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. Index Terms-Vehicle security, Human behavior modeling, Machine learning, Intelligent systems. A. MotivationIn the last few years, modeling dynamic human behaviors is becoming an increasingly popular paradigm in many different research areas, however, the application for security is less investigated. Nowadays, vehicle theft is a reality. According to the National Insurance Crime Bureau, a vehicle is stolen every 25 seconds in the U.S. and each year along over 1.2 million vehicles were stolen across the country, causing 8 billion US dollars in losses. Therefore the work on vehicle security is significant.In this paper, we focus on the research of utilizing dynamic human behavior models for the vehicle security (preventing from being stolen) application. Since alive biometrical features in dynamic human behaviors are unique and hard to duplicate comparing with other patterns in common security applications, such as password, fingerprint, facial recognition, etc. Therefore, dynamic human behavior models can be utilized as a secure key for the vehicle security application.A methodology based on modeling dynamic human driving behaviors with hidden Markov models (HMMs) is proposed in this paper. It means that a car with this technology embedded can identify the driver through the driving performance in real time. When an illegitimate driver come to use the car and the demonstrated driving behaviors do not match the specified model, the car will automatically stop running and deliver alarm signals accordingly. B. Related WorkIn the past decade, significant researches towards learning skills directly from human have been conducted primarily by Asada's group at MIT[1], the Navlab group at CMU [2] and our group at CUHK [3][4]. In [1], a debarring robot is controlled through an associative neural network which maps process parameter f...
We present an intelligent driver identification system to handle vehicle theft based on modeling dynamic human behaviors. We propose to recognize illegitimate drivers through their driving behaviors. Since human driving behaviors belong to a dynamic biometrical feature which is complex and difficult to imitate compared with static features such as passwords and fingerprints, we find that this novel idea of utilizing human dynamic features for enhanced security application is more effective. In this paper, we first describe our experimental platform for collecting and modeling human driving behaviors. Then we compare fast Fourier transform (FFT), principal component analysis (PCA), and independent component analysis (ICA) for data preprocessing. Using machine learning method of support vector machine (SVM), we derive the individual driving behavior model and we then demonstrate the procedure for recognizing different drivers by analyzing the corresponding models. The experimental results of learning algorithms and evaluation are described.
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