Developing a Method to Automatically Extract Road Boundary and Linear Road Markings from a Mobile Mapping System Point Cloud Using Oriented Bounding Box Collision-Detection Techniques

Advancements in data-acquisition technology have led to the increasing demand for high-precision road data for autonomous driving. Specifically, road boundaries and linear road markings, like edge and lane markings, provide fundamental guidance for various applications. Unfortunately, their extracti...

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Published in:	Remote sensing (Basel, Switzerland) Vol. 15; no. 19; p. 4656
Main Authors:	Kang, Seokchan, Lee, Jeongwon, Lee, Jiyeong
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.10.2023
Subjects:	Algorithms automatic extraction Boundaries Data acquisition data collection Deep learning Electronic data processing extracts ground MMS survey Horizontal orientation Image processing Methods OBB collision-detection algorithm point cloud data remote sensing road boundary road marking Roads Roads & highways Sensors South Korea Streets South Korea
ISSN:	2072-4292, 2072-4292
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Abstract	Advancements in data-acquisition technology have led to the increasing demand for high-precision road data for autonomous driving. Specifically, road boundaries and linear road markings, like edge and lane markings, provide fundamental guidance for various applications. Unfortunately, their extraction usually requires labor-intensive manual work, and the automatic extraction, which can be applied universally for diverse curved road types, presents a challenge. Given this context, this study proposes a method to automatically extract road boundaries and linear road markings by applying an oriented bounding box (OBB) collision-detection algorithm. The OBBs are generated from a reference line using the point cloud data’s position and intensity values. By applying the OBB collision-detection algorithm, road boundaries and linear road markings can be extracted efficiently and accurately in straight and curved roads by adjusting search length and width to detect OBB collision. This study assesses horizontal position accuracy using automatically extracted and manually digitized data to verify this method. The resulting RMSE for extracted road boundaries is +4.8 cm and +5.3 cm for linear road markings, indicating that high-accuracy road boundary and road marking extraction was possible. Therefore, our results demonstrate that the automatic extraction adjusting OBB detection parameters and integrating the OBB collision-detection algorithm enables efficient and precise extraction of road boundaries and linear road markings in various curving types of roads. Finally, this enhances its practicality and simplifies the implementation of the extraction process.
AbstractList	Advancements in data-acquisition technology have led to the increasing demand for high-precision road data for autonomous driving. Specifically, road boundaries and linear road markings, like edge and lane markings, provide fundamental guidance for various applications. Unfortunately, their extraction usually requires labor-intensive manual work, and the automatic extraction, which can be applied universally for diverse curved road types, presents a challenge. Given this context, this study proposes a method to automatically extract road boundaries and linear road markings by applying an oriented bounding box (OBB) collision-detection algorithm. The OBBs are generated from a reference line using the point cloud data’s position and intensity values. By applying the OBB collision-detection algorithm, road boundaries and linear road markings can be extracted efficiently and accurately in straight and curved roads by adjusting search length and width to detect OBB collision. This study assesses horizontal position accuracy using automatically extracted and manually digitized data to verify this method. The resulting RMSE for extracted road boundaries is +4.8 cm and +5.3 cm for linear road markings, indicating that high-accuracy road boundary and road marking extraction was possible. Therefore, our results demonstrate that the automatic extraction adjusting OBB detection parameters and integrating the OBB collision-detection algorithm enables efficient and precise extraction of road boundaries and linear road markings in various curving types of roads. Finally, this enhances its practicality and simplifies the implementation of the extraction process.
Audience	Academic
Author	Lee, Jeongwon Lee, Jiyeong Kang, Seokchan
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Cites_doi	10.1080/14498596.2021.1960912 10.3390/rs14122768 10.1016/j.aei.2019.100936 10.1109/CISP-BMEI.2018.8633181 10.1109/TITS.2020.3028033 10.1109/ACCESS.2019.2958671 10.1109/ACCESS.2020.2985413 10.3390/ijgi9100608 10.1145/1653771.1653851 10.1088/1742-6596/1213/4/042079 10.1109/JSTARS.2019.2904514 10.3390/rs12091379 10.1109/DICTA.2015.7371262 10.1109/TPAMI.2020.3005434 10.3390/rs14143279 10.1177/0361198120981948 10.1016/j.isprsjprs.2014.12.027 10.3390/s19163466 10.5194/isprs-archives-XLVIII-1-W1-2023-93-2023 10.1109/TITS.2020.2990120 10.1016/j.neucom.2017.09.098 10.21307/ijanmc-2021-003 10.1088/1742-6596/1453/1/012141 10.1088/1361-6501/aa76a3 10.3390/rs8090710 10.1145/358669.358692 10.1080/22797254.2018.1535837 10.3390/rs13132612 10.3390/rs8060501 10.1109/JSTARS.2019.2893967 10.1109/JSEN.2021.3057999 10.1016/j.isprsjprs.2018.10.007 10.20965/ijat.2017.p0657
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SubjectTerms	Algorithms automatic extraction Boundaries Data acquisition data collection Deep learning Electronic data processing extracts ground MMS survey Horizontal orientation Image processing Methods OBB collision-detection algorithm point cloud data remote sensing road boundary road marking Roads Roads & highways Sensors South Korea Streets
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Title	Developing a Method to Automatically Extract Road Boundary and Linear Road Markings from a Mobile Mapping System Point Cloud Using Oriented Bounding Box Collision-Detection Techniques
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