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Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review.
Sun, Pengpeng; Sun, Chenghao; Wang, Runmin; Zhao, Xiangmo.
Afiliação
  • Sun P; School of Information Engineering, Chang'an University, Xi'an 710064, China.
  • Sun C; School of Information Engineering, Chang'an University, Xi'an 710064, China.
  • Wang R; School of Information Engineering, Chang'an University, Xi'an 710064, China.
  • Zhao X; School of Information Engineering, Chang'an University, Xi'an 710064, China.
Sensors (Basel) ; 22(23)2022 Nov 30.
Article em En | MEDLINE | ID: mdl-36502018
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Condução de Veículo Tipo de estudo: Diagnostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Condução de Veículo Tipo de estudo: Diagnostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China