A Review of Efficient Transmission Methods for Cooperative Perception in Autonomous Driving

DU Shaoyi; SUN Yuan; LIU Yuying; CAO Jurui; ZHUO Shuaichen; TIAN Zhiqiang; ZHENG Xinhu

doi:10.7652/xjtuxb202610001

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A Review of Efficient Transmission Methods for Cooperative Perception in Autonomous Driving

Special Topic Fusion Perception and Decision-Making Planning for Intelligent Driving | 更新时间：2026-09-29

- A Review of Efficient Transmission Methods for Cooperative Perception in Autonomous Driving
- Journal of Xi'an Jiaotong University Vol. 60, Issue 10, Pages: 1-21(2026)
- 作者机构：
  
  西安交通大学人工智能学院，710049，西安
  香港科技大学（广州）系统枢纽智能交通学域，511453，广州
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202610001
  CLC： TP183
- Received：30 March 2026，
  
  Online First：24 June 2026，
  
  Published：10 October 2026
- 稿件说明：
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DU Shaoyi, SUN Yuan, LIU Yuying, et al. A Review of Efficient Transmission Methods for Cooperative Perception in Autonomous Driving[J/OL]. Journal of Xi'an Jiaotong University,2026,60 (10):1-21. https://doi.org/10.7652/xjtuxb202610001.
DOI：

DU Shaoyi, SUN Yuan, LIU Yuying, et al. A Review of Efficient Transmission Methods for Cooperative Perception in Autonomous Driving[J/OL]. Journal of Xi'an Jiaotong University,2026,60 (10):1-21. https://doi.org/10.7652/xjtuxb202610001. DOI：

摘要

协同感知技术依托智能网联汽车与车路云协同网络，通过多智能体间的信息交互，有效弥补了单车智能在遮挡、远距离探测等长尾场景下的感知盲区。然而，受限的无线通信带宽与多源异构数据已成为系统规模化部署的核心瓶颈，因此围绕这一问题系统阐述了面向自动驾驶协同感知的通信高效传输方法。首先，从空间区域级和实例任务级两个层次，归纳了抑制冗余背景传输的特征筛选机制。其次，围绕交互拓扑构建、时空位姿对齐与跨模态语义对齐等多模态信息融合关键问题，总结了应对节点异构、时间异步与空间错位的代表性方法。然后，从特征压缩稀疏表示、生成式补全与大模型语义协同三条路径，梳理了带宽约束下兼顾通信效率与感知精度的实现方式。最后，系统整理了该领域的大规模开源数据集，并深入分析了当前在场景认知、动态网络安全以及生成式重构可信性等方面面临的瓶颈与未来发展趋势。该文旨在系统梳理相关研究进展，为构建更加鲁棒、安全的智能网联车路协同网络提供理论参考。

Abstract

Based on intelligent connected vehicles and vehicle-road-cloud cooperative networks

the perception blind spots of single-vehicle intelligence in long-tail scenarios

such as occlusion and long-range detection

are effectively compensated by cooperative perception technology through information interaction among multiple agents. However

the large-scale deployment of such systems is critically bottlenecked by limited wireless communication bandwidth and multi-source heterogeneous data. To address this issue

communication-efficient transmission methods for cooperative perception in autonomous driving are systematically reviewed. First

feature filtering mechanisms for suppressing redundant background transmission are summarized at both the spatial region and instance task levels. Second

representative methods for addressing node heterogeneity

temporal asynchrony

and spatial misalignment are summarized

focusing on key issues in multimodal information fusion

including interaction topology construction

spatiotemporal pose alignment

and cross-modal semantic alignment. Then

implementation approaches that balance communication efficiency and perception accuracy under bandwidth constraints are reviewed along three pathways: sparse representation for feature compression

generative completion

and semantic collaboration with large models. Finally

large-scale open-source datasets in this field are systematically compiled

and current bottlenecks and future development trends in scene cognition

dynamic network security

and the credibility of generative reconstruction are deeply analyzed. Through this paper

relevant research progress is systematically reviewed

and a theoretical reference is provided for the construction of more robust and secure intelligent connected vehicle-road cooperative networks.

关键词

Keywords

references

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