Advanced Temporal Dilated Convolutional Neural Network for a Robust Car Driver Identification

The latest generation cars are often equipped with advanced driver assistance systems, usually known as ADAS (Advanced Driver Assistance Systems). These systems are able to assist the car driver by leveraging several levels of automation. Therefore, it is essential to adapt the ADAS technology to the car driver’s identity to personalize the provided assistance services. For these reasons, such car driver profiling algorithms have been developed by the scientific community. The algorithm herein proposed is able to recognize the driver’s identity with an accuracy close to 99% thanks to ad-hoc specific analysis of the driver’s PhotoPlethysmoGraphic (PPG) signal. In order to rightly identify the driver profile, the proposed approach uses a 1D Dilated Temporal Convolutional Neural Network architecture to learn the features of the collected driver’s PPG signal. The proposed deep architecture is able to correlate the specific PPG features with subject identity enabling the car ADAS services associated with the recognized identity. Extensive validation and testing of the developed pipeline confirmed its reliability and effectiveness.