Driving profile modeling and recognition based on soft computing approach

Advancements in biometrics-based authentication have led to its increasing prominence and are being incorporated into everyday tasks. Existing vehicle security systems rely only on alarms or smart card as forms of protection. A biometric driver recognition system utilizing driving behaviors is a hig...

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Main Authors:	Abdul Rahman, Abdul Wahab, Quek, Chai, Chin, Keong Tan, Takeda, Kazuya, Member, IEEE
Format:	Article
Language:	English
Published:	IEEE 2009
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/38143/ http://irep.iium.edu.my/38143/ http://irep.iium.edu.my/38143/1/Driving_Profile_Modeling_and_Recognition_Based_on_Soft_Computing_Approach.pdf

id	iium-38143
recordtype	eprints
spelling	iium-381432014-09-10T07:51:20Z http://irep.iium.edu.my/38143/ Driving profile modeling and recognition based on soft computing approach Abdul Rahman, Abdul Wahab Quek, Chai Chin, Keong Tan Takeda, Kazuya Member, IEEE, T Technology (General) Advancements in biometrics-based authentication have led to its increasing prominence and are being incorporated into everyday tasks. Existing vehicle security systems rely only on alarms or smart card as forms of protection. A biometric driver recognition system utilizing driving behaviors is a highly novel and personalized approach and could be incorporated into existing vehicle security system to form a multimodal identification system and offer a greater degree of multilevel protection. In this paper, detailed studies have been conducted to model individual driving behavior in order to identify features that may be efficiently and effectively used to profile each driver. Feature extraction techniques based on Gaussian mixture models (GMMs) are proposed and implemented. Features extracted from the accelerator and brake pedal pressure were then used as inputs to a fuzzy neural network (FNN) system to ascertain the identity of the driver. Two fuzzy neural networks, namely, the evolving fuzzy neural network (EFuNN) and the adaptive network-based fuzzy inference system (ANFIS), are used to demonstrate the viability of the two proposed feature extraction techniques. The performances were compared against an artificial neural network (NN) implementation using the multilayer perceptron (MLP) network and a statistical method based on the GMM. Extensive testing was conducted and the results show great potential in the use of the FNN for real-time driver identification and verification. In addition, the profiling of driver behaviors has numerous other potential applications for use by law enforcement and companies dealing with buses and truck drivers. IEEE 2009-04 Article PeerReviewed application/pdf en http://irep.iium.edu.my/38143/1/Driving_Profile_Modeling_and_Recognition_Based_on_Soft_Computing_Approach.pdf Abdul Rahman, Abdul Wahab and Quek, Chai and Chin, Keong Tan and Takeda, Kazuya and Member, and IEEE, (2009) Driving profile modeling and recognition based on soft computing approach. IEEE Transactions on Neural Networks, 20 (4). pp. 563-582. ISSN 2162-237X http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4796254
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Abdul Rahman, Abdul Wahab Quek, Chai Chin, Keong Tan Takeda, Kazuya Member, IEEE, Driving profile modeling and recognition based on soft computing approach
description	Advancements in biometrics-based authentication have led to its increasing prominence and are being incorporated into everyday tasks. Existing vehicle security systems rely only on alarms or smart card as forms of protection. A biometric driver recognition system utilizing driving behaviors is a highly novel and personalized approach and could be incorporated into existing vehicle security system to form a multimodal identification system and offer a greater degree of multilevel protection. In this paper, detailed studies have been conducted to model individual driving behavior in order to identify features that may be efficiently and effectively used to profile each driver. Feature extraction techniques based on Gaussian mixture models (GMMs) are proposed and implemented. Features extracted from the accelerator and brake pedal pressure were then used as inputs to a fuzzy neural network (FNN) system to ascertain the identity of the driver. Two fuzzy neural networks, namely, the evolving fuzzy neural network (EFuNN) and the adaptive network-based fuzzy inference system (ANFIS), are used to demonstrate the viability of the two proposed feature extraction techniques. The performances were compared against an artificial neural network (NN) implementation using the multilayer perceptron (MLP) network and a statistical method based on the GMM. Extensive testing was conducted and the results show great potential in the use of the FNN for real-time driver identification and verification. In addition, the profiling of driver behaviors has numerous other potential applications for use by law enforcement and companies dealing with buses and truck drivers.
format	Article
author	Abdul Rahman, Abdul Wahab Quek, Chai Chin, Keong Tan Takeda, Kazuya Member, IEEE,
author_facet	Abdul Rahman, Abdul Wahab Quek, Chai Chin, Keong Tan Takeda, Kazuya Member, IEEE,
author_sort	Abdul Rahman, Abdul Wahab
title	Driving profile modeling and recognition based on soft computing approach
title_short	Driving profile modeling and recognition based on soft computing approach
title_full	Driving profile modeling and recognition based on soft computing approach
title_fullStr	Driving profile modeling and recognition based on soft computing approach
title_full_unstemmed	Driving profile modeling and recognition based on soft computing approach
title_sort	driving profile modeling and recognition based on soft computing approach
publisher	IEEE
publishDate	2009
url	http://irep.iium.edu.my/38143/ http://irep.iium.edu.my/38143/ http://irep.iium.edu.my/38143/1/Driving_Profile_Modeling_and_Recognition_Based_on_Soft_Computing_Approach.pdf
first_indexed	2023-09-18T20:54:45Z
last_indexed	2023-09-18T20:54:45Z
_version_	1777410229625946112

Driving profile modeling and recognition based on soft computing approach

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