Affiliation:
1. Faculty of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China
2. School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
3. Division of Mechanics and Acoustics, National Institute of Metrology, Beijing 102200, China
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.
Funder
the National Key R&D Program of China
the National Natural Science Foundation of China
the UK Engineering and Physical Sciences Research Council
the Basic Research of National Institute of Metrology
the Henan science and technology research
the Haizhi project of Henan Association for science and technology
the cultivation plan of young teachers of Henan University of Technology
the innovation fund of Henan University of Technology
Cited by
1 articles.
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