A Machine Learning Approach for Driver Identification Based on CAN-BUS Sensor Data

TL;DR

This paper presents a supervised machine learning approach to identify drivers based on CAN-BUS sensor data, addressing protocol variation issues and demonstrating promising accuracy with different driver datasets.

Contribution

It introduces a driver identification method using CAN-BUS data and supervised learning, handling protocol variations and providing experimental validation.

Findings

Achieved high accuracy with 10 drivers on complete data set.

Demonstrated effective driver identification with 2 drivers on partial data.

Statistically significant results compared to baseline algorithms.

Abstract

Driver identification is a momentous field of modern decorated vehicles in the controller area network (CAN-BUS) perspective. 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 the protocol of different models of vehicle. Our aim is to identify the driver through supervised learning algorithms based on driving behavior analysis. To determine 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 the 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 complete data set with 10 drivers and a partial data set with two drivers. The accuracy is good with less number of drivers compared to the higher number of drivers. We have achieved statistically significant results in terms of accuracy in contrast to the baseline algorithm