Task-Oriented Wireless Communications for Collaborative Perception in Intelligent Unmanned Systems

TL;DR

This paper presents a task-oriented wireless communication framework for collaborative perception in intelligent unmanned systems, optimizing communication and perception processes to improve environmental understanding under wireless constraints.

Contribution

It introduces a novel joint optimization framework combining channel-adaptive compression, robust fusion, and distributed scheduling for CP in dynamic wireless environments.

Findings

Effective semantic information extraction under wireless constraints

Low-overhead learning-based scheduling algorithm

Verified improvements in autonomous driving scenarios

Abstract

Collaborative Perception (CP) has shown great potential to achieve more holistic and reliable environmental perception in intelligent unmanned systems (IUSs). However, implementing CP still faces key challenges due to the characteristics of the CP task and the dynamics of wireless channels. In this article, a task-oriented wireless communication framework is proposed to jointly optimize the communication scheme and the CP procedure. We first propose channel-adaptive compression and robust fusion approaches to extract and exploit the most valuable semantic information under wireless communication constraints. We then propose a task-oriented distributed scheduling algorithm to identify the best collaborators for CP under dynamic environments. The main idea is learning while scheduling, where the collaboration utility is effectively learned with low computation and communication overhead. Case studies are carried out in connected autonomous driving scenarios to verify the proposed framework. Finally, we identify several future research directions.