JigsawComm: Joint Semantic Feature Encoding and Transmission for Communication-Efficient Cooperative Perception
Chenyi Wang, Zhaowei Li, Ming F. Li, Wujie Wen

TL;DR
JigsawComm is an end-to-end framework that enhances multi-agent cooperative perception by selectively transmitting semantically relevant features, significantly reducing communication bandwidth while maintaining or improving perception accuracy.
Contribution
It introduces a novel joint semantic feature encoding and transmission method that minimizes redundancy and optimizes data exchange in cooperative perception systems.
Findings
Reduces data volume by over 20-500 times on benchmarks.
Maintains or exceeds state-of-the-art perception accuracy.
Effectively eliminates cross-agent redundancy.
Abstract
Multi-agent cooperative perception (CP) promises to overcome the inherent occlusion and range limitations of single-agent systems in autonomous driving, yet its practicality is severely constrained by limited Vehicle-to-Everything (V2X) communication bandwidth. Existing approaches attempt to improve bandwidth efficiency via compression or heuristic message selection, but neglect the semantic relevance and cross-agent redundancy of the transmitted data. In this paper, we formulate a joint semantic feature encoding and transmission problem that maximizes CP accuracy under a communication budget, and introduce JigsawComm, an end-to-end semantic-aware framework that learns to ``assemble the puzzle'' of multi-agent feature transmission. JigsawComm uses a regularized encoder to extract \emph{sparse, semantically relevant features}, and a lightweight Feature Utility Estimator (FUE) to predict…
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Taxonomy
TopicsAdvanced Neural Network Applications · Adversarial Robustness in Machine Learning · Embodied and Extended Cognition
