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Survey

Lane Detection: A Survey with New Results

Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing 100084, China
College of Information Sciences and Technology, Pennsylvania State University, University Park, PA 16802, U.S.A.
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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.

Keywords

convolutional neural network (CNN)datasetdeep learninghigh-definition (HD) maplane detection

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Journal of Computer Science and Technology
Volume 35 Issue 3,
May 2020
Pages 493-505
DOI: 10.1007/s11390-020-0476-4
Cite this article:
Liang D, Guo Y-C, Zhang S-K, et al. Lane Detection: A Survey with New Results. Journal of Computer Science and Technology, 2020, 35(3): 493-505. https://doi.org/10.1007/s11390-020-0476-4

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Received: 28 March 2020
Revised: 17 April 2020
Published: 29 May 2020
©Institute of Computing Technology, Chinese Academy of Sciences 2020
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