Driver Clustering According to the Ratio of Dangerous Behavior Using Machine Learning Algorithms | Zendy

Natalya Dmitriyevna Badanina | Zendy; Vladimir Anatolievich Sudakov | Zendy

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Driver Clustering According to the Ratio of Dangerous Behavior Using Machine Learning Algorithms

Author(s) -

Natalya Dmitriyevna Badanina,

Vladimir Anatolievich Sudakov

Publication year - 2022

Publication title -

modelling and data analysis

Language(s) - English

Resource type - Journals

eISSN - 2311-9454

pISSN - 2219-3758

DOI - 10.17759/mda.2022120101

Subject(s) - cluster analysis , histogram , computer science , segmentation , cluster (spacecraft) , data mining , algorithm , k means clustering , quality (philosophy) , artificial intelligence , machine learning , image (mathematics) , philosophy , epistemology , programming language

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.

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