A Task-Oriented Real-Time and Robust Feature Compression and Selection Method in Collaborative Intelligence System

The emerging autonomous driving has stringent requirements for latency and reliability. In this paper, we propose a task-oriented real-time and robust feature compression and selection method in collaborative intelligence system. Our design, consisting of a three-dimensional channel compression (TDCC) module and a one-dimensional feature selection (ODFS) module, can efficiently reduce feature size in different dimensional spaces. The TDCC initially reduces the number of feature channels in three-dimensional space. Subsequently, the ODFS flattens the three-dimensional feature maps into a one-dimensional feature vector and selects the most effective feature dimensions of the vector for transmission. Specifically, in ODFS, the most relevant information is first aggregated into specific dimensions through an information-theory-based inductive loss function. Then, the important features are selected using the mask generated by the mask generator. Extensive experiments demonstrate that the proposed method outperforms the baseline in both communication latency and task accuracy, while exhibiting robustness against poor channel conditions.