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Machine learning based real-time vehicle data analysis for safe driving modeling

Authors:

Pamul Yadav,

Sangsu Jung,

Dhananjay SinghAuthors Info & Claims

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

Pages 1355 - 1358

https://doi.org/10.1145/3297280.3297584

Published: 08 April 2019 Publication History

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Abstract

This paper identifies a necessity to evaluate the Meta features of vehicles which could be helpful in improving the vehicle driver's skill to prevent accidents and also evaluate the change in the quality of cars over passing time. This paper does an analysis of the vehicle data using supervised learning based linear regression model that is used as an estimator for Driver's Safety Metrics and Economic Driving Metrics. The data collected was obtained from fifteen different drivers over a span of one month which accumulated over 15000 data points. And the metrics that we have devised have potential application in automotive technology analysis for developing an advanced intelligent vehicles. Also, we have presented a system for performing the real-time experiment based on the OnBoard-Diagnosis version II (OBD-II) scanner data. Finally, we have analyzed and presented the parameter accuracy over 80% for the driver's safety solution in real-world scenario.

References

[1]

Singh D, Singh M., "Internet of Vehicles for Smart and Safe Driving", International Conference on Connected Vehicles and Expo (ICCVE), Shenzhen, 19-23 Oct., 2015.

Google Scholar

[2]

Zhang, Y., Lin, W., and Chin, Y., "Data-Driven Driving Skill Characterization: Algorithm Comparison and Decision Fusion," SAE Technical Paper 2009-01-1286, 2009, C. L Cardoso.

Google Scholar

[3]

J. E. Meseguer, C. T. Calafate, J. C. Cano and P. Manzoni, "DrivingStyles: A smartphone application to assess driver behavior," 2013 IEEE Symposium on Computers and Communications (ISCC), Split, 2013, pp.000535--000540.

Google Scholar

[4]

Schneider, A., Hommel, G., & Blettner, M. (2010). Linear Regression Analysis: Part 14 of a Series on Evaluation of Scientific Publications. Deutsches Ärzteblatt International, 107(44), pp. 776--782.

Google Scholar

[5]

Kenneth L. Clarkson. 1985. Algorithms for Closest-Point Problems (Computational Geometry). Ph.D. Dissertation. Stanford University, Palo Alto, CA. UMI Order Number: AAT 8506171.

Digital Library

Google Scholar

[6]

Google Scholar

[7]

Goszczynska H., Kowalczyk L., Kuraszkiewicz B. (2014) Correlation Matrices as a Tool to Analyze the Variability of EEG Maps. In: Piętka E., Kawa J., Wieclawek W. (eds) Information Technologies in Biomedicine, Volume 4. Advances in Intelligent Systems and Computing, vol 284. Springer.

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Cited By

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Li JSong ZZhang ZLi YCao C(2024)In-Vehicle Digital Forensics for Connected and Automated Vehicles With Public AuditingIEEE Internet of Things Journal10.1109/JIOT.2023.331057811:4(6368-6383)Online publication date: 15-Feb-2024
https://doi.org/10.1109/JIOT.2023.3310578
Geglio AHedayati ETascillo MAnderson DBarker JHavens T(2022)Deep Convolutional Autoencoder for Assessment of Drive-Cycle Anomalies in Connected Vehicle Sensor Data2022 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI51031.2022.10022233(743-749)Online publication date: 4-Dec-2022
https://doi.org/10.1109/SSCI51031.2022.10022233
Kushwaha MAbirami M(2022)Comparative Analysis on the Prediction of Road Accident Severity Using Machine Learning AlgorithmsMicro-Electronics and Telecommunication Engineering10.1007/978-981-16-8721-1_26(269-280)Online publication date: 28-Feb-2022
https://doi.org/10.1007/978-981-16-8721-1_26
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Index Terms

Machine learning based real-time vehicle data analysis for safe driving modeling
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems

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Published In

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

April 2019

2682 pages

ISBN:9781450359337

DOI:10.1145/3297280

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University, Marietta, Georgia
,
George A. Papadopoulos
University of Cyprus, Nicosia, Cyprus

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 08 April 2019

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SAC '19: The 34th ACM/SIGAPP Symposium on Applied Computing

April 8 - 12, 2019

Limassol, Cyprus

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Li JSong ZZhang ZLi YCao C(2024)In-Vehicle Digital Forensics for Connected and Automated Vehicles With Public AuditingIEEE Internet of Things Journal10.1109/JIOT.2023.331057811:4(6368-6383)Online publication date: 15-Feb-2024
https://doi.org/10.1109/JIOT.2023.3310578
Geglio AHedayati ETascillo MAnderson DBarker JHavens T(2022)Deep Convolutional Autoencoder for Assessment of Drive-Cycle Anomalies in Connected Vehicle Sensor Data2022 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI51031.2022.10022233(743-749)Online publication date: 4-Dec-2022
https://doi.org/10.1109/SSCI51031.2022.10022233
Kushwaha MAbirami M(2022)Comparative Analysis on the Prediction of Road Accident Severity Using Machine Learning AlgorithmsMicro-Electronics and Telecommunication Engineering10.1007/978-981-16-8721-1_26(269-280)Online publication date: 28-Feb-2022
https://doi.org/10.1007/978-981-16-8721-1_26
Nouh RSingh MSingh D(2021)SafeDrive: Hybrid Recommendation System Architecture for Early Safety Predication Using Internet of VehiclesSensors10.3390/s2111389321:11(3893)Online publication date: 4-Jun-2021
https://doi.org/10.3390/s21113893
Singh MSingh M(2021)Vehicle Mobile Data Analysis for Driving Safety and SecurityInformation Security of Intelligent Vehicles Communication10.1007/978-981-16-2217-5_10(141-153)Online publication date: 19-May-2021
https://doi.org/10.1007/978-981-16-2217-5_10
Agrahari ASingh D(2020)Smart City Transportation Technologies: Automatic No-Helmet Penalizing SystemBlockchain Technology for Smart Cities10.1007/978-981-15-2205-5_6(115-132)Online publication date: 8-Feb-2020
https://doi.org/10.1007/978-981-15-2205-5_6
Signoretti GSilva MAraujo JSilva ISilva DFerrari PSisinni E(2019)A Dependability Evaluation for OBD-II Edge Devices: An Internet of Intelligent Vehicles Perspective2019 9th Latin-American Symposium on Dependable Computing (LADC)10.1109/LADC48089.2019.8995679(1-9)Online publication date: Nov-2019
https://doi.org/10.1109/LADC48089.2019.8995679

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Driving Maneuvers Analysis Using Naturalistic Highway Driving Data

Numerical Analysis of Tractor Accidents using Driving Simulator for Autonomous Driving Tractor

Road Traffic Accidents in Morocco: Exploratory Analysis of Driver, Vehicle, and Pedestrian Factors

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