用真实标签构建统一特征空间，让不同传感器的车实现高效协同感知
GT-Space: Enhancing Heterogeneous Collaborative Perception with Ground Truth Feature Space
- 基于真实标注构建通用特征空间，统一异构感知数据
- 仅需一个适配器即可对齐多源特征，检测准确率显著提升
- 适合自动驾驶中多车异构感知场景，部署灵活且可扩展
在自动驾驶中，多智能体协同感知通过共享感知数据提升探测能力。核心挑战在于处理来自不同传感模态或模型架构的异构特征，导致数据融合困难。现有方法常需重训练编码器或设计成对适配模块，难以实际扩展。为此，我们提出GT-Space——一种面向异构智能体的灵活可扩展协同感知框架。该框架利用真实标注构建公共特征空间，为特征对齐提供统一参考。在此空间下，各智能体仅需一个适配模块即可完成特征投影，无需与其他智能体进行成对交互。此外，我们设计了基于对比损失训练的融合网络，适用于多种模态组合。在仿真数据集OPV2V和V2XSet以及真实世界数据集RCooper上的大量实验表明，GT-Space在检测准确率上持续优于基线，并具备强鲁棒性。代码将发布于https://github.com/KingScar/GT-Space。
原文摘要 · Abstract (English)
In autonomous driving, multi-agent collaborative perception enhances sensing capabilities by enabling agents to share perceptual data. A key challenge lies in handling {\em heterogeneous} features from agents equipped with different sensing modalities or model architectures, which complicates data fusion. Existing approaches often require retraining encoders or designing interpreter modules for pairwise feature alignment, but these solutions are not scalable in practice. To address this, we propose {\em GT-Space}, a flexible and scalable collaborative perception framework for heterogeneous agents. GT-Space constructs a common feature space from ground-truth labels, providing a unified reference for feature alignment. With this shared space, agents only need a single adapter module to project their features, eliminating the need for pairwise interactions with other agents. Furthermore, we design a fusion network trained with contrastive losses across diverse modality combinations. Extensive experiments on simulation datasets (OPV2V and V2XSet) and a real-world dataset (RCooper) demonstrate that GT-Space consistently outperforms baselines in detection accuracy while delivering robust performance. Our code will be released at https://github.com/KingScar/GT-Space.
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