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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.
Sensors (Basel, Switzerland)
|April 13, 2024
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.
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.
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