Abstract Our main objective was to develop a new driving assistance system that could help less experienced drivers improve their driving skills. We describe a statistical method we developed to extract distinctions between experienced and less experienced drivers. This paper makes three key contributions. The first involves a technology for feature extraction based on AdaBoost, which selects a small number of features critical for operation between experienced and less experienced drivers. The second involves a simple definition for experienced and less experienced drivers. The third involves the introduction of wavelet transforms that were used to analyze the frequency characteristics of driver operations. We performed a series of experiments using a driving simulator on a specially designed course that included several curves and then used the proposed method to extract features of driving operations that demonstrated the differences between the two groups.
Learning Characteristic Driving Operations in Curve Sections that Reflect Drivers’ Skill Levels
International Journal of Intelligent Transportation Systems Research ; 12 , 3 ; 135-145
2014-03-15
11 pages
Article (Journal)
Electronic Resource
English
Driver behavior , Driving simulator , Driving skill , Features extraction , Curve sections Engineering , Electrical Engineering , Automotive Engineering , Robotics and Automation , Computer Imaging, Vision, Pattern Recognition and Graphics , Civil Engineering , User Interfaces and Human Computer Interaction
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