Driver Recognition on Segway

Statistical machine learning approach to understand human behaviors has been attracting considerable amounts of attention in recent years. If the authors understand more about humans, the authors can make more user-friendly machines. In this paper, the authors propose the driver recognition method from their record of manipulations using support vector machine. The authors demonstrate the efficiency of the authors’ method using the Segway. The performance of the recognition is quite good especially when the authors introduce the pre-process with FFT. DOI: 10.4018/jalr.2012010107 International Journal of Artificial Life Research, 3(1), 76-88, January-March 2012 77 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. more powerful than HMM for driver behavior recognition. In this paper, we present an intelligent driver identification system using the support vector machine (SVM) (Cortes & Vapnik, 1995). SVM has been applied to many areas, such as pattern recognition, regression or equalization. The aim of this research is to improve the safety of the gyro-pod Segway by detecting a nonauthorized driver. The reason we choose Segway as a target vehicle is that human behavior changes drastically from one person to another with this machine and it is totally new way of driving. This topic just establishes the first step of a long study about behavior recognition. If we can identify who drives the Segway, the same technique can be used to estimate more complex things such as the estimation of driver’s intention. It may be more complex, but it can have a huge application. This paper consists of seven sections. Section 2 describes the experimental apparatus – the vehicle and the sensor – in this research. We propose the method of recognition using SVM in Section 3. Section 4 explains the experimental settings. The experimental result is shown in Section 5 and discussed in Section 6. We conclude this paper in Section 7. 2. EXPERIMENTAL APPARATUS