Adaptive Point Cloud Clustering Algorithm for Practical Roadside MmWave Radar Systems

Millimeter-Wave radar has been widely applied in the field of autonomous driving due to an excellent performance under complex weather conditions. However, in practical roadside scenarios, the challenge of sparse point clouds leading to clustering difficulties and the issue of large vehicle point cl...

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Bibliographic Details
Published in:IEEE Vehicular Technology Conference pp. 1 - 5
Main Authors: Zhang, Luyi, Zhang, Jinhang, Shi, Haixin, Gao, Lu, Hu, Xiaopeng, Chen, Rui
Format: Conference Proceeding
Language:English
Published: IEEE 24.06.2024
Subjects:
Accuracy
Adaptive systems
Azimuth
clustering
Clustering algorithms
DBSCAN
Logic gates
Millimeter wave measurements
Millimeter-Wave radar
MmWave
point cloud
Point cloud compression
roadside
wave gate
ISSN:2577-2465
Online Access:Get full text
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Summary:Millimeter-Wave radar has been widely applied in the field of autonomous driving due to an excellent performance under complex weather conditions. However, in practical roadside scenarios, the challenge of sparse point clouds leading to clustering difficulties and the issue of large vehicle point clouds dispersing, resulting in fragmentation, currently hampers the practical ap-plication of radar sensors. We propose an adaptive point cloud clustering algorithm based on DBSCAN. First, we propose an improved DBSCAN clustering algorithm based on distance and speed thresholds, which enhances the differentiation of point clouds between different vehicles, and an adaptive ellipse gate strategy to solve the large vehicle point clouds fragmentation problem. Then, a secondary clustering algorithm based on azimuth is exploited, effectively addressing the issues of large vehicle fragmentation and anomalous speed values. Practical roadside experimental results demonstrate that our proposed algorithm significantly outperforms traditional algorithms, showing considerable potential in practical applications.
ISSN:2577-2465
DOI:10.1109/VTC2024-Spring62846.2024.10683343