@article{Liang2020, 
author = {Dun Liang and Yuan-Chen Guo and Shao-Kui Zhang and Tai-Jiang Mu and Xiaolei Huang},
title = {Lane Detection: A Survey with New Results},
year = {2020},
journal = {Journal of Computer Science and Technology},
volume = {35},
number = {3},
pages = {493-505},
keywords = {convolutional neural network (CNN), dataset, deep learning, high-definition (HD) map, lane detection},
url = {https://www.sciopen.com/article/10.1007/s11390-020-0476-4},
doi = {10.1007/s11390-020-0476-4},
abstract = {Lane detection is essential for many aspects of autonomous driving, such as lane-based navigation and high-definition (HD) map modeling. Although lane detection is challenging especially with complex road conditions, considerable progress has been witnessed in this area in the past several years. In this survey, we review recent visual-based lane detection datasets and methods. For datasets, we categorize them by annotations, provide detailed descriptions for each category, and show comparisons among them. For methods, we focus on methods based on deep learning and organize them in terms of their detection targets. Moreover, we introduce a new dataset with more detailed annotations for HD map modeling, a new direction for lane detection that is applicable to autonomous driving in complex road conditions, a deep neural network LineNet for lane detection, and show its application to HD map modeling.}
}