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

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

Pengpeng Sun  1 , Chenghao Sun  1 , Runmin Wang  1 , Xiangmo Zhao  1
Affiliations
Review

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

Pengpeng Sun et al. Sensors (Basel). .

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.

Keywords: a review; cooperative perception; object detection; roadside LiDAR.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Roadside LiDAR and output point cloud. (a) 32L-LiDAR-R; (b) point clouds from a single LiDAR output; (c) point clouds from multiple LiDAR outputs.
Figure 2
Figure 2
Point cloud of the typical LiDAR.
Figure 3
Figure 3
Technical route based on a single roadside LiDAR.
Figure 4
Figure 4
Representation of LiDAR point clouds. (a) Point cloud mapping. (b) Voxel-based [45].
Figure 5
Figure 5
PointPillars 3D object detection model architecture [79].
Figure 6
Figure 6
Logical illustration of raw-level and object-level schemes for cooperative object detection.
Figure 7
Figure 7
The system architecture of a feature-based cooperative perception framework [90].
Figure 8
Figure 8
LiDAR operating in rain and snow and its output point cloud. (a) Under rain. (b) Under snow. (c) Point cloud under snow.
Figure 9
Figure 9
Dataset collected by roadside LiDAR and cameras.
See this image and copyright information in PMC

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