  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/.
