Driver Identification Using Vehicular Sensing Data: A Deep Learning Approach

Abstract

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 ubiquitous presence of smartphones as well as the broad range of state-of-the-art sensors have paved the way to collect huge amount of data from both vehicles and drivers. This paper addresses the necessity of having a large volume of labeled data for driver identification and presents a novel methodology to identify drivers based on their driving behavior analysis. The proposed architecture benefits from triplet loss training for driving time series in an unsupervised approach. An encoder architecture based on exponentially dilated causal convolutions is employed to obtain the representations. An SVM classifier is then trained on top of the representations to predict the person behind the wheel. The experiment results demonstrated higher performance of the proposed methodology when compared to benchmark methods.