Task-Oriented Wireless Communications for Collaborative Perception in Intelligent Unmanned Systems
This work addresses the problem of unreliable perception in dynamic environments for applications like autonomous driving, but it appears incremental as it builds on existing collaborative perception methods with specific optimizations.
The authors tackled the challenge of implementing Collaborative Perception in intelligent unmanned systems by proposing a task-oriented wireless communication framework that jointly optimizes communication and perception, achieving more holistic and reliable environmental perception in case studies on connected autonomous driving.
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.