NDLI: Lane detection method based on lane structural analysis and CNNs

Content Provider	IET Digital Library
Author	Ye, Yang Yang Hao, Xiao Li Chen, Hou Jin
Abstract	Advanced driving assistance systems (ADASs) play a vital role in the safety of transportation. The detection of lane markings is a very important part of ADASs. For the safety of autonomous vehicles and vehicles driven by human drivers, accurate detection results are necessary. In this study, the authors propose a novel algorithm based on lane structural analysis and convolutional neural networks (CNNs) for lane marking detection. First, a pre-processing stage is used to remove the pavement that constitutes the background of the lane markings. Next, a set of local waveforms from local images is used to generate a region of interest and a CNN classifier is employed to detect lane marking candidates. Finally, a lane geometry analysis stage determines whether or not the candidate is a part of a lane marking. The major contributions of this study can be summarised as follows. First, they propose a novel method to describe a road using waveforms. Second, they analyse the local and global characteristics of the road geometry to detect the lane markings. Third, they provide an effective method to obtain training data for the proposed machine learning method. Experimental results demonstrate that the proposed method outperforms conventional methods.
Starting Page	513
Ending Page	520
Page Count	8
ISSN	1751956X
Volume Number	12
e-ISSN	17519578
Issue Number	Issue 6, Aug (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/12/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2017.0143
Journal	IET Intelligent Transport Systems
Publisher Date	2018-02-06
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	ADAS Advanced Driving Assistance System Autonomous Vehicle CNN Detect Lane Marking Driver Information System Human Driver Knowledge Engineering Technique Lane Detection Method Lane Marking Detection Lane Structural Analysis Learning in AI Local Image Machine Learning Method Neural Computing Technique Neural Nets Road Safety Traffic Engineering Computing Training Data Transportation
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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