Multi-Feature Agglomerative Hierarchical Clustering Based Abnormal Driving Behavior Detection

Driving behavior detection and abnormal identification is a cornerstone to assessing drivers’ driving behavior and improving driving safety. In this study, we propose a multi-feature agglomerative hierarchical clustering method to detect abnormal driving. First, several typical abnormal driving behaviors are analyzed and their motion characteristics are extracted, and a feature similarity measurement strategy is proposed based on structural distance. Then, these features are clustered and analyzed according to the Laplacian transform of the feature matrix, which can effectively reduce the dimensionality of clustering data and achieve the automatic acquisition of the number of clusters. Finally, an experiment is implemented to test the effectiveness of proposed detection method. The results show that this method can accurately determine three typical abnormal driving behaviors; overspeed, rapid acceleration/deceleration, and frequent lane change. These results demonstrate the study’s potential to improve the abnormal driving behavior and enhance driving safety.