A Machine Learning Approach for Driver Identification Based on CAN-BUS Sensor Data
This addresses driver identification for vehicle security or personalization, but it is incremental as it applies existing supervised learning methods to a known problem with protocol variations.
The paper tackled driver identification using CAN-BUS sensor data and achieved statistically significant accuracy results, with better performance for fewer drivers (e.g., two drivers) compared to a larger set (e.g., 10 drivers).
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