Conductor Classification Using OBD-II System Information

• Pedro H. A. Ribeiro UFPI
• José M. P. M. Júnior UFPI

Abstract

The sharp rise in crime, evident in the number of stolen vehicles between 2014 and 2017, is a determining factor in the development of systems capable of classifying drivers into publicly traded vehicles. This work then proposes an assessment of machine learning techniques for driver classification. The evaluation is based on the average accuracy, median, maximum and minimum values, standard deviation and execution time of each computational technique applied for this purpose. The analysis of the presented data shows that the accuracy of the technical production of a machine or of a sower must be analyzed, being necessary also an analysis of the execution time, and later, an application of the techniques in an author's own database.

Keywords: Machine Learning, OBD-II System, Driver Classification

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