Human Driving Behavior Recognition Based on Hidden Markov Models
Xiaoning Meng, Ka Keung Lee, Yangsheng Xu · 2006
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.