Citation

Chen W, Wang W, Wang K, Li Z, Li H, Liu S. J. Traffic Transp. Eng. Engl. Ed. 2020; 7(6): 748-774.

Copyright

(Copyright © 2020, Periodical Offices of Chang'an University, Publisher Elsevier Publishing)

DOI

10.1016/j.jtte.2020.10.002

PMID

unavailable

Abstract

Recently, the development and application of lane line departure warning systems have been in the market. For any of the systems, the key part of lane line tracking, lane line identification, or lane line departure warning is whether it can accurately and quickly detect lane lines. Since 1990s, they have been studied and implemented for the situations defined by the good viewing conditions and the clear lane markings on road. After then, the accuracy for particular situations, the robustness for a wide range of scenarios, time efficiency and integration into higher-order tasks define visual lane line detection and tracking as a continuing research subject. At present, these kinds of lane marking line detection methods based on machine vision and image processing can be divided into two categories: the traditional image processing and semantic segmentation (includes deep learning) methods. The former mainly involves feature-based and model-based steps, and which can be classified into similarity- and discontinuity-based ones; and the model-based step includes different parametric straight line, curve or pattern models. The semantic segmentation includes different machine learning, neural network and deep learning methods, which is the new trend for the research and application of lane line departure warning systems. This paper describes and analyzes the lane line departure warning systems, image processing algorithms and semantic segmentation methods for lane line detection.

Language: en

Keywords

Image analysis; Image processing; Lane departure warning; Lane line detection; Semantic segmentation; Traffic engineering