C2L3-Fusion: An Integrated 3D Object Detection Method for Autonomous Vehicles
Article
Article Title | C2L3-Fusion: An Integrated 3D Object Detection Method for Autonomous Vehicles |
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ERA Journal ID | 34304 |
Article Category | Article |
Authors | Ngo, Thanh Binh, Ngo, Long, Phi, Anh Vu, Nguyen, Trung Thị Hoa Trang, Nguyen, Andy, Brown, Jason and Perera, Asanka |
Journal Title | Sensors |
Journal Citation | 25 (9) |
Article Number | 2688 |
Number of Pages | 16 |
Year | 2025 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 1424-8220 |
1424-8239 | |
Web Address (URL) | https://www.mdpi.com/1424-8220/25/9/2688 |
Abstract | Accurate 3D object detection is crucial for autonomous vehicles (AVs) to navigate safely in complex environments. This paper introduces a novel fusion framework that integrates Camera image-based 2D object detection using YOLOv8 and LiDAR data-based 3D object detection using PointPillars, hence named C2L3-Fusion. Unlike conventional fusion approaches, which often struggle with feature misalignment, C2L3-Fusion enhances spatial consistency and multi-level feature aggregation, significantly improving detection accuracy. Our method outperforms state-of-the-art approaches such as YoPi-CLOCs Fusion Network, standalone YOLOv8, and standalone PointPillars, achieving mean Average Precision (mAP) scores of 89.91% (easy), 79.26% (moderate), and 78.01% (hard) on the KITTI dataset. Successfully implemented on the Nvidia Jetson AGX Xavier embedded platform, C2L3-Fusion maintains real-time performance while enhancing robustness, making it highly suitable for self-driving vehicles. This paper details the methodology, mathematical formulations, algorithmic advancements, and real-world testing of C2L3-Fusion, offering a comprehensive solution for 3D object detection in autonomous navigation. |
Keywords | deep learning; AI; autonomous vehicle; 3D detection; 2D detection; C2L3-Fusion |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 4007. Control engineering, mechatronics and robotics |
Byline Affiliations | University of Transport and Communications, Vietnam |
Mobifone Digital Services, Vietnam | |
Michigan State University, United States | |
School of Engineering |
https://research.usq.edu.au/item/zy340/c2l3-fusion-an-integrated-3d-object-detection-method-for-autonomous-vehicles
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