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CPD-KD: a cooperative perception network for discrepancy feature fusion through knowledge distillation.
Caizhen He1, Hai Wang2, Tong Luo3
1School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, 212013, China.
Scientific Reports
|July 27, 2025
Summary
Intelligent connected vehicles use roadside sensors and 5G for enhanced environmental perception. A new Cooperative Perception network for Discrepancy feature fusion through Knowledge Distillation (CPD-KD) improves accuracy by fusing vehicle and infrastructure data.
Area of Science:
- Intelligent Transportation Systems
- Computer Vision
- Wireless Communication
Background:
- Environmental perception for intelligent connected vehicles (ICVs) traditionally relies on onboard sensors, limiting range and accuracy.
- Existing methods struggle with blurred features and information loss when fusing vehicle and infrastructure data.
- Cooperative perception using roadside units (RSUs) and 5G enhances ICV situational awareness.
Purpose of the Study:
- To propose a novel Cooperative Perception network for Discrepancy feature fusion through Knowledge Distillation (CPD-KD).
- To address challenges of blurred features and information loss in traditional cooperative perception methods.
- To improve the accuracy and efficiency of environmental perception for intelligent connected vehicles.
Main Methods:
- Developed a Sparse convolution-based Knowledge Distillation network (SKD) to refine single-view point cloud features using fused viewpoint data.
- Introduced a Discrepancy Feature Attention Fusion module to effectively integrate differential information between vehicle and infrastructure sensing.
- Validated the CPD-KD algorithm on real-world (DAIR-V2X) and simulated (V2X-Set) datasets.
Main Results:
- The SKD network successfully alleviated blurring of target features in point cloud data.
- The Discrepancy Feature Attention Fusion module enhanced the cooperative efficiency of vehicle-infrastructure data fusion.
- CPD-KD demonstrated significant improvements in the accuracy of cooperative perception.
Conclusions:
- The proposed CPD-KD algorithm effectively enhances cooperative perception accuracy for intelligent connected vehicles.
- Knowledge distillation and discrepancy feature fusion are key to overcoming limitations in current cooperative perception systems.
- CPD-KD offers a promising solution for more robust and accurate environmental sensing in connected vehicle environments.
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