Scipediacontent: Scipediacontent moved page Draft Content 657687389 to Wang et al 2018e

2021-02-12T12:56:04Z

Scipediacontent moved page Draft Content 657687389 to Wang et al 2018e

Scipediacontent: Created page with " == Abstract == Lane marking detection and localization are crucial for autonomous driving and lane-based pavement surveys. Numerous studies have been done to detect and loc..."

2021-02-12T12:56:00Z

Created page with " == Abstract == Lane marking detection and localization are crucial for autonomous driving and lane-based pavement surveys. Numerous studies have been done to detect and loc..."

New page

== Abstract ==

Lane marking detection and localization are crucial for autonomous driving and lane-based pavement surveys. Numerous studies have been done to detect and locate lane markings with the purpose of advanced driver assistance systems, in which image data are usually captured by vision-based cameras. However, a limited number of studies have been done to identify lane markings using high-resolution laser images for road condition evaluation. In this study, the laser images are acquired with a digital highway data vehicle (DHDV). Subsequently, a novel methodology is presented for the automated lane marking identification and reconstruction, and is implemented in four phases: (1) binarization of the laser images with a new threshold method (multi-box segmentation based threshold method); (2) determination of candidate lane markings with closing operations and a marching square algorithm; (3) identification of true lane marking by eliminating false positives (FPs) using a linear support vector machine method; and (4) reconstruction of the damaged and dash lane marking segments to form a continuous lane marking based on the geometry features such as adjacent lane marking location and lane width. Finally, a case study is given to validate effects of the novel methodology. The findings indicate the new strategy is robust in image binarization and lane marking localization. This study would be beneficial in road lane-based pavement condition evaluation such as lane-based rutting measurement and crack classification.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_657687389-beopen245-8909-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://dx.doi.org/10.3390/s18051635 http://dx.doi.org/10.3390/s18051635] under the license https://creativecommons.org/licenses/by

* [http://europepmc.org/articles/PMC5982162 http://europepmc.org/articles/PMC5982162] under the license https://creativecommons.org/licenses/by/4.0

* [https://dx.doi.org/10.3390/s18051635 https://dx.doi.org/10.3390/s18051635]

* [https://www.mdpi.com/1424-8220/18/5/1635/pdf https://www.mdpi.com/1424-8220/18/5/1635/pdf]

* [http://www.mdpi.com/1424-8220/18/5/1635/pdf http://www.mdpi.com/1424-8220/18/5/1635/pdf],
: [http://dx.doi.org/10.3390/s18051635 http://dx.doi.org/10.3390/s18051635] under the license cc-by

* [https://dblp.uni-trier.de/db/journals/sensors/sensors18.html#LiLW18 https://dblp.uni-trier.de/db/journals/sensors/sensors18.html#LiLW18],
: [https://pubmed.ncbi.nlm.nih.gov/29783789 https://pubmed.ncbi.nlm.nih.gov/29783789],
: [https://www.ncbi.nlm.nih.gov/pubmed/29783789 https://www.ncbi.nlm.nih.gov/pubmed/29783789],
: [https://doi.org/10.3390/s18051635 https://doi.org/10.3390/s18051635],
: [https://academic.microsoft.com/#/detail/2804614876 https://academic.microsoft.com/#/detail/2804614876] under the license https://creativecommons.org/licenses/by/4.0/

← Older revision	Revision as of 12:56, 12 February 2021
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Wang et al 2018e - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 657687389 to Wang et al 2018e

Scipediacontent: Created page with " == Abstract == Lane marking detection and localization are crucial for autonomous driving and lane-based pavement surveys. Numerous studies have been done to detect and loc..."