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文章背景与核心概要

在自动驾驶和智能交通系统中，多智能体协同感知通过共享信息显著提升了对复杂环境的理解能力。然而，现实场景中传感器模态（如相机、激光雷达等）和模型架构的异构性，给特征融合与通信带来了巨大挑战。传统的基于协议的两阶段方法虽然试图将不同模态的特征映射到共享空间中，但由于模态特异性统计混杂因子的存在，往往会导致语义不一致和误差累积，在模态差异较大的场景下尤为明显。

为了克服这一技术瓶颈，本文介绍了全新的 CauseCollab 框架。该框架首次将因果视角引入协议空间的表征学习中，通过因果度量学习（Causal Metric Learning）显式地将语义核心因素与模态特异性的统计噪声剥离。同时，它引入了上下文引导的统一转换器（Context-guided Unified Converter），确保了跨模态的语义一致性，并且新模态的接入仅需训练极少参数的适配器（Adapters）。

在 OPV2V 和 DAIR-V2X 数据集上的广泛实验表明，CauseCollab 不仅达到了当前最先进（SOTA）的性能水平，而且在面对显著模态鸿沟（Modality Gaps）的复杂场景时，展现出了更为卓越的鲁棒性和增益效果。

CauseCollab: Causal Unified and Modality-Agnostic Network for Heterogeneous Collaborative Perception

CauseCollab: Causal Unified and Modality-Agnostic Network for Heterogeneous Collaborative Perception

📄 Summary

CauseCollab is a novel framework designed to improve multi-agent collaborative perception by addressing the challenges of heterogeneous sensor modalities and model architectures. While traditional protocol-based methods map diverse features into a shared space, they often suffer from semantic inconsistency and error accumulation caused by modality-specific statistical confounders.

CauseCollab solves this by applying a causal perspective to representation learning. Using causal metric learning and a context-guided Unified Converter, it successfully disentangles semantic factors from statistical noise. This ensures cross-modal consistency, allows new modalities to be integrated with minimal parameter adapters, and achieves state-of-the-art performance on the OPV2V and DAIR-V2X datasets—particularly in scenarios with significant modality gaps.

CauseCollab 是一个旨在通过解决异构传感器模态和模型架构挑战来改进多智能体协同感知的新型框架。虽然传统的基于协议的方法将多样化的特征映射到共享空间中，但它们往往受到由模态特定统计混杂因子引起的语义不一致和误差累积的困扰。

CauseCollab 通过将因果视角应用于表征学习来解决这一问题。通过使用因果度量学习和上下文引导的统一转换器（Unified Converter），它成功地将语义因素与统计噪声解耦。这确保了跨模态的一致性，允许以极少的参数适配器集成新模态，并在 OPV2V 和 DAIR-V2X 数据集上取得了最先进的性能——尤其是在具有显著模态差距的场景中。

📌 Article Metadata

Field Details
Title CauseCollab: Causal Unified and Modality-Agnostic Network for Heterogeneous Collaborative Perception
Authors Weize Li, Yang Li, Quan Yuan, Xiaoyuan Fu, Guiyang Luo, Jinglin Li
Primary Subject Artificial Intelligence (cs.AI)
Submitted On September 3, 2026
Status Accepted at ICML 2026 (17 pages)
Identifiers arXiv: 2609.03818 [cs.AI]
DOI 10.48550/arXiv.2609.03818
License Creative Commons Attribution 4.0 International
字段 详情
标题 CauseCollab: Causal Unified and Modality-Agnostic Network for Heterogeneous Collaborative Perception
作者 Weize Li, Yang Li, Quan Yuan, Xiaoyuan Fu, Guiyang Luo, Jinglin Li
主要学科 人工智能 (cs.AI)
提交于 2026年9月3日
状态 已被 ICML 2026 接收（17页）
标识符 arXiv: 2609.03818 [cs.AI]
DOI 10.48550/arXiv.2609.03818
许可协议 知识共享署名 4.0 国际版

🔍 Abstract

Collaborative perception enhances environment understanding through multi-agent information sharing, but its performance in real-world scenarios is constrained by heterogeneous sensor modalities and model architectures. Recent protocol-based two-stage methods alleviate this problem by mapping heterogeneous features into a shared protocol space; however, independently trained modality-specific converters often generate modality-specific pseudo-protocol distributions, leading to semantic inconsistency and error accumulation, which is particularly pronounced in scenarios with large modality discrepancies.

To address this issue, we propose CauseCollab, a causal unified and modality-agnostic network. CauseCollab formulates representation learning in the protocol space from a causal perspective, explicitly disentangling semantic factors from modality-specific statistical confounders via causal metric learning. Meanwhile, CauseCollab adopts a context-guided Unified Converter for heterogeneous modalities to ensure cross-modal semantic consistency. In addition, integrating new modalities only requires training adapters with minimal parameters. Extensive experiments on the OPV2V and DAIR-V2X datasets demonstrate that CauseCollab achieves state-of-the-art performance, with more significant gains in scenarios involving large modality gaps.

协同感知通过多智能体信息共享增强了对环境的理解，但其在现实场景中的性能受到异构传感器模态和模型架构的制约。最近基于协议的两阶段方法通过将异构特征映射到共享的协议空间中来缓解这一问题；然而，独立训练的模态特定转换器往往会产生模态特定的伪协议分布，从而导致语义不一致和误差累积，这在模态差异较大的场景中尤为明显。

为了解决这一问题，我们提出了 CauseCollab，即一个因果统一且模态无关的网络。CauseCollab 从因果视角构建了协议空间中的表征学习，通过因果度量学习显式地将语义因子与模态特定的统计混杂因子解耦。同时，CauseCollab 针对异构模态采用了上下文引导的统一转换器，以确保跨模态的语义一致性。此外，集成新模态仅需训练具有极少参数的适配器。在 OPV2V 和 DAIR-V2X 数据集上进行的广泛实验表明，CauseCollab 实现了最先进的性能，并且在涉及大模态差距的场景中收益更为显著。