Collaborative perception by leveraging the shared semantic information plays a crucial role in overcoming the individual limitations of isolated agents. However, existing collaborative perception methods tend to focus solely on the spatial features of semantic information, while neglecting the importance of the temporal dimension. Consequently, the potential benefits of collaboration remain underutilized. In this article, we propose Select2Col, a novel collaborative perception framework that takes into account the \underline{s}patial-t\underline{e}mpora\underline{l} importanc\underline{e} of semanti\underline{c} informa\underline{t}ion. Within the Select2Col, we develop a collaborator selection method that utilizes a lightweight graph neural network (GNN) to estimate the importance of semantic information (IoSI) of each collaborator in enhancing perception performance, thereby identifying contributive collaborators while excluding those that potentially bring negative impact. Moreover, we present a semantic information fusion algorithm called HPHA (historical prior hybrid attention), which integrates multi-scale attention and short-term attention modules to capture the IoSI in feature representation from the spatial and temporal dimensions respectively, and assigns IoSI-consistent weights for efficient fusion of information from selected collaborators. Extensive experiments on three open datasets demonstrate that our proposed Select2Col significantly improves the perception performance compared to state-of-the-art approaches. The code associated with this research is publicly available at https://github.com/huangqzj/Select2Col/.

翻译：摘要：通过共享语义信息实现的协同感知在克服孤立个体的局限性中发挥着关键作用。然而，现有的协同感知方法往往仅关注语义信息的空间特征，而忽略了时间维度的重要性，导致协同的潜在优势未能充分发挥。本文提出Select2Col，一种考虑语义信息时空重要性的新型协同感知框架。在该框架内，我们开发了一种协作者选择方法，利用轻量级图神经网络（GNN）评估每个协作者语义信息对感知性能提升的重要性（IoSI），从而识别具有贡献的协作者，同时排除可能带来负面影响的个体。此外，我们提出了一种名为HPHA（历史先验混合注意力）的语义信息融合算法，该算法集成了多尺度注意力和短期注意力模块，分别从空间和时间维度捕获特征表示中的IoSI，并分配与IoSI一致的权重，以高效融合所选协作者的信息。在三个公开数据集上的大量实验表明，与现有最先进方法相比，提出的Select2Col显著提升了感知性能。本研究的代码已公开于https://github.com/huangqzj/Select2Col/。