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

arXiv: https://arxiv.org/abs/2406.03086 · HTML: https://mlsanity.com/paper/2406.03086/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 15 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 13 of 13)

- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://mlsanity.com/paper/2205.13542/) (2022)
- [Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps](https://mlsanity.com/paper/2209.12836/) (2022)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [When2com: Multi-Agent Perception via Communication Graph Grouping](https://mlsanity.com/paper/2006.00176/) (2020)
- [Robust Collaborative 3D Object Detection in Presence of Pose Errors](https://mlsanity.com/paper/2211.07214/) (2022)
- [Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges](https://mlsanity.com/paper/2301.06262/) (2023)
- [Semantics-Empowered Communication for Networked Intelligent Systems](https://mlsanity.com/paper/2007.11579/) (2020)
- [UMC: A Unified Bandwidth-efficient and Multi-resolution based Collaborative Perception Framework](https://mlsanity.com/paper/2303.12400/) (2023)
- [AutoCast: Scalable Infrastructure-less Cooperative Perception for Distributed Collaborative Driving](https://mlsanity.com/paper/2112.14947/) (2021)
- [DOLPHINS: Dataset for Collaborative Perception enabled Harmonious and Interconnected Self-driving](https://mlsanity.com/paper/2207.07609/) (2022)
- [MASS: Mobility-Aware Sensor Scheduling of Cooperative Perception for Connected Automated Driving](https://mlsanity.com/paper/2302.13029/) (2023)

## Built on by (most cited first, 4 of 4)

- [DiffCP: Ultra-Low Bit Collaborative Perception via Diffusion Model](https://mlsanity.com/paper/2409.19592/) (2024)
- [A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue](https://mlsanity.com/paper/2505.06997/) (2025)
- [Timeliness-Oriented Scheduling and Resource Allocation in Multi-Region Collaborative Perception](https://mlsanity.com/paper/2601.04542/) (2026)
- [TALSC: Timeliness-Aware Large-Small VLM Collaboration for Infrastructure-Assisted Autonomous Driving](https://mlsanity.com/paper/2608.01998/) (2026)

## Abstract (arXiv)

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.