Multi-label Lane Detection Algorithm for Autonomous Vehicle Using Deep Learning

Chae Song Park; Kyong Su Yi

doi:10.22680/kasa2024.16.1.029

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2024 Vol.16, Issue 1 Preview Page Next Page

Multi-label Lane Detection Algorithm for Autonomous Vehicle Using Deep Learning 자율주행 차량을 위한 멀티 레이블 차선 검출 딥러닝 알고리즘

31 March 2024. pp. 29-34

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Abstract

This paper presents a multi-label lane detection method for autonomous vehicles based on deep learning. The proposed algorithm can detect two types of lanes: center lane and normal lane. The algorithm uses a convolution neural network with an encoder-decoder architecture to extract features from input images and produce a multi-label heatmap for predicting lane’s label. This architecture has the potential to detect more diverse types of lanes in that it can add the number of labels by extending the heatmap’s dimension. The proposed algorithm was tested on an OpenLane dataset and achieved 85 Frames Per Second (FPS) in end to-end inference time. The results demonstrate the usability and computational efficiency of the proposed algorithm for the lane detection in autonomous vehicles.

Keywords

Lane detection

Autonomous vehicle

Deep learning

Autonomous driving perception

키워드

차선 검출

자율 주행

딥러닝

자율주행 인지

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Information

Publisher :Korean Auto-vehicle Safety Association
Publisher(Ko) :한국자동차안전학회
Journal Title :Journal of Auto-vehicle Safety Association
Journal Title(Ko) :자동차안전학회지
Volume : 16
No :1
Pages :29-34
Received Date : 2023-09-19
Revised Date : 2024-02-21
Accepted Date : 2024-02-22
DOI :https://doi.org/10.22680/kasa2024.16.1.029

Journal of Auto-vehicle Safety Association ISSN:2005-9396(Print) 자동차안전학회지

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