机群需要一个上下文平面：重新思考自主无人机的协同感知
Fleets Need a Context Plane: Rethinking Cooperative Perception for Autonomous Drones
- Texas Tech University（德克萨斯理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对无人机机群协同感知的上下文适配问题，提出上下文平面接口，实现低开销运行时策略决策，在UAV3D上验证了其准确率与全量共享相当且带宽占用极低。
AI中文摘要：
协同感知允许无人机机群融合来自多个视点的观测结果。然而，现有系统通常在设计时就固定了特征共享策略，或仅适配单一上下文信号，这与无人机机群的实际情况不符——其任务、带宽、编队几何形状和场景覆盖范围会在飞行过程中发生变化。我们通过使用已发布的DiscoNet检查点在评估时控制特征交换（无需重新训练），量化了上下文盲共享在UAV3D数据集上的代价。感知任务感知型共享的准确率与全量共享相当，仅使用5%-10%的字节量。测试的最优对等体选择策略会随字节预算变化，选择错误的策略会导致最多7.7的平均精度（AP）损失。此外，在受限预算下，两个具有相同全场景准确率的策略在任务区域内的AP相差5.9，表明必须共同考虑多个上下文维度。因此，我们提出了上下文平面，这是一个用于运行时上下文的有界结构化接口。每架无人机以10Hz频率发布大小不超过1KB的描述符，轻量、可替换的策略利用机群上下文决定每架无人机的计算、共享和融合内容。现有共享方案在该接口内成为固定策略。在Jetson AGX Orin上的ROS 2原型中，上下文平面占用约0.01%的数据平面带宽，每次策略决策耗时0.10ms。这些结果表明，显式上下文接口可支持低开销的运行时适配，无需修改或重新训练感知模型。
英文摘要：
Cooperative perception allows a drone fleet to combine observations from multiple viewpoints. However, existing systems typically fix their feature-sharing policies at design time or adapt to only one context signal. This is a poor fit for aerial fleets, whose missions, bandwidth, formation geometry, and scene coverage can change during flight. We quantify the cost of context-blind sharing on UAV3D by controlling feature exchange at evaluation time using a released DiscoNet checkpoint, without retraining. Mission-aware sharing matches full-sharing accuracy while using only 5-10% of the bytes. The best tested peer selection policy changes with the byte budget, and choosing the wrong policy loses up to 7.7 AP. Moreover, under a constrained budget, two policies with the same full-scene accuracy differ by 5.9 AP within the mission region, showing that multiple context axes must be considered jointly. We therefore propose the context plane, a bounded, structured interface for runtime context. Each drone publishes a descriptor of at most 1 KB at 10 Hz, and lightweight, replaceable policies use the fleet context to decide what each drone computes, shares, and fuses. Existing sharing schemes become fixed policies within this interface. In our ROS 2 prototype on a Jetson AGX Orin, the context plane uses approximately 0.01% of the data-plane bandwidth, and each policy decision takes 0.10 ms. These results show that an explicit context interface can support low-overhead runtime adaptation without modifying or retraining the perception model.
