Smartwatch-Based Open-Set Driver Identification by Using GMM-Based Behavior Modeling Approach

Abstract

Driver identification must be studied because of the development of telematics and Internet of Things applications. Many application services require an accurate account of a driver's identity; for example, usage-based insurance may require a remote collection of data regarding driving. Recently, a Gaussian mixture model (GMM)-based behavioral modeling approach has been successfully developed for smartwatch-based driver authentication. This study extends the GMM-based behavioral modeling approach from driver authentication to open-set driver identification. Because the proposed approach can help for identifying illegal users, it is highly suitable for real-world conditions. According to a review of the relevant literature, this study proposed the first smartwatch-based driver identification system. This study proposed three open-set driver identification methods for different application domains. The result of this research provides a reference for designing driver identification systems. To demonstrate the feasibility of the proposed method, an experimental system that evaluates the performance of the driver identification method in simulated and real environments was proposed. The experimental results for the three proposed methods of driver identification illustrated an equal error rate (EER) of 11.19%, 10.65%, and 10.50% under a simulated environment and an EER of 17.95%, 17.07%, and 16.66% under a real environment.