Cooperative perception enables autonomous agents to share encoded representations over wireless communication to enhance each other's live situational awareness. However, the tension between the limited communication bandwidth and the rich sensor information hinders its practical deployment. Recent studies have explored selection strategies that share only a subset of features per frame while striving to keep the performance on par. Nevertheless, the bandwidth requirement still stresses current wireless technologies. To fundamentally ease the tension, we take a proactive approach, exploiting the temporal continuity to identify features that capture environment dynamics, while avoiding repetitive and redundant transmission of static information. By incorporating temporal awareness, agents are empowered to dynamically adapt the sharing quantity according to environment complexity. We instantiate this intuition into an adaptive selection framework, COOPERTRIM, which introduces a novel conformal temporal uncertainty metric to gauge feature relevance, and a data-driven mechanism to dynamically determine the sharing quantity. To evaluate COOPERTRIM, we take semantic segmentation and 3D detection as example tasks. Across multiple open-source cooperative segmentation and detection models, COOPERTRIM achieves up to 80.28% and 72.52% bandwidth reduction respectively while maintaining a comparable accuracy. Relative to other selection strategies, COOPERTRIM also improves IoU by as much as 45.54% with up to 72% less bandwidth. Combined with compression strategies, COOPERTRIM can further reduce bandwidth usage to as low as 1.46% without compromising IoU performance. Qualitative results show COOPERTRIM gracefully adapts to environmental dynamics, localization error, and communication latency, demonstrating flexibility and paving the way for real-world deployment.

翻译：协同感知使得自主智能体能够通过无线通信共享编码后的表征，以增强彼此对实时态势的感知能力。然而，有限的通信带宽与丰富的传感器信息之间的张力阻碍了其实际部署。近期研究探索了每帧仅共享特征子集的选择策略，同时力求保持性能相当。尽管如此，带宽需求仍对当前无线技术构成压力。为了从根本上缓解这一张力，我们采取一种主动方法，利用时间连续性来识别捕捉环境动态的特征，同时避免静态信息的重复和冗余传输。通过融入时间感知，智能体能够根据环境复杂度动态调整共享数量。我们将这一思路实例化为一个自适应选择框架——COOPERTRIM，该框架引入了一种新颖的保形时间不确定性度量来评估特征相关性，以及一个数据驱动的机制来动态决定共享数量。为评估COOPERTRIM，我们以语义分割和3D检测为例任务。在多个开源协同分割与检测模型中，COOPERTRIM分别实现了高达80.28%和72.52%的带宽削减，同时保持了相当的精度。相对于其他选择策略，COOPERTRIM在减少高达72%带宽的同时，还将交并比（IoU）提升了多达45.54%。结合压缩策略，COOPERTRIM能够在不影响IoU性能的前提下，进一步将带宽使用率降低至低至1.46%。定性结果表明，COOPERTRIM能够优雅地适应环境动态、定位误差和通信延迟，展现了灵活性，并为实际部署铺平了道路。