WiFi-based driver’s activity recognition using multi-layer classification

Abstract

The driver’s activity monitoring has gained more consideration in advanced vehicular technology because of its abundant benefits for driving assistance systems. The driver’s activity recognition may also provide vital information about the state of driver attentiveness causing a substantial reduction in the number of fatal accidents. This paper aims to design an innovative framework to characterize in-vehicle activities using ubiquitous WiFi devices for the safety benefits of drivers. In our presented device-free system, driver’s activities are investigated as multi-class classification problem leveraging Channel State Information (CSI) of WiFi signals. In this novel approach, we have examined the capability of the Sparse Least Square Support Vector Machine (SLS-SVM) with Baye’s maximum likelihood estimation for better discrimination of in-vehicle activities. In this framework, multiple classifiers are arranged in a multi-layer hierarchy for higher scalability as compared to conventional single-layer methods. This sophisticated scheme leads to address the problem of driver’s activity recognition with significant improvement in performance. The proposed low-cost solution can provide an average recognition accuracy of 91.5% for valuable insight into the design of an autonomous driving system.