A lane and curve detection using novel pre-processing with OpenCV

Yalabaka, Srikanth; Tejaswi, Aravelli; Nethaji, Acha; Prasad, Ch. Rajendra; Vamshi, Konne; Kumar, Naveen

doi:10.1063/5.0198890

International Conference on Intelligent and Smart Computation (Iciasc-2023)

2024

DOI: 10.1063/5.0198890

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A lane and curve detection using novel pre-processing with OpenCV

Srikanth Yalabaka,

Aravelli Tejaswi,

Acha Nethaji

et al.

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A Framework for Optimizing Deep Learning-Based Lane Detection and Steering for Autonomous Driving

Yordanov,

Chakraborty,

Hasan

et al. 2024

Sensors

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Improving the ability of autonomous vehicles to accurately identify and follow lanes in various contexts is crucial. This project aims to provide a novel framework for optimizing a self-driving vehicle that can detect lanes and steer accordingly. A virtual sandbox environment was developed in Unity3D that provides a semi-automated procedural road and driving generation framework for a variety of road scenarios. Four types of segments replicate actual driving situations by directing the car using strategically positioned waypoints. A training dataset thus generated was used to train a behavioral driving model that employs a convolutional neural network to detect the lane and ensure that the car steers autonomously to remain within lane boundaries. The model was evaluated on real-world driving footage from Comma.ai, exhibiting an autonomy of 77% in low challenge road conditions and of 66% on roads with sharper turns.

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A Framework for Optimizing Deep Learning-Based Lane Detection and Steering for Autonomous Driving

Yordanov,

Chakraborty,

Hasan

et al. 2024

Sensors

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show abstract

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A lane and curve detection using novel pre-processing with OpenCV

Cited by 1 publication

References 19 publications

A Framework for Optimizing Deep Learning-Based Lane Detection and Steering for Autonomous Driving

A Framework for Optimizing Deep Learning-Based Lane Detection and Steering for Autonomous Driving

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