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Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review
Light Detection and Ranging (LiDAR) technology has the advantages of high detection accuracy, a wide range of perception, and not being affected by light. The 3D LiDAR is placed at the commanding height of the traffic scene, the overall situation can be grasped from the perspective of top view, and...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9738246/ https://www.ncbi.nlm.nih.gov/pubmed/36502018 http://dx.doi.org/10.3390/s22239316 |
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author | Sun, Pengpeng Sun, Chenghao Wang, Runmin Zhao, Xiangmo |
author_facet | Sun, Pengpeng Sun, Chenghao Wang, Runmin Zhao, Xiangmo |
author_sort | Sun, Pengpeng |
collection | PubMed |
description | Light Detection and Ranging (LiDAR) technology has the advantages of high detection accuracy, a wide range of perception, and not being affected by light. The 3D LiDAR is placed at the commanding height of the traffic scene, the overall situation can be grasped from the perspective of top view, and the trajectory of each object in the traffic scene can be accurately perceived in real time, and then the object information can be distributed to the surrounding vehicles or other roadside LiDAR through advanced wireless communication equipment, which can significantly improve the local perception ability of an autonomous vehicle. This paper first describes the characteristics of roadside LiDAR and the challenges of object detection and then reviews in detail the current methods of object detection based on a single roadside LiDAR and multi-LiDAR cooperatives. Then, some studies for roadside LiDAR perception in adverse weather and datasets released in recent years are introduced. Finally, some current open challenges and future works for roadside LiDAR perception are discussed. To the best of our knowledge, this is the first work to systematically study roadside LiDAR perception methods and datasets. It has an important guiding role in further promoting the research of roadside LiDAR perception for practical applications. |
format | Online Article Text |
id | pubmed-9738246 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97382462022-12-11 Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review Sun, Pengpeng Sun, Chenghao Wang, Runmin Zhao, Xiangmo Sensors (Basel) Review Light Detection and Ranging (LiDAR) technology has the advantages of high detection accuracy, a wide range of perception, and not being affected by light. The 3D LiDAR is placed at the commanding height of the traffic scene, the overall situation can be grasped from the perspective of top view, and the trajectory of each object in the traffic scene can be accurately perceived in real time, and then the object information can be distributed to the surrounding vehicles or other roadside LiDAR through advanced wireless communication equipment, which can significantly improve the local perception ability of an autonomous vehicle. This paper first describes the characteristics of roadside LiDAR and the challenges of object detection and then reviews in detail the current methods of object detection based on a single roadside LiDAR and multi-LiDAR cooperatives. Then, some studies for roadside LiDAR perception in adverse weather and datasets released in recent years are introduced. Finally, some current open challenges and future works for roadside LiDAR perception are discussed. To the best of our knowledge, this is the first work to systematically study roadside LiDAR perception methods and datasets. It has an important guiding role in further promoting the research of roadside LiDAR perception for practical applications. MDPI 2022-11-30 /pmc/articles/PMC9738246/ /pubmed/36502018 http://dx.doi.org/10.3390/s22239316 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Sun, Pengpeng Sun, Chenghao Wang, Runmin Zhao, Xiangmo Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review |
title | Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review |
title_full | Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review |
title_fullStr | Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review |
title_full_unstemmed | Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review |
title_short | Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review |
title_sort | object detection based on roadside lidar for cooperative driving automation: a review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9738246/ https://www.ncbi.nlm.nih.gov/pubmed/36502018 http://dx.doi.org/10.3390/s22239316 |
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