CPBW: A Change-Point-Detection and Bag-of-Words Based Mechanism Utilizing Smartphone Triaxial Accelerometer Data for Driver Identification

Effective driver identification is one of critical aspects of Internet of Vehicles (IoV) applications, playing a pivotal role in various contexts such as vehicle anti-theft, fleet management, personalized insurance, vehicle settings automation, digital forensics, and so on. In this paper, we propose CPBW, a novel mechanism that combines Change Point Detection and Bag-of-Words. The CPBW utilizes smartphone triaxial accelerometer data to accurately identify drivers. The key innovation of CPBW lies in its exceptional efficiency within short time windows, significantly enhancing real-time performance. The study adopts Naturalistic Driving Studies, collecting unrestricted real-world data to increase applicability. However, challenges arise from dynamic urban environments influencing driving behavior and the need to balance hardware costs, privacy concerns, and data reliability. In comparison to previous methodologies, CPBW demonstrates a reduced time requirement for driver identification. Particularly, our proposed CPBW mechanism showcases impressive performance, achieving accuracy, precision, recall, and F1-score up to 98.1%, 98.1%, 98.1%, and 98.0%, respectively. As a result, CPBW markedly enhances the practicality of driver identification in real-world scenarios.