Extending the Detection Range for Low-Channel Roadside LiDAR by Static Background Construction

Liu, Hui; Lin, Ciyun; Gong, Bowen; Wu, Dayong

doi:10.1109/tgrs.2022.3155634

Cited by 16 publications

(10 citation statements)

References 39 publications

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“…The background filtering method based on point cloud mapping aims to encode the temporal point cloud according to the azimuth angle and the ID of the laser beam, as shown in Figure 4 a. The background modeling and filtering are performed on the point cloud at the same beam and azimuth [ 39 , 40 , 41 , 42 , 43 , 44 ]. Zhao et al [ 40 ] proposed an azimuth–height background filtering method.…”

Section: Object Detection Based On Roadside Lidarmentioning

confidence: 99%

“…When the roadside LiDAR is deployed in crowded traffic scenes, object point clouds such as vehicles will be misjudged as background points, thus affecting the accuracy of the background model. Liu et al [ 44 ] optimized the construction method of the background point cloud based on the maximum distance. By assuming that the vehicles and another object point only appear in the peer area, they introduced the filtering of the passing region to eliminate the object points introduced in the background point cloud.…”

Section: Object Detection Based On Roadside Lidarmentioning

confidence: 99%

“…(2) Object point cloud segmentation and recognition: After filtering the background point cloud, it is necessary to further identify vehicles, pedestrians, and other objects from the filtered foreground point cloud. Firstly, a three-dimensional object point cloud clustering algorithm, such as the point cloud clustering method based on Euler distance [ 55 , 56 , 57 , 58 ], point density, and its variants [ 36 , 43 , 44 , 59 , 60 , 61 , 62 ], is used to accurately segment the foreground object point cloud into independent objects. Then, according to the prior knowledge of the object, several handcrafted features, such as the standard deviation and clustering dimension of the cluster point cloud, are extracted from the cluster.…”

Section: Object Detection Based On Roadside Lidarmentioning

confidence: 99%

“…Since the shape of point cloud objects is mostly irregular, the clustering method based on point cloud density can detect objects with arbitrary shapes, so it is also applied to the segmentation of roadside laser radar object point clouds. Among them, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method is the most widely used [ 43 , 44 , 59 , 60 , 61 , 62 ].…”

Section: Object Detection Based On Roadside Lidarmentioning

confidence: 99%

See 3 more Smart Citations

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

Sun

Wang

et al. 2022

Sensors

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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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Section: Object Detection Based On Roadside Lidarmentioning

confidence: 99%

Section: Object Detection Based On Roadside Lidarmentioning

confidence: 99%

Section: Object Detection Based On Roadside Lidarmentioning

confidence: 99%

Section: Object Detection Based On Roadside Lidarmentioning

confidence: 99%

See 2 more Smart Citations

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

Sun

Wang

et al. 2022

Sensors

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“…13 A density-based spatial clustering with noise (DBSCAN) algorithm is used to identify the vehicles in close vicinity and far range by utilizing Fast Fourier Transform (FFT) after tracking the object trajectory based on their distance and direction. 14 Simultaneous localization and mapping (SLAM) based on camera and LiDAR has been studied with both camera and LiDAR datasets. 15 Integrating the depth-based loss function can be used to increase the accuracy of depth estimation for distant objects and the bias between the multiple classes of objects can be balanced by separating the loss function into the foreground and background portions.…”

Section: Introductionmentioning

confidence: 99%

Object recognition from on-the-road traffic data

Muhammad

Farooq²,

Zuguo³

et al. 2022

5th International Conference on Informatics Engineering and Information Science (ICIEIS 2022)

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In order to detect the object and inspect the road conditions in real-time, the 2-dimensional (2D) and 3dimensional (3D) data obtained from the onboard sensors, LiDAR and digital cameras are analyzed for object recognition to assist driving. Due to the uncertainties of the dynamic objects, such as pedestrians, animals or vibrated vehicles, extraction of complete and clear objects from LiDARs datasets requires complex post-processing since LiDAR data can be used for scanning at long distances, i.e., 300m, which can alarm the driver timely to take necessary actions. The dynamic and static objects from the LiDARs point clouds can be detected with the teacher-student framework algorithm along with the KITTI dataset. Furthermore, a semi-supervised theory is utilized to improve detection performance.

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Heterogeneous Traffic Flow Detection Using CAV-Based Sensor With I-GAIN

Gong

Lin

et al. 2023

IEEE Access

Self Cite

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This study proposes using connected automatic vehicles (CAVs) as traffic flow detectors to collect and exchange traffic flow data for heterogeneous traffic management and control. The proposed method includes the construction of a mathematical matrix to represent the status of the road section, the use of unsupervised machine learning to evaluate traffic data, and an improved generative adversarial imputation net (GAIN) to evaluate and impute missing traffic data. Next-generation simulation (NGSIM) data are used to verify the accuracy and robustness of the proposed method. One of the primary innovations of this study is the use of GAIN, a deep learning framework based on generative adversarial networks (GANs), to impute missing traffic data. GAIN has been shown to be more robust and stable when handling incomplete heterogeneous data than existing imputation methods. Additionally, this study contributes to the field by proposing the use of CAVs as sensors to detect mixed traffic flow, which could lead to more efficient and accurate traffic management and control. Experimental results demonstrate that the proposed method outperforms existing imputation methods, with a normalized root mean squared error and symmetric mean absolute percentage error of less than 0.2/0.3 and 0.08/0.13 in I-80 and Lankershim Boulevard, respectively. The findings of this study have important implications for the development and implementation of connected and automated vehicle technologies in the field of transportation.INDEX TERMS CAV-based sensor, heterogeneous traffic flow, mixed traffic flow, improved generative adversarial imputation net, NGSIM

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Extending the Detection Range for Low-Channel Roadside LiDAR by Static Background Construction

Cited by 16 publications

References 39 publications

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

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

Object recognition from on-the-road traffic data

Heterogeneous Traffic Flow Detection Using CAV-Based Sensor With I-GAIN

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