An Evolutionary Approach to Driving Tendency Recognition for Advanced Driver Assistance Systems
Department of Computer Engineering, Sungkyunkwan University (SKKU), Suwon 440-746, Republic of Korea
Driving tendency recognition is important for constructing Advanced Driver Assistance Systems (ADAS). However, it had not been a lot of research using vehicle sensing data, due to the high difficulty to define it. In this paper, we attempt to improve the learning capability of a machine learning method using evolutionary computation. We propose a driving tendency recognition method, with consideration of data characteristics. Comparison of our classification system with conventional methods demonstrated the effectiveness and accuracy over 92% in our system. Our proposed evolutionary approach is confirmed that improve the classification accuracy of the learning method through evolution in the experiment.
© Owned by the authors, published by EDP Sciences, 2016
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