A Novel Hybrid Approach for Segmenting Drivers Based on Their Driving Style

<div class="section abstract"><div class="htmlview paragraph">Drivers present diverse landscapes with their distinct personalities, preferences, and driving habits influenced by many factors. Though drivers' behavior is highly variable, they can exhibit clear patterns that make sorting them into one category or another possible. Discrete segmentation provides an effective way to categorize and address the differences in driving style. The segmentation approach offers many benefits, including simplification, measurement, proven methodology, customization, and safety. Numerous studies have investigated driving style classification using real-world vehicle data. These studies employed various methods to identify and categorize distinct driving patterns, including naturalist differences in driving and field operational tests. This paper presents a novel hybrid approach for segmenting driver behavior based on their driving patterns. We leverage vehicle acceleration data to create granular driver segments by combining event and trip-based methodologies - subsequently, a clustering analysis groups drivers based on their performance during key driving events. The effectiveness of our proposed method is validated through a rigorous evaluation using a virtual driving simulator and three predefined driver types. The proposed approach showed promising accuracy and provided a reasonable and effective way to categorize the drivers. This method simplifies the complexity of driver behaviors, enables precise measurement, and leverages proven methodologies from other industries, ultimately contributing to safer and more personalized driving experiences. Driving style classification is a powerful means of invigorating and enriching research in many aspects of driving, especially within Autonomous Vehicles (AVs). This approach can potentially improve traffic safety and increase driver enjoyment and efficiency as fuel consumption.</div></div>