CPBW: A Change-Point-Detection and Bag-of-Words Based Mechanism Utilizing Smartphone Triaxial Accelerometer Data for Driver Identification
Journal
IEEE Internet of Things Journal
Start Page
1-1
ISSN
2327-4662
2372-2541
Date Issued
2024
Author(s)
Yu-Ming Chen
En-Hau Yeh
Shun-Ren Yang
Rongxing Lu
Abstract
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 article, we propose CPBW, a novel mechanism that combines change point detection and Bag-of-Words (BoW). The CPBW utilizes the smartphone triaxial accelerometer data to accurately identify drivers. The key innovation of CPBW lies in its exceptional efficiency within short time windows, significantly enhancing the real-time performance. The study adopts naturalistic driving studies, collecting the 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 the 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.
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Type
journal article