A Machine Learning Approach for Driver Identification Based on CAN-BUS Sensor Data
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
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningDiagnosticDriver IdentificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Discrete Wavelet Transform for Generative Adversarial Network to Identify Drivers Using Gyroscope and Accelerometer Sensors
Driver identification is a central research area in intelligent transportation systems, with applications in commercial freight transport and usage-based insurance. One way to perform the identification is to use smartph…
ClassificationData AugmentationDriver AuthenticationDriver Identification+2Automatic Driver Identification from In-Vehicle Network Logs
Data generated by cars is growing at an unprecedented scale. As cars gradually become part of the Internet of Things (IoT) ecosystem, several stakeholders discover the value of in-vehicle network logs containing the meas…
Driver IdentificationDriver2vec: Driver Identification from Automotive Data
With increasing focus on privacy protection, alternative methods to identify vehicle operator without the use of biometric identifiers have gained traction for automotive data analysis. The wide variety of sensors instal…
Autonomous DrivingDriver IdentificationTripletDriver Identification Using Automobile Sensor Data from a Single Turn
As automotive electronics continue to advance, cars are becoming more and more reliant on sensors to perform everyday driving operations. These sensors are omnipresent and help the car navigate, reduce accidents, and pro…
Driver IdentificationNavigateTime Series AnalysisTime Series ClassificationOne shot learning based drivers head movement identification using a millimetre wave radar sensor
Concentration of drivers on traffic is a vital safety issue; thus, monitoring a driver being on road becomes an essential requirement. The key purpose of supervision is to detect abnormal behaviours of the driver and pro…
One-Shot Learning