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.

通过提出一个面向任务的无线通信框架，本文旨在共同优化通信方案和合作感知（Collaborative Perception, CP）过程，以解决实现 CP 所面临的关键挑战。研究提出了适应性压缩和强健融合方法，目的是在无线通信限制下提取和利用最有价值的语义信息；同时还提出了一种面向任务的分布式调度算法，以在动态环境中识别最佳合作者。通过在连接自动驾驶场景中进行案例研究来验证所提出的框架，并确定了几个未来研究方向。