An Intelligent Vehicle Security System Based on Modeling Human Driving Behaviors

摘要

This paper presents an intelligent vehicle security system for handling the vehicle thefts problem under the framework of capturing and analyzing dynamic human behaviors. Since human driving skill is a kind of dynamic biometrical feature which is complex and difficult to imitate, it is unique and more secure than static features such as password, fingerprint and face. By utilizing this dynamic property we focus on the research ideal of classifying the drivers into authorized ones and unauthorized ones by modeling their individual driving performance. Firstly, we develop an experimental system architecture. We collect the data of steering, acceleration and braking directly from human driving behaviors as inputs to the system, which aims to achieve better robustness and efficiency. Then, we use fast fourier transform (FFT), principal component analysis (PCA) and independent component analysis (ICA) for data reduction. The features extracted are sent to support vector machine (SVM) for learning and recognition. In the next step, we embed the intelligent classifier into a security system to identify the authorized drivers in response to the real time driving performances. Finally, the experimental results verify that the proposed method is valid and useful against the vehicle thefts problem with a success rate of around 80%.