  Multi-agent collaborative perception as a potential application for
vehicle-to-everything communication could significantly improve the perception
performance of autonomous vehicles over single-agent perception. However,
several challenges remain in achieving pragmatic information sharing in this
emerging research. In this paper, we propose SCOPE, a novel collaborative
perception framework that aggregates the spatio-temporal awareness
characteristics across on-road agents in an end-to-end manner. Specifically,
SCOPE has three distinct strengths: i) it considers effective semantic cues of
the temporal context to enhance current representations of the target agent;
ii) it aggregates perceptually critical spatial information from heterogeneous
agents and overcomes localization errors via multi-scale feature interactions;
iii) it integrates multi-source representations of the target agent based on
their complementary contributions by an adaptive fusion paradigm. To thoroughly
evaluate SCOPE, we consider both real-world and simulated scenarios of
collaborative 3D object detection tasks on three datasets. Extensive
experiments demonstrate the superiority of our approach and the necessity of
the proposed components.
