面向自动驾驶协同感知的高效传输方法综述

杜少毅; 孙远; 刘宇颖; 曹俱睿; 卓帅臣; 田智强; 郑心湖

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面向自动驾驶协同感知的高效传输方法综述

更新时间：2026-06-22

- 面向自动驾驶协同感知的高效传输方法综述
- A Review of Efficient Transmission for Cooperative Perception in Autonomous Driving
- 西安交通大学学报 2026年
- 作者机构：
  
  1.西安交通大学人工智能学院，710049，西安
  2.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：
  中图分类号： TP183
- 收稿：2026-03-30，
  
  修回：2026-06-19，
  
  录用：2026-06-22，
- 稿件说明：
移动端阅览
杜少毅, 孙远, 刘宇颖, 等. 面向自动驾驶协同感知的高效传输方法综述[J/OL]. 西安交通大学学报, 2026.

DU Shaoyi, SUN Yuan, LIU Yuying, et al. A Review of Efficient Transmission for Cooperative Perception in Autonomous Driving[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY, 2026.
杜少毅, 孙远, 刘宇颖, 等. 面向自动驾驶协同感知的高效传输方法综述[J/OL]. 西安交通大学学报, 2026. DOI：

DU Shaoyi, SUN Yuan, LIU Yuying, et al. A Review of Efficient Transmission for Cooperative Perception in Autonomous Driving[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY, 2026. 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，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。本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

Cooperative perception allows vehicles to share information with other agents

effectively reducing perception blind spots for single-vehicle intelligence in complex

long-tail scenarios. However

real-world deployment faces major bottlenecks

primarily due to limited wireless bandwidth and multi-source heterogeneous data. This article provides a comprehensive review of communication-efficient transmission methods for cooperative perception in autonomous driving

alongside open-source datasets and future trends. To reduce communication overhead

the review begins by examining information filtering strategies within the perception process. It then explores cooperative relationship modeling and feature alignment mechanisms among heterogeneous agents. Furthermore

this article summarizes feature compression techniques designed to tackle strict data transmission constraints. Additionally

the review categorizes large-scale open-source datasets in this field and analyzes current bottlenecks

particularly in scene cognition and dynamic network security. Ultimately

this article aims to provide a theoretical reference for building more robust and secure vehicle-infrastructure cooperative networks.

关键词

Keywords

references

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