NDLI: Human Driving Behavior Recognition Based on Hidden Markov Models

Content Provider	IEEE Xplore Digital Library
Author	Xiaoning Meng Ka Keung Lee Yangsheng Xu
Copyright Year	2006
Description	Author affiliation: Dept. of Autom. & Comput.-Aided Eng., Chinese Univ. of Hong Kong, Hong Kong (Xiaoning Meng; Ka Keung Lee; Yangsheng Xu)
Abstract	Automobiles are by now indispensable to our personal lives, but the problem of car thefts threatens the automobile security seriously. In this paper we present an intelligent vehicle security system for handling the vehicle theft problem under the framework of modeling dynamic human behaviors. We propose to recognize the drivers through their driving performances and hope this can help reduce the number of car thefts significantly. Firstly we describe our experimental system-a real time graphic driving simulator-for collecting and modeling human driving behaviors. Using the proposed machine learning method hidden Markov model (HMM), the individual driving behavior model is derived and then we demonstrate the procedure for recognizing different drivers through analyzing the corresponding models. Then we define performance measures for evaluating our resultant learning models using a hidden-Markov-model-(HMM)-based similarity measure, which helps us to derive the similarity of individual behavior and corresponding model. The experimental results of learning algorithms and evaluations are described and finally verify that the proposed method is valid and useful against the vehicle thefts problem.
Starting Page	274
Ending Page	279
File Size	200874
Page Count	6
File Format	PDF
ISBN	142440570X
DOI	10.1109/ROBIO.2006.340166
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-12-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Hidden Markov models Automobiles Security Intelligent vehicles Vehicle dynamics Real time systems Graphics Learning systems Performance evaluation Intelligent systems Vehicle security Human behavior modeling Machine learning
Content Type	Text
Resource Type	Article

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