SmartDriveAuth: Enhancing Vehicle Security with Continuous Driver Authentication via Wearable PPG Sensors and Deep Learning

Abstract

The paper introduces a novel approach for continuous driver authentication in vehicle security, utilizing wearable photoplethysmography (PPG) sensors and Long Short-Term Memory (LSTM)–based deep learning. This study aims to overcome the limitations of traditional one-time authentication (OTA) methods, which typically involve passwords, PINs, or physical keys. While effective for initial identity verification, these conventional methods do not continuously validate the driver’s identity during vehicle operation. The proposed system leverages an LSTM-based prediction model to efficiently predict the subsequent PPG values using the raw PPG signals from wrist-worn devices. The predicted values are continuously compared with actual real-time data (received from the sensors) for authentication. The proposed system eliminates the need to permanently store user biometrics in a database. Motion artifacts and momentary disruptions have minimal impact on system performance. Experimental validation was conducted with 15 participants driving in varied conditions to simulate real-life driving conditions. The study evaluated the system’s accuracy, achieving an Equal Error Rate (EER) of 4.8%, demonstrating its potential as a viable solution for continuous driver authentication in dynamic environments.