A Machine Learning Approach for Driver Identification

Abstract

Driver identification is a momentous field of modern decorated vehicles in the perspective of the controller area network (CAN-Bus). Many conventional systems are used to identify the driver. One step ahead, most of the researchers use sensor data of CAN-Bus but there are some difficulties because of the variation of a protocol of different models of vehicle. We aim to identify the driver through supervised learning algorithms based on driving behavior analysis. To identify the driver, a driver verification technique is proposed that evaluate driving pattern using the measurement of CAN sensor data. In this paper on-board diagnostic (OBD-II) is used to capture the data from CAN-Bus sensor and the sensors are listed under SAE J1979 statement. According to the service of OBD-II drive identification is possible. However, we have gained two types of accuracy on a full data set with 10 drivers and a partial data set with two drivers. The accuracy is good with less number of drivers compared to a higher number of drivers. We have achieved statistically significant results in terms of accuracy in contrast to the baseline algorithm.