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.

Development of a Representative EV Urban Driving Cycle Based on a k-Means and SVM Hybrid Clustering Algorithm

“…PCA was proposed and applied by statisticians K. Pearson and H. Hotelling in 1901 [41,42]. PCA is a multivariate statistical method that transforms a large number of correlated variables into a few uncorrelated principal components through orthogonal transformation and can maintain the original variable information as much as possible.…”

“…Generally, we use characteristic parameters to describe the vehicle motion state. If all the characteristic parameters are used for classification, it will increase both the computational complexity and the difficulty of analysis [41,42]. Although each parameter characterizes different motion information, the parameters are not independent, and some parameters can be expressed by a combination of several 6 Journal of Advanced Transportation other parameters.…”

“…Although each parameter characterizes different motion information, the parameters are not independent, and some parameters can be expressed by a combination of several 6 Journal of Advanced Transportation other parameters. Therefore, we use the PCA algorithm to reduce the dimensionality of the characteristic parameters [41,42].…”

Road Vehicles Surroundings Supervision: Onboard Sensors and Communications

“…These papers could be divided in two main groups. The first deals with specific technologies or solutions [1][2][3][4][5][6][7] and the second presents applications or architectures for complete autonomous vehicles (or parts of them) [8][9][10][11]. Furthermore, the first group presents a quite wide vision of research fields that could be involved such as onboard sensors [1][2][3], communications [4], driver supervision [5], and traffic analysis [6,7].…”

“…Even in intelligent vehicles and sometimes for the design of assistance systems, driver's intention classification and identification is identified as the key technology. To study driver's steering intention under different typical operating conditions, five driving school coaches of different ages and genders were selected as the test drivers for a real vehicle test [5].…”