A Vision-based Lane Departure Warning Framework

Jiaju Wu; Pengshuai Yin; Xin Shu; Huichou Huang; Fei Liu; Qingyao Wu

doi:10.1109/ICEBE52470.2021.00029

A Vision-based Lane Departure Warning Framework

Jiaju Wu
, Pengshuai Yin
, Xin Shu
, Huichou Huang
, Fei Liu^*
, Qingyao Wu^*

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

Abstract

Lane departure warning is an essential function of driver assistance systems. In this paper, we propose a vision-based lane departure warning framework, which can determine whether the vehicle deviates from the lane only with images as input. Our framework combines the deep learning method and traditional line detection algorithm. Specifically, the framework consists of a Lane Boundaries Localization module, a Lane Boundaries Generator module, and a Departure Warning module. The Lane Boundaries Localization module is a deep neural network for finding the area containing lane Boundaries. Then the Lane Boundaries Generator module performs backbone detection and line detection to generate lane boundaries on the located area. After identifying the lane boundaries, the Departure Warning module can quickly determine whether the vehicle deviates from the lane. Additionally, we implemented and deployed our framework to an Android platform and tested its running speed. ©2021 IEEE.

Original language	English
Title of host publication	Proceedings - 2021 IEEE International Conference on e-Business Engineering, ICEBE 2021
Place of Publication	Los Alamitos, Calif.
Publisher	IEEE
Pages	139-143
ISBN (Electronic)	978-1-6654-4418-7
ISBN (Print)	978-1-6654-4419-4
DOIs	https://doi.org/10.1109/ICEBE52470.2021.00029
Publication status	Published - Nov 2021
Externally published	Yes
Event	17th IEEE International Conference on e-Business Engineering (ICEBE 2021) - Guangzhou, China Duration: 12 Nov 2021 → 14 Nov 2021

Publication series

Name	International Conference on e-Business Engineering

Conference

Conference	17th IEEE International Conference on e-Business Engineering (ICEBE 2021)
Place	China
City	Guangzhou
Period	12/11/21 → 14/11/21

Funding

This work was supported by National Natural Science Foundation of China (NSFC) 61876208, Key-Area Research and Development Program of Guangdong Province 2018B010108002, and National Natural Science Foundation of China (NSFC) 61873094.

Research Keywords

lane departure warning
lane detection
deep learning
Hough transform
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10.1109/ICEBE52470.2021.00029

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@inproceedings{32eebe0210cd4c4ab93f21f3634f7719,

title = "A Vision-based Lane Departure Warning Framework",

abstract = "Lane departure warning is an essential function of driver assistance systems. In this paper, we propose a vision-based lane departure warning framework, which can determine whether the vehicle deviates from the lane only with images as input. Our framework combines the deep learning method and traditional line detection algorithm. Specifically, the framework consists of a Lane Boundaries Localization module, a Lane Boundaries Generator module, and a Departure Warning module. The Lane Boundaries Localization module is a deep neural network for finding the area containing lane Boundaries. Then the Lane Boundaries Generator module performs backbone detection and line detection to generate lane boundaries on the located area. After identifying the lane boundaries, the Departure Warning module can quickly determine whether the vehicle deviates from the lane. Additionally, we implemented and deployed our framework to an Android platform and tested its running speed. {\textcopyright}2021 IEEE.",

keywords = "lane departure warning, lane detection, deep learning, Hough transform, LINES",

author = "Jiaju Wu and Pengshuai Yin and Xin Shu and Huichou Huang and Fei Liu and Qingyao Wu",

year = "2021",

month = nov,

doi = "10.1109/ICEBE52470.2021.00029",

language = "English",

isbn = "978-1-6654-4419-4",

series = "International Conference on e-Business Engineering",

publisher = "IEEE",

pages = "139--143",

booktitle = "Proceedings - 2021 IEEE International Conference on e-Business Engineering, ICEBE 2021",

address = "United States",

note = "17th IEEE International Conference on e-Business Engineering (ICEBE 2021) ; Conference date: 12-11-2021 Through 14-11-2021",

}

Wu, J, Yin, P, Shu, X, Huang, H, Liu, F & Wu, Q 2021, A Vision-based Lane Departure Warning Framework. in Proceedings - 2021 IEEE International Conference on e-Business Engineering, ICEBE 2021. International Conference on e-Business Engineering, IEEE, Los Alamitos, Calif., pp. 139-143, 17th IEEE International Conference on e-Business Engineering (ICEBE 2021), Guangzhou, China, 12/11/21. https://doi.org/10.1109/ICEBE52470.2021.00029

A Vision-based Lane Departure Warning Framework. / Wu, Jiaju; Yin, Pengshuai; Shu, Xin et al.
Proceedings - 2021 IEEE International Conference on e-Business Engineering, ICEBE 2021. Los Alamitos, Calif.: IEEE, 2021. p. 139-143 (International Conference on e-Business Engineering).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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AU - Wu, Jiaju

AU - Yin, Pengshuai

AU - Shu, Xin

AU - Huang, Huichou

AU - Liu, Fei

AU - Wu, Qingyao

PY - 2021/11

Y1 - 2021/11

N2 - Lane departure warning is an essential function of driver assistance systems. In this paper, we propose a vision-based lane departure warning framework, which can determine whether the vehicle deviates from the lane only with images as input. Our framework combines the deep learning method and traditional line detection algorithm. Specifically, the framework consists of a Lane Boundaries Localization module, a Lane Boundaries Generator module, and a Departure Warning module. The Lane Boundaries Localization module is a deep neural network for finding the area containing lane Boundaries. Then the Lane Boundaries Generator module performs backbone detection and line detection to generate lane boundaries on the located area. After identifying the lane boundaries, the Departure Warning module can quickly determine whether the vehicle deviates from the lane. Additionally, we implemented and deployed our framework to an Android platform and tested its running speed. ©2021 IEEE.

AB - Lane departure warning is an essential function of driver assistance systems. In this paper, we propose a vision-based lane departure warning framework, which can determine whether the vehicle deviates from the lane only with images as input. Our framework combines the deep learning method and traditional line detection algorithm. Specifically, the framework consists of a Lane Boundaries Localization module, a Lane Boundaries Generator module, and a Departure Warning module. The Lane Boundaries Localization module is a deep neural network for finding the area containing lane Boundaries. Then the Lane Boundaries Generator module performs backbone detection and line detection to generate lane boundaries on the located area. After identifying the lane boundaries, the Departure Warning module can quickly determine whether the vehicle deviates from the lane. Additionally, we implemented and deployed our framework to an Android platform and tested its running speed. ©2021 IEEE.

KW - lane departure warning

KW - lane detection

KW - deep learning

KW - Hough transform

KW - LINES

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BT - Proceedings - 2021 IEEE International Conference on e-Business Engineering, ICEBE 2021

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Y2 - 12 November 2021 through 14 November 2021

ER -

A Vision-based Lane Departure Warning Framework

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