A comparative study of clustering analysis method for driver's steering intention classification and identification under different typical conditions

Abstract

Driver's intention classification and identification is identified as the key technology for intelligent vehicles and is widely used in a variety of advanced driver assistant systems (ADAS). To study driver's steering intention under different typical operating conditions, five driving school coaches of different ages and genders are selected as the test drivers for a real vehicle test. Four kinds of typical car steering condition test data with four different vehicles are collected. Test data are filtered by the Butterworth filter and are used for extracting the driver steering characteristic parameters. Based on Principal Component Analysis (PCA), the three kinds of clustering analysis methods, including the Fuzzy C-Means algorithm (FCM), the Gustafson-Kessel algorithm (GK) and the Gath-Geva algorithm (GG), considered are proposed to classify and identify driver's intention under different typical operating conditions. Results show that the three approaches can successfully classify and identify drivers' intention respectively despite some accuracy error by FCM. Meanwhile, compared with FCM and GK, GG was the best performing in classification and identification of the driver's intention. In order to verify the validity of the identification method designed by this article, five different drivers were selected. Five tests were carried out on the driving simulator. The results show that the results of each identification are exactly the same as the actual driver's intention.