动态调整信息共享策略，让多车感知更准又省带宽。
HydraCollab: Adaptive Collaborative-Perception for Distributed Autonomous Systems
- 根据感知置信度动态选择传什么、何时协作
- 在多个数据集上用更少带宽实现更高精度
- 适合车联网和无人机等带宽受限场景
协同感知通过共享感知信息提升多机器人系统的环境认知能力。现有方法在通信带宽与感知精度之间存在固有权衡：传递更多信息可提高感知性能，但会增加通信开销。然而真实通信网络存在带宽限制，需在不牺牲感知效果的前提下最小化通信负担。为此，我们提出HydraCollab，一种自适应协同感知框架，(i) 选择性传输最具信息量的传感器特征，(ii) 根据空间置信图动态采用中间或后期协作策略。在V2X-R、V2X-Radar和UAV3D-mini数据集上的大量评估表明，HydraCollab在准确率与通信成本之间实现了最佳平衡。相比SOTA方法Where2comm，HydraCollab在V2X-R上仅使用41%带宽，V2X-Radar上仅用26%，同时分别提升性能0.78%和0.75%。代码与模型已开源。
原文摘要 · Abstract (English)
Collaborative-perception enables multi-robot systems to enhance situational awareness by sharing perceptual information. Existing collaborative-perception systems face an inherent trade-off between communication bandwidth requirements and perception accuracy, where methods that exchange more information achieve better perception results at the cost of increased communication overhead. However, real-world communication networks impose bandwidth constraints that require minimizing communication overhead without sacrificing perception performance. To address this challenge, we propose HydraCollab, an adaptive collaborative-perception framework that (i) selectively transmits the most informative sensor features and (ii) dynamically employs collaboration strategies (intermediate or late) based on spatial confidence maps. Extensive evaluations on the V2X-R, V2X-Radar and UAV3D-mini datasets demonstrate that HydraCollab achieves the best overall trade-off between accuracy and communication cost among existing collaborative-perception methods. Relative to SOTA Where2comm, HydraCollab uses only 41% of the bandwidth on V2X-R and 26% on V2X-Radar while improving performance by 0.78% and 0.75% respectively. Our code and models are available at https://github.com/AICPS/HydraCollab.
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