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

Sheng Zhou, Yukuan Jia, Ruiqing Mao, Zhaojun Nan, Yuxuan Sun, Zhisheng Niu

TL;DR

This work tackles the challenge of achieving reliable collaborative perception (CP) in intelligent unmanned systems under dynamic wireless conditions. It presents a task-oriented framework that jointly optimizes channel-adaptive region feature maps (RFMs), robust feature fusion via metric learning, and learning-based distributed scheduling using restless multi-armed bandits (RMAB) to select collaborators. Key contributions include channel-adaptive RFM extraction, robust fusion under spatio-temporal misalignment, and the C-MASS scheduling algorithm with online learning, validated by case studies in connected autonomous driving showing CP gains under bandwidth constraints. The framework lays groundwork for timely, energy-efficient, and privacy-aware CP in real-world multi-agent systems with practical implications for autonomous driving and robotics.

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.

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

TL;DR

This work tackles the challenge of achieving reliable collaborative perception (CP) in intelligent unmanned systems under dynamic wireless conditions. It presents a task-oriented framework that jointly optimizes channel-adaptive region feature maps (RFMs), robust feature fusion via metric learning, and learning-based distributed scheduling using restless multi-armed bandits (RMAB) to select collaborators. Key contributions include channel-adaptive RFM extraction, robust fusion under spatio-temporal misalignment, and the C-MASS scheduling algorithm with online learning, validated by case studies in connected autonomous driving showing CP gains under bandwidth constraints. The framework lays groundwork for timely, energy-efficient, and privacy-aware CP in real-world multi-agent systems with practical implications for autonomous driving and robotics.

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.
Paper Structure (17 sections, 5 figures)

This paper contains 17 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Task-Oriented Wireless Communication for CP: An Overview
  3. Key Challenges
  4. Limited Bandwidth and Fading Channels
  5. Inaccurate Spatio-Temporal Alignment
  6. Dynamic Perception Topology & Channel Conditions
  7. Causality Issue
  8. RFM Proposal and Feature Fusion
  9. Channel-Adaptive RFMs Proposal
  10. Feature Fusion under Spatio-Temporal Misalignment
  11. Case Study: Two-hop Feature Fusion in Connected Autonomous Driving
  12. Task-Oriented Distributed Scheduling
  13. Spatial Reasoning
  14. Metadata Exchange
  15. Learning While Scheduling
  16. ...and 2 more sections

Figures (5)

  • Figure 1: Illustration of the proposed task-oriented wireless communication framework for CP in IUSs.
  • Figure 2: Illustration of the proposed channel-adaptive RFM proposal and robust feature fusion modules.
  • Figure 3: Performance of the proposed CP architecture.
  • Figure 4: Illustration of the C-MASS scheme. Left: The diagram of C-MASS algorithm. Upper right: The orange nodes and edges are already scheduled agents and anticipated detections in the current round. Empirical collaboration utilities are calculated using the learned perception topology and the current redundancy level. Bottom right: The confidence bounds of collaboration utilities. Agent 4 is preferred over Agent 3 because it has not been explored for a relatively long time.
  • Figure 5: Performance of CP with different CoV selection schemes. (a) The average perception loss. (b) The average recall.