Driver Clustering According to the Ratio of Dangerous Behavior Using Machine Learning Algorithms
The paper conducts the research of defining dangerous driving of a vehicle using signals collected during the ride. A number of modern clustering models for drivers segmentation on classes based on the ratio of dangerous driving was used. New approach of data aggregation aiming to cluster data by signal distribution histograms was developed. Achieved results could be used in commercial systems that monitor the quality of drivers behavior in retrospective.