Chen, Hongmei and Wang, Haifeng and Liu, Zilong and Gu, Dongbing and Ye, Wen (2024) HP3D-V2V: High-Precision 3D Object Detection Vehicle-to-Vehicle Cooperative Perception Algorithm. Sensors, 24 (7). p. 2170. DOI https://doi.org/10.3390/s24072170
Chen, Hongmei and Wang, Haifeng and Liu, Zilong and Gu, Dongbing and Ye, Wen (2024) HP3D-V2V: High-Precision 3D Object Detection Vehicle-to-Vehicle Cooperative Perception Algorithm. Sensors, 24 (7). p. 2170. DOI https://doi.org/10.3390/s24072170
Chen, Hongmei and Wang, Haifeng and Liu, Zilong and Gu, Dongbing and Ye, Wen (2024) HP3D-V2V: High-Precision 3D Object Detection Vehicle-to-Vehicle Cooperative Perception Algorithm. Sensors, 24 (7). p. 2170. DOI https://doi.org/10.3390/s24072170
Abstract
Cooperative perception in the field of connected autonomous vehicles (CAVs) aims to overcome the inherent limitations of single-vehicle perception systems, including long-range occlusion, low resolution, and susceptibility to weather interference. In this regard, we propose a high-precision 3D object detection V2V cooperative perception algorithm. The algorithm utilizes a voxel grid-based statistical filter to effectively denoise point cloud data to obtain clean and reliable data. In addition, we design a feature extraction network based on the fusion of voxels and PointPillars and encode it to generate BEV features, which solves the spatial feature interaction problem lacking in the PointPillars approach and enhances the semantic information of the extracted features. A maximum pooling technique is used to reduce the dimensionality and generate pseudoimages, thereby skipping complex 3D convolutional computation. To facilitate effective feature fusion, we design a feature level-based crossvehicle feature fusion module. Experimental validation is conducted using the OPV2V dataset to assess vehicle coperception performance and compare it with existing mainstream coperception algorithms. Ablation experiments are also carried out to confirm the contributions of this approach. Experimental results show that our architecture achieves lightweighting with a higher average precision (AP) than other existing models.
Item Type: | Article |
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Uncontrolled Keywords: | cooperative perception; 3D object detection; feature extraction; crossvehicle feature fusion |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 08 Apr 2024 12:23 |
Last Modified: | 30 Oct 2024 19:21 |
URI: | http://repository.essex.ac.uk/id/eprint/38160 |
Available files
Filename: sensors-24-02170.pdf
Licence: Creative Commons: Attribution 4.0