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基于路侧激光雷达的协同驾驶自动化目标检测:综述。

Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review.

机构信息

School of Information Engineering, Chang'an University, Xi'an 710064, China.

出版信息

Sensors (Basel). 2022 Nov 30;22(23):9316. doi: 10.3390/s22239316.

Abstract

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.

摘要

激光雷达(LiDAR)技术具有检测精度高、感知范围广、不受光照影响等优点。3D LiDAR 放置在交通场景的制高点,可以从俯瞰的角度全局把握情况,并实时准确感知交通场景中每个物体的轨迹,然后通过先进的无线通信设备将物体信息分发给周围的车辆或其他路边 LiDAR,这可以显著提高自动驾驶汽车的局部感知能力。本文首先描述了路边 LiDAR 的特点和目标检测所面临的挑战,然后详细回顾了基于单路边 LiDAR 和多 LiDAR 协同的目标检测的当前方法。然后,介绍了一些针对恶劣天气下的路边 LiDAR 感知和近年来发布的数据集的研究。最后,讨论了目前路边 LiDAR 感知的一些开放性挑战和未来的工作。据我们所知,这是第一篇系统研究路边 LiDAR 感知方法和数据集的论文,它对进一步推动路边 LiDAR 感知的研究具有重要的指导作用,以实现实际应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8c7/9738246/c3d166bb09e9/sensors-22-09316-g001.jpg

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