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Appl. Sci. 2017, 7(10), 1014; doi:10.3390/app7101014

A Comparative Study of Clustering Analysis Method for Driver’s Steering Intention Classification and Identification under Different Typical Conditions

1
School of Automobile and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
2
Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
*
Author to whom correspondence should be addressed.
Received: 22 August 2017 / Revised: 28 September 2017 / Accepted: 28 September 2017 / Published: 30 September 2017
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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 View Full-Text
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Hua, Y.; Jiang, H.; Tian, H.; Xu, X.; Chen, L. A Comparative Study of Clustering Analysis Method for Driver’s Steering Intention Classification and Identification under Different Typical Conditions. Appl. Sci. 2017, 7, 1014.

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