# Semantic Communication for Cooperative Perception based on Importance Map

arXiv: https://arxiv.org/abs/2311.06498 · HTML: https://mlsanity.com/paper/2311.06498/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2023.

## Built on by (most cited first, 8 of 8)

- [Systematic Literature Review on Vehicular Collaborative Perception -- A Computer Vision Perspective](https://mlsanity.com/paper/2504.04631/) (2025)
- [A Survey on Semantic Communications in Internet of Vehicles](https://mlsanity.com/paper/2503.03767/) (2025)
- [Semantic Communication for Cooperative Perception using HARQ](https://mlsanity.com/paper/2409.09042/) (2024)
- [JigsawComm: Joint Semantic Feature Encoding and Transmission for Communication-Efficient Cooperative Perception](https://mlsanity.com/paper/2511.17843/) (2025)
- [Deep Reinforcement Learning-Based User Scheduling for Collaborative Perception](https://mlsanity.com/paper/2502.10456/) (2025)
- [HMS-SCP: Task-Oriented Multi-Scale Semantic Communication for V2X Cooperative Perception](https://mlsanity.com/paper/2608.14603/) (2026)
- [Task-Oriented Semantic Communication for Hazard Warning and Remote Operation in Connected Vehicle Platoons](https://mlsanity.com/paper/2609.06242/) (2026)
- [Embodied Semantic Communication for Collective Autonomous Agents: A Tutorial on Representation, Wireless Delivery, and Closed-Loop Coordination](https://mlsanity.com/paper/2609.35936/) (2026)

## Abstract (arXiv)

Cooperative perception, which has a broader perception field than single-vehicle perception, has played an increasingly important role in autonomous driving to conduct 3D object detection. Through vehicle-to-vehicle (V2V) communication technology, various connected automated vehicles (CAVs) can share their sensory information (LiDAR point clouds) for cooperative perception. We employ an importance map to extract significant semantic information and propose a novel cooperative perception semantic communication scheme with intermediate fusion. Meanwhile, our proposed architecture can be extended to the challenging time-varying multipath fading channel. To alleviate the distortion caused by the time-varying multipath fading, we adopt explicit orthogonal frequency-division multiplexing (OFDM) blocks combined with channel estimation and channel equalization. Simulation results demonstrate that our proposed model outperforms the traditional separate source-channel coding over various channel models. Moreover, a robustness study indicates that only part of semantic information is key to cooperative perception. Although our proposed model has only been trained over one specific channel, it has the ability to learn robust coded representations of semantic information that remain resilient to various channel models, demonstrating its generality and robustness.