Long-Range LiDAR Vehicle Detection Through Clustering and Classification for Autonomous Racing
With the expansion of autonomous driving technology, autonomous racing has been actively studied in recent years. For safe autonomous racing, fast computation speed and wide detection range are essential. However, existing methods can perform detection only in short range due to the sparsity of LiDA...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10883986/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849720254776737792 |
|---|---|
| author | Na-Young Lim Tae-Hyoung Park |
| author_facet | Na-Young Lim Tae-Hyoung Park |
| author_sort | Na-Young Lim |
| collection | DOAJ |
| description | With the expansion of autonomous driving technology, autonomous racing has been actively studied in recent years. For safe autonomous racing, fast computation speed and wide detection range are essential. However, existing methods can perform detection only in short range due to the sparsity of LiDAR data. While some methods have been proposed to address this limitation, they often neglect real-time processing, making them unsuitable for racing environments. To address this, we propose a novel clustering and classification-based method specifically designed to enhance long-range vehicle detection while maintaining computational efficiency. First, our method employs a lightweight road segmentation and ground removal module to eliminate irrelevant data, significantly reducing computational overhead without down-sampling. Second, we introduce a 2D Bird’s Eye View (BEV)-based clustering approach, which is faster and more robust than traditional 3D Euclidean clustering, to generate object candidates efficiently. Additionally, a machine learning classifier trained on long-range vehicle features is incorporated to enhance detection accuracy at extended distances. Finally, range-view-based verification is performed to refine and finalize detection results, ensuring high reliability. The proposed method is evaluated on the aiMotive dataset, long-range dataset. Experimental results demonstrate that our approach achieves over 80% detection accuracy at distances exceeding 50 meters, with a computation time of only 25 ms per frame. These results underscore the novelty and effectiveness of the proposed method as an optimized solution for long-range vehicle detection in real-time autonomous racing environments. |
| format | Article |
| id | doaj-art-470d9c6bd4504e718af34f2a21dea8a1 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-470d9c6bd4504e718af34f2a21dea8a12025-08-20T03:11:58ZengIEEEIEEE Access2169-35362025-01-0113304063041910.1109/ACCESS.2025.354126710883986Long-Range LiDAR Vehicle Detection Through Clustering and Classification for Autonomous RacingNa-Young Lim0https://orcid.org/0009-0003-6330-5207Tae-Hyoung Park1https://orcid.org/0000-0002-3695-344XDepartment of Intelligent Systems and Robotics, Chungbuk National University, Cheongju-si, South KoreaDepartment of Intelligent Systems and Robotics, Chungbuk National University, Cheongju-si, South KoreaWith the expansion of autonomous driving technology, autonomous racing has been actively studied in recent years. For safe autonomous racing, fast computation speed and wide detection range are essential. However, existing methods can perform detection only in short range due to the sparsity of LiDAR data. While some methods have been proposed to address this limitation, they often neglect real-time processing, making them unsuitable for racing environments. To address this, we propose a novel clustering and classification-based method specifically designed to enhance long-range vehicle detection while maintaining computational efficiency. First, our method employs a lightweight road segmentation and ground removal module to eliminate irrelevant data, significantly reducing computational overhead without down-sampling. Second, we introduce a 2D Bird’s Eye View (BEV)-based clustering approach, which is faster and more robust than traditional 3D Euclidean clustering, to generate object candidates efficiently. Additionally, a machine learning classifier trained on long-range vehicle features is incorporated to enhance detection accuracy at extended distances. Finally, range-view-based verification is performed to refine and finalize detection results, ensuring high reliability. The proposed method is evaluated on the aiMotive dataset, long-range dataset. Experimental results demonstrate that our approach achieves over 80% detection accuracy at distances exceeding 50 meters, with a computation time of only 25 ms per frame. These results underscore the novelty and effectiveness of the proposed method as an optimized solution for long-range vehicle detection in real-time autonomous racing environments.https://ieeexplore.ieee.org/document/10883986/Autonomous racingLiDAR long-rangevehicle detectionreal-time |
| spellingShingle | Na-Young Lim Tae-Hyoung Park Long-Range LiDAR Vehicle Detection Through Clustering and Classification for Autonomous Racing IEEE Access Autonomous racing LiDAR long-range vehicle detection real-time |
| title | Long-Range LiDAR Vehicle Detection Through Clustering and Classification for Autonomous Racing |
| title_full | Long-Range LiDAR Vehicle Detection Through Clustering and Classification for Autonomous Racing |
| title_fullStr | Long-Range LiDAR Vehicle Detection Through Clustering and Classification for Autonomous Racing |
| title_full_unstemmed | Long-Range LiDAR Vehicle Detection Through Clustering and Classification for Autonomous Racing |
| title_short | Long-Range LiDAR Vehicle Detection Through Clustering and Classification for Autonomous Racing |
| title_sort | long range lidar vehicle detection through clustering and classification for autonomous racing |
| topic | Autonomous racing LiDAR long-range vehicle detection real-time |
| url | https://ieeexplore.ieee.org/document/10883986/ |
| work_keys_str_mv | AT nayounglim longrangelidarvehicledetectionthroughclusteringandclassificationforautonomousracing AT taehyoungpark longrangelidarvehicledetectionthroughclusteringandclassificationforautonomousracing |