On-Board Diagnostics and Driver Profiling

Navneeth, S; Prithvil, K P; Hari, N R Sri; Thushar, R; Rajeswari, M

doi:10.1109/icccs49678.2020.9277449

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…The Android application receives data through Bluetooth from the car's ECU via the OBD-II adapter. This hexadecimal data is processed by the Android app to generate the DTCs or fault codes, which are then shown to the user [7]. The Table.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Approaches for In-Vehicle Failure Prognosis in Automobiles: A Review

Rasheed,

Qazi,

Dur e Shawar Agha

et al. 2024

VFAST trans. softw. eng.

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The automobile industry has a growing need for reliable and safe health monitoring systems equipped with low-cost sensor networks and intelligent algorithms. This paper provides an overview of approaches already exist, used in on-board health monitoring systems for vehicles. It focuses on the methodologies, theories, and applications employed in the data measurement and data analysis systems of vehicle (cars) on-board health monitoring systems. A fault detection and diagnosis system, which is accurate, plays a vital role in ensuring the safety of autonomous vehicles by preventing potentially dangerous situations. This study focuses on emphasizing a fault diagnosis system that utilizes hybrid methods. Among the various options considered in this analysis, internal sensors emerge as the preferred choice due to their numerous benefits, including affordability, durability, widespread availability, ease of access, and low energy consumption. Model-based methods require various techniques that may introduce errors to estimation results, while signal-based methods necessitate a time-consuming process of including all possible conditions in a pre-built database. Based on this review, future development trends in designing new low-cost health monitoring systems for vehicles are also discussed.

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Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Approaches for In-Vehicle Failure Prognosis in Automobiles: A Review

Rasheed,

Qazi,

Dur e Shawar Agha

et al. 2024

VFAST trans. softw. eng.

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show abstract

“…The results of the different examined algorithms indicated that for this specific dataset the RF approach performed in the best way [ 18 ]. Also, a similar approach was performed by Navneeth et al [ 19 ] who, through an OBD II connector and a smartphone, collected data and analyzed them using ML and especially the K-nearest neighbors (KNN) algorithm in order to cluster the driver behaviour and calculate a score which indicates how risky the driver’s behaviour is. Carvalho et al [ 20 ] utilized only data that can be collected via the smartphone’s sensors such as acceleration, position, etc.…”

Section: Related Workmentioning

confidence: 99%

Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data

Πεππές

Alexakis

Adamopoulou

et al. 2021

Sensors

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In the last few decades, vehicles are equipped with a plethora of sensors which can provide useful measurements and diagnostics for both the vehicle’s condition as well as the driver’s behaviour. Furthermore, the rapid increase for transportation needs of people and goods together with the evolution of Information and Communication Technologies (ICT) push the transportation domain towards a new more intelligent and efficient era. The reduction of CO2 emissions and the minimization of the environmental footprint is, undeniably, of utmost importance for the protection of the environment. In this light, it is widely acceptable that the driving behaviour is directly associated with the vehicle’s fuel consumption and gas emissions. Thus, given the fact that, nowadays, vehicles are equipped with sensors that can collect a variety of data, such as speed, acceleration, fuel consumption, direction, etc. is more feasible than ever to put forward solutions which aim not only to monitor but also improve the drivers’ behaviour from an environmental point of view. The approach presented in this paper describes a holistic integrated platform which combines well-known machine and deep learning algorithms together with open-source-based tools in order to gather, store, process, analyze and correlate different data flows originating from vehicles. Particularly, data streamed from different vehicles are processed and analyzed with the utilization of clustering techniques in order to classify the driver’s behaviour as eco-friendly or not, followed by a comparative analysis of supervised machine and deep learning algorithms in the given labelled dataset.

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“…Empirical studies examining the amount of human influence in the field of IT security are lacking [18]. Some existing empirical studies have analyzed user perception, behaviour, and attitude towards computer ethics and information security [3][4][5] because computer security and ethics are the essential components of a management information system [6]. Navneeth et al [5] examined the driver's profile and behaviour.…”

Section: Introductionmentioning

confidence: 99%

“…Some existing empirical studies have analyzed user perception, behaviour, and attitude towards computer ethics and information security [3][4][5] because computer security and ethics are the essential components of a management information system [6]. Navneeth et al [5] examined the driver's profile and behaviour. Ameen et al [6] identified driving behaviours to reduce the risk of accidents based on real-time data recorded from vehicles and reference data provided by previous researchers.…”

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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On-Board Diagnostics and Driver Profiling

Cited by 5 publications

References 7 publications

Machine Learning Approaches for In-Vehicle Failure Prognosis in Automobiles: A Review

Machine Learning Approaches for In-Vehicle Failure Prognosis in Automobiles: A Review

Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data

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

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