Abstract
This paper describes a rule-based Driver Scoring System model, derived from behavioral data collected using a driving simulator. It introduces a novel approach to establish driver profiles through feature engineering of acquired dataset, with features evaluating various aspects of driver behavior. The research aims to provide employers and drivers with profile-specific feedback and recommendations to design training protocols. Principal Component Analysis is applied on preprocessed dataset from 412 drivers for dimensionality reduction and feature selection. The K-means clustering algorithm is used for data analysis, resulting in three distinct clusters. The Kruskal-Wallis test, supplemented by post hoc Dunn testing is employed to determine statistical significance between clusters. Clusters are portrayed using descriptive statistics, specifically the mean scores and overall driver performance averages. Our method delineates three driver profiles, with two driving styles reflecting desirable driving skills and good overall performance, while the third represents unacceptable driving skills and bad overall performance.