Driver identification has emerged as an active field of study to further personalize the
integrated advanced driver-assistance systems into intelligent vehicles, provide security and …

With increasing focus on privacy protection, alternative methods to identify vehicle operator
without the use of biometric identifiers have gained traction for automotive data analysis. The …

Driver identification plays a pivotal role in the design of advanced driver assistant systems.
The continued development of in-vehicle networking systems, CAN-bus technology, and the …

As a powerful non-intrusive method, driver identification based on driving data analysis has
recently gained attention as it is beneficial for providing security, privacy, and …

The scarcity of driving data constrains the accuracy of deep learning (DL)-based driver
identification methods in practical application scenarios. To address this issue, this study …

The rapid development of intelligent transportation and Internet of Vehicles technology
provides a technical means for obtaining massive, real-time, and multi-dimensional driving …

Recent studies on machine learning based driver identification have shown that leveraging
deep neural networks to learn latent features from vehicle sensor data boosts the …

Driver identification has shown sustainable development in recent years in a wide variety of
applications including but not limited to security, personalization, fleet management …

Recently, the problem of driver classification has received considerable attention in the
literature. Most approaches formulate this problem as a classification task, in which the …

Recent studies have shown that both the quantity and quality of the driving data are crucial
for the success of deep learning based driver identification solutions. This is not only true for …