Abstract
Our objective is to develop a new driving assist system that can help low-skilled drivers improve their driving skill. In this paper, we describe a statistical method we have developed to extract distinctions between high- and low-skilled drivers. There are three key contributions. The first is the introduction of wavelet transform to analyze the frequency character of driver operations. The second is a feature extraction technology based on AdaBoost, which selects a small number of critical operation features between high- and low-skilled drivers. The third is a simple definition for high- and low-skilled drivers. We performed a series of experiments using a driving simulator on a specially designed course including several curves and then used the proposed method to extract driving operation features showing the difference between the two groups.
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F2012–I01-016
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Acknowledgments
The author Li was supported through the Global COE Program, “Global Center of Excellence for Mechanical Systems Innovation,” by the Ministry of Education, Culture, Sports, Science and Technology.
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Li, S. et al. (2013). Dominant Driving Operations in Curve Sections Differentiating Skilled and Unskilled Drivers. In: Proceedings of the FISITA 2012 World Automotive Congress. Lecture Notes in Electrical Engineering, vol 200. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33838-0_6
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DOI: https://doi.org/10.1007/978-3-642-33838-0_6
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