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HP3D-V2V: High-Precision 3D Object Detection Vehicle-to-Vehicle Cooperative Perception Algorithm.

Hongmei Chen1, Haifeng Wang1, Zilong Liu2

  • 1Faculty of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China.

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|April 13, 2024
PubMed
Summary

This study introduces a novel V2V cooperative perception algorithm for connected autonomous vehicles (CAVs). The proposed method enhances 3D object detection accuracy and efficiency, outperforming existing models.

Keywords:
3D object detectioncooperative perceptioncrossvehicle feature fusionfeature extraction

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Area of Science:

  • Computer Science
  • Robotics
  • Artificial Intelligence

Background:

  • Single-vehicle perception systems in connected autonomous vehicles (CAVs) face limitations like occlusion and weather interference.
  • Cooperative perception aims to enhance situational awareness by sharing sensor data between vehicles.

Purpose of the Study:

  • To propose a high-precision 3D object detection V2V cooperative perception algorithm for CAVs.
  • To address limitations in spatial feature interaction and enhance semantic information in perception.

Main Methods:

  • Utilized a voxel grid-based statistical filter for point cloud denoising.
  • Developed a feature extraction network fusing voxels and PointPillars to generate Bird's-Eye View (BEV) features.
  • Implemented maximum pooling for dimensionality reduction and a cross-vehicle feature fusion module.

Main Results:

  • The proposed algorithm achieved higher average precision (AP) compared to existing coperception models.
  • Experimental validation on the OPV2V dataset confirmed the effectiveness of the approach.
  • Ablation studies validated the contributions of individual components.

Conclusions:

  • The developed V2V cooperative perception algorithm offers improved 3D object detection performance for CAVs.
  • The architecture achieves lightweighting while maintaining high accuracy.
  • This approach enhances feature interaction and semantic understanding in multi-vehicle perception systems.