Real Time Road Lane Detection using Computer Vision Techniques in Python

Joy, Salna; Mamta, B S; Mukesh, Tejas B; Ahmed, Mohammed Muzammil; Kiran, Uday

doi:10.1109/icacrs55517.2022.10029238

Cited by 11 publications

(4 citation statements)

References 11 publications

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“…The quest for effective lane detection algorithms has been ongoing for several decades, evolving from simple edge-based techniques to complex machine learning models. Initial methods relied on straightforward image processing operations, such as edge detection filters and region-of-interest selection as in [4], which were limited to well-marked and clear weather conditions.…”

Section: Evolution Of Lane Detection Techniquesmentioning

confidence: 99%

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Convision - a Context-Based Deep Learning Algorithm for Efficient Lane Detection

Needhi,

et al. 2024

Preprint

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Section: Evolution Of Lane Detection Techniquesmentioning

confidence: 99%

“…As autonomous vehicles ventured into more dynamic and less controlled environments, the limitations of these early methods became apparent in [5]. They struggled with diverse challenges such as shadows, lane occlusions, and variable lighting conditions.…”

Section: Robustness Challenges and Solutionsmentioning

confidence: 99%

Convision - a Context-Based Deep Learning Algorithm for Efficient Lane Detection

Needhi,

et al. 2024

Preprint

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“…In today's automotive industry, major companies such as General Motors, Volkswagen, and Hyundai have invested extensive research in advancing autonomous vehicle technologies (Jia and He, 2022;Kumar et al, 2022;Chen et al, 2023;Jung et al, 2020). The core of selfdriving car operation lies in its ability to mimic human driver behavior when choosing driving modes and lanes (Joy et al, 2022;Ma et al, 2021). However, self-driving cars should not have options for reckless and negligent driving that can endanger other drivers and passengers on the roads.…”

Section: Introductionmentioning

confidence: 99%

“…, 2020). The core of self-driving car operation lies in its ability to mimic human driver behavior when choosing driving modes and lanes (Joy et al. , 2022; Ma et al.…”

Section: Introductionmentioning

confidence: 99%

Procedure for describing traffic situation scene development

Korotysheva,

Zhukov

2024

IJIUS

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PurposeThis study aims to comprehensively address the challenge of delineating traffic scenarios in video footage captured by an embedded camera within an autonomous vehicle.Design/methodology/approachThis methodology involves systematically elucidating the traffic context by leveraging data from the object recognition subsystem embedded in vehicular road infrastructure. A knowledge base containing production rules and logical inference mechanism was developed. These components enable real-time procedures for describing traffic situations.FindingsThe production rule system focuses on semantically modeling entities that are categorized as traffic lights and road signs. The effectiveness of the methodology was tested experimentally using diverse image datasets representing various meteorological conditions. A thorough analysis of the results was conducted, which opens avenues for future research.Originality/valueOriginality lies in the potential integration of the developed methodology into an autonomous vehicle’s control system, working alongside other procedures that analyze the current situation. These applications extend to driver assistance systems, harmonized with augmented reality technology, and enhance human decision-making processes.

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