Incremental Learning Assisted Dynamic Driver Identification: A New Perspective

With the popularity of intelligent and connected vehicles, driver identification based on driver behaviors, which has great significance in driving safety, vehicle alarm system and other situations, attracts more and more attention. However, the implementation of driver identification also faces some problems, such as insufficient computing power, slow transmission rate and so on. In order to solve these problems, people use mobile edge computing technology to transfer the computing process, which makes drive identification more feasible in practical scenarios. Many researchers have done a lot of works for the accuracy of driver identification. Nonetheless, few of them pay attention to the identification efficiency in the scenarios where drivers are constantly added to the model. Different from the works before, we propose a new driver identification scheme based on incremental learning for the first time, which can greatly improve the efficiency of model retraining when new driver is added. We extract the driver behavior characteristics data from two physical vehicles and train an incremental learning model in edge server. The experimental results show that the our scheme can accurately identify drivers. With the number of drivers in model increasing, although the identification accuracy slowly decreases, it remains above 93%, which means our scheme against the catastrophic forgetting well. Comparing with the existing identification schemes, our scheme requires less time and memory resources for model retraining, which is suitable for practical scenarios.