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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...

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The nonholonomic characteristic of space robot is used to plan the path of the manipulator, by whose motion the base attitude and the manipulator joints attain the desired states. Firstly, the functions of the joint angles are parameterized by sinusoidal functions. Secondly, the objective function is defined according to the accuracy requirement and the constraints of the system state. Finally, Genetic Algorithm (GA) is used to search the global optimal solution of the parameters. Comparing with other methods, our approach has a number of advantages: 1) The kinematic and dynamic constraints of the manipulator are taken into consideration in the planning process; 2) The dynamic singular point doesn't affect the algorithm since only the direct kinematic equations are utilized; 3) The planned path is very smooth and more applicable in controlling the manipulator; 4) The state converges to the global optimal values. The simulation results verify the method.

Taking as reference the concept of agent in the artificial intelligence, this paper proposes a new multi-agent approach which can be employed in the surveillance for a people group in public places rather than a single person. The agent embodies the state and logic relationship between the person which the agent represents and the others in the same group. It does not merely stand for such individual information of persons as given by the existing surveillance systems. The results of experiments show that by using our multi-agent approach to compute and analyze the relationship between a number of agents, we can well perform real-time surveillance for the group and the related events so as to enhance the applicability and intelligence level of surveillance systems. 1