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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM40257

AI-Enhanced Automatic Traffic Violation Detection and Challan Issuance using GNSS and OBU Sensors in Self-Driving Cars.

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM40257

Volume:

8 |

12 |

Dec 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM40257

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

AS

Arihant Srivastav

DNS

Dr Nupur Soni

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Abstract

The swift progress in self-driving vehicle technology requires strong systems to guarantee adherence to traffic regulations. This study introduces an AI-powered system for automatic detection of traffic violations and issuance of fines in self-driving vehicles utilizing Global Navigation Satellite System (GNSS) and On-Board Unit (OBU) sensors. The system combines AI algorithms with GNSS for accurate geolocation tracking and OBU sensors for monitoring vehicle status. Infractions like running signals, exceeding speed limits, and improper use of lanes are identified instantly. A main server analyzes violation data and automatically generates electronic fines for offenders. The suggested system improves adherence to traffic regulations, minimizes human involvement, and fosters safer roadways by utilizing AI’s predictive and analytical features. This novel strategy tackles shortcomings in current traffic enforcement techniques, rendering it a scalable answer for contemporary smart cities.

KEYWORDS

Autonomous Vehicles Traffic Violation Detection GNSS OBU Sensors AI Smart Cities Electronic Challan Traffic Compliance

Cite This Article

Arihant Srivastav, Dr Nupur Soni, "AI-Enhanced Automatic Traffic Violation Detection and Challan Issuance using GNSS and OBU Sensors in Self-Driving Cars.," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 12, Dec 2024. https://doi.org/10.55041/IJSREM40257

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Publication Date

12/30/2024

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Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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