshihiro WAKITA, Koji OZAWA, Chiyomi MIYAJIMA, Kei IGARASHI, Katunobu ITOU, Kazuya TAKEDA, Fumitada ITAKURA, "Driver Identification Using Driving Behavior Signals" in IEICE TRANSACTIONS on Information,
vol. E89-D, no. 3, pp. 1188-1194, March 2006, doi: 10.1093/ietisy/e89-d.3.1188.
Abstract: In this paper, we propose a driver identification method that is based on the driving behavior signals that are observed while the driver is following another vehicle. Driving behavior signals, such as the use of the accelerator pedal, brake pedal, vehicle velocity, and distance from the vehicle in front, were measured using a driving simulator. We compared the identification rate obtained using different identification models. As a result, we found the Gaussian Mixture Model to be superior to the Helly model and the optimal velocity model. Also, the driver's operation signals were found to be better than road environment signals and car behavior signals for the Gaussian Mixture Model. The identification rate for thirty driver using actual vehicle driving in a city area was 73%.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.3.1188/_p
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@ARTICLE{e89-d_3_1188,
author={Toshihiro WAKITA, Koji OZAWA, Chiyomi MIYAJIMA, Kei IGARASHI, Katunobu ITOU, Kazuya TAKEDA, Fumitada ITAKURA, },
journal={IEICE TRANSACTIONS on Information},
title={Driver Identification Using Driving Behavior Signals},
year={2006},
volume={E89-D},
number={3},
pages={1188-1194},
abstract={In this paper, we propose a driver identification method that is based on the driving behavior signals that are observed while the driver is following another vehicle. Driving behavior signals, such as the use of the accelerator pedal, brake pedal, vehicle velocity, and distance from the vehicle in front, were measured using a driving simulator. We compared the identification rate obtained using different identification models. As a result, we found the Gaussian Mixture Model to be superior to the Helly model and the optimal velocity model. Also, the driver's operation signals were found to be better than road environment signals and car behavior signals for the Gaussian Mixture Model. The identification rate for thirty driver using actual vehicle driving in a city area was 73%.},
keywords={},
doi={10.1093/ietisy/e89-d.3.1188},
ISSN={1745-1361},
month={March},}
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TY - JOUR
TI - Driver Identification Using Driving Behavior Signals
T2 - IEICE TRANSACTIONS on Information
SP - 1188
EP - 1194
AU - Toshihiro WAKITA
AU - Koji OZAWA
AU - Chiyomi MIYAJIMA
AU - Kei IGARASHI
AU - Katunobu ITOU
AU - Kazuya TAKEDA
AU - Fumitada ITAKURA
PY - 2006
DO - 10.1093/ietisy/e89-d.3.1188
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E89-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2006
AB - In this paper, we propose a driver identification method that is based on the driving behavior signals that are observed while the driver is following another vehicle. Driving behavior signals, such as the use of the accelerator pedal, brake pedal, vehicle velocity, and distance from the vehicle in front, were measured using a driving simulator. We compared the identification rate obtained using different identification models. As a result, we found the Gaussian Mixture Model to be superior to the Helly model and the optimal velocity model. Also, the driver's operation signals were found to be better than road environment signals and car behavior signals for the Gaussian Mixture Model. The identification rate for thirty driver using actual vehicle driving in a city area was 73%.
ER -
English
