Driver Identification with OBD-II Public Data

Uvarov, Kirill; Ponomarev, Andrew

doi:10.23919/fruct50888.2021.9347648

Cited by 12 publications

(14 citation statements)

References 11 publications

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Section: Introductionmentioning

confidence: 99%

“…At most three sensors named fuel level, coolant temperature, and oil pressure were furnished last century until the 70th year. The sensors were very simple because the driver was informed regarding the features of the engine and the amount of fuel through the magnetoelectric and light display devices [2]. Nowadays, cars are equipped with many microcomputers.…”

Section: Introductionmentioning

confidence: 99%

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A machine learning approach for driver identification

Khan

Ali

Haque

et al. 2023

IJEECS

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A machine learning approach for driver identification

Khan

Ali

Haque

et al. 2023

IJEECS

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“…They mentioned an acceleration level of approximately ±2 m/s 2 for safe driving behaviour and ±4 m/s 2 for aggressive drivers with a risk of collision. Uvarov et al [11] identified drivers with sensors listed in SAE J1979 specification, but the accuracy declined by approximately 15%. Sivaraj et al [12] compared the standard limits of different OBD II parameters, such as speed, acceleration, retardation, and jerk.…”

Section: Introductionmentioning

confidence: 99%

Driving pattern analysis to determine driver behaviors for local authority based on cloud using OBD II

Singh

2022

Int. j. electr. comput. eng. syst. (Online)

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Aggressive driving is the main cause of road accidents and it is affected by driving behavior which endanger not only the driver himself but also the people around. It is very significant step to identify such behaviors of the drivers by the local authorities which would help in correcting the behaviors or to understand the root cause of the accidents by analyzing the data recorded by the On Board Diagnostic( OBD ) II device. An aggressive driving behavior is characterized by sudden change inmaneuverings of vehicle which eventually yields non uniform parameters values returned by the ECU (Engine Control Unit) system without any specific reason. In this research work, the real time data is recorded from ECU using OBD II and the accelerometer. The Artificial Intelligenceis used in grouping the different types of data toidentifythe behaviors data on the basis of similarity of datapoints.The purpose of this research work is to identify such drivers and reduce the risk of further accidents.The work identifies the behaviors as bad, normal and aggressive behavior. As the clustering is made on basis crowded data which signifies the similar driving patterns for most of the time in the course of recording, therefore, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) unsupervised learning algorithm was used. The data will be sent to the cloud so that it can be accessed by the authority from any place for further action.ANOVA test is conducted usingIBMSPSS(Statistical Package for the Social Sciences) package to compare and determine the best method to collect data by comparing the means between groups.

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“…Driver fingerprinting could lead to important privacy compromises [1]. Today we cannot consider just a vehicle as a modern car, as it is a fully decorated smart device with various functions like multimedia, security system, and different sensors [2]. The sensors were very simple because the driver was informed regarding the features of the engine and the amount of fuel through the magnetoelectric and light display devices [2].…”

Section: Introductionmentioning

confidence: 99%

“…Today we cannot consider just a vehicle as a modern car, as it is a fully decorated smart device with various functions like multimedia, security system, and different sensors [2]. The sensors were very simple because the driver was informed regarding the features of the engine and the amount of fuel through the magnetoelectric and light display devices [2]. Using state-of-the-art technology in real-time all the microcomputers are communicated with each other through CAN-BUS (Controller Area Network) [3].…”

Section: Introductionmentioning

confidence: 99%

A Novel Compound Feature based Driver Identification

Khan¹,

Ali²,

Fazlul³

et al. 2022

IJACSA

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In today's world, it is time to identify the driver through technology. At present, it is possible to find out the driving style of the drivers from every car through controller area network (CAN-BUS) sensor data which was not possible through the conventional car. Many researchers did their work and their main purpose was to find out the driver driving style from end-to-end analysis of CAN-BUS sensor data. So, it is potential to identify each driver individually based on the driver's driving style. We propose a novel compound featurebased driver identification to reduce the number of input attributes based on some mathematical operation. Now, the role of machine learning in the field of any type of data analysis is incomparable and significant. The state-of-the-art algorithms have been applied in different fields. Occasionally these are tested in a similar domain. As a result, we have used some prominent algorithms of machine learning, which show different results in the field of aspiration of the model. The other goal of this study is to compare the conspicuous classification algorithms in the index of performance metrics in driver behavior identification. Hence, we compare the performance of SVM, Naïve Bayes, Logistic Regression, k-NN, Random Forest, Decision tree, Gradient boosting.

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Driver Identification with OBD-II Public Data

Cited by 12 publications

References 11 publications

A machine learning approach for driver identification

A machine learning approach for driver identification

Driving pattern analysis to determine driver behaviors for local authority based on cloud using OBD II

A Novel Compound Feature based Driver Identification

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