In-Car Driver Authentication Using Wireless Sensing

Abstract

Automobiles have become an essential part of everyday lives. In this work, we attempt to make them smarter by introducing the idea of in-car driver authentication using wireless sensing. Our aim is to develop a model which can recognize drivers automatically. Firstly, we address the problem of "changing in-car environments", where the existing wireless sensing based human identification system fails. To this end, we build the first in-car driver radio biometric dataset to understand the effect of changing environments on human radio biometrics. This dataset consists of radio biometrics of five people collected over a period of two months. We leverage this dataset-to create machine learning (ML) models that make the proposed system adaptive to new in-car environments. We obtained a maximum accuracy of 99.3% in classifying two drivers and 90.66% accuracy in validating a single driver.