UGC Approved Journal no 63975(19)
New UGC Peer-Reviewed Rules

ISSN: 2349-5162 | ESTD Year : 2014
Volume 12 | Issue 10 | October 2025

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Published in:

Volume 11 Issue 3
March-2024
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2403246


Registration ID:
534112

Page Number

c373-c378

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Title

Detection of Lane and Speed Breaker Warning System for Autonomous Vehicles using Machine Learning Algorithm

Abstract

Autonomous vehicles rely on a multitude of sensors and intelligent systems to navigate safely and efficiently. This paper presents a comprehensive Lane Detection and Speed Breaker Warning System designed to enhance the capabilities of autonomous vehicles using advanced machine learning algorithms. The primary goal is to improve the vehicle’s perception of the road environment, specifically focusing on accurate lane detection and timely recognition of speed breakers. The proposed system integrates a combination of computer vision techniques and machine learning algorithms to achieve robust performance in real-world scenarios. For lane detection, a convolutional neural network (CNN) is employed to analyze camera inputs and identify lane boundaries. This enables the vehicle to precisely follow the road markings, ensuring safe navigation within lanes. To address the challenge of speed breaker detection, a machine learning model is trained on a diverse dataset containing images of roads with varying types and conditions of speed breakers. The model is designed to classify road segments and predict the presence of speed breakers ahead. When a speed breaker is detected, the system activates a warning mechanism to alert the autonomous vehicle, allowing it to adjust its speed and suspension settings accordingly. The effectiveness of the proposed system is evaluated through extensive simulations and real-world testing scenarios. The results demonstrate a significant improvement in lane-keeping accuracy and the ability to anticipate and respond to speed breakers proactively. The Lane and Speed Breaker Warning System contributes to the overall safety and reliability of autonomous vehicles, making them better equipped to handle diverse road conditions.

Key Words

Autonomous Vehicles, Lane Detection, Speed Breaker Warning, Machine Learning, Convolutional Neural Network, Computer Vision.

Cite This Article

"Detection of Lane and Speed Breaker Warning System for Autonomous Vehicles using Machine Learning Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.c373-c378, March-2024, Available :http://www.jetir.org/papers/JETIR2403246.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Detection of Lane and Speed Breaker Warning System for Autonomous Vehicles using Machine Learning Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppc373-c378, March-2024, Available at : http://www.jetir.org/papers/JETIR2403246.pdf

Publication Details

Published Paper ID: JETIR2403246
Registration ID: 534112
Published In: Volume 11 | Issue 3 | Year March-2024
DOI (Digital Object Identifier):
Page No: c373-c378
Country: Pune, Maharashtra, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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