Driver Identification System Based on a Machine Learning Operations Platform Using Controller Area Network Data

Hyunseo Shin, Wangyu Park, Suhong Kim, Juhum Kweon, Changjoo Moon

Ensuring vehicle security and preventing unauthorized driving are critical in modern transportation. Traditional driver identification methods, such as biometric authentication, require additional hardware and may not adapt well to changing driving behaviors. This study proposes a real-time driver identification system leveraging a Machine Learning Operations (MLOps)-based platform that continuously re-trains a deep learning model using vehicle Controller Area Network (CAN) data. The system collects CAN data, converts them into Markov Transition Field (MTF) images, and classifies drivers using a ResNet-18 model deployed on the Google Cloud Platform (GCP). An automated pipeline utilizing Pub/Sub, GCP Composer, and Vertex AI ensures continuous model updates based on newly uploaded driving data. Our experimental results demonstrate that models trained only on recent data significantly outperform those incorporating historical data, highlighting the necessity of frequent retraining. The intruder detection system effectively identifies unregistered drivers, further enhancing vehicle security. By automating model retraining and deployment, this system provides an adaptive solution that accommodates evolving driving behaviors, reducing reliance on static models. These findings emphasize the importance of real-time data adaptation in driver authentication systems, contributing to enhanced vehicle security and safety.