時系列クラスタリング手法を用いた実環境下での運転行動評価に関する研究

～時系列の形状的・時間的成分を反映した類似度計算～

抄録

There are drivers who are prone to accidents. To find drivers with high crash risk, in-cabin systems are required to monitor and evaluate driver’s daily driving behavior. Recently, a large of driving data has been recroded, but it is difficult to label them. Therefore, in this study, we propose to develop a time series clustering based driving behavior evaluation system, which does not require pre-defined rules or labels. A k-means method with Dynamic Time Warping (DTW) and DTW Barycenter Averaging (DBA) is used to cluster time series data. Conventional DTW was improved to deal with temporal differences between time series. The experimental resuls show that the proposed system could effectively detect abnormal driving behaior, and is more effective in actual driving conditions than rule-based methods.