The integration of unmanned aerial vehicles (UAVs) into shared airspace for beyond visual line of sight (BVLOS) operations presents significant challenges but holds transformative potential for sectors like transportation, construction, energy and defense. A critical prerequisite for this integration is equipping UAVs with enhanced situational awareness to ensure safe operations. Current approaches mainly target single object detection or classification, or simpler sensing outputs that offer limited perceptual understanding and lack the rapid end-to-end processing needed to convert sensor data into safety-critical insights. In contrast, our study leverages radar technology for novel end-to-end semantic segmentation of aerial point clouds to simultaneously identify multiple collision hazards. By adapting and optimizing the PointNet architecture and integrating aerial domain insights, our framework distinguishes five distinct classes: mobile drones (DJI M300 and DJI Mini) and airplanes (Ikarus C42), and static returns (ground and infrastructure) which results in enhanced situational awareness for UAVs. To our knowledge, this is the first approach addressing simultaneous identification of multiple collision threats in an aerial setting, achieving a robust 94% accuracy. This work highlights the potential of radar technology to advance situational awareness in UAVs, facilitating safe and efficient BVLOS operations.

翻译：将无人机（UAV）集成到共享空域中，以执行超视距（BVLOS）任务，虽面临重大挑战，却为交通运输、建筑、能源和国防等领域带来变革潜力。实现这一集成的关键前提是赋予无人机增强的态势感知能力，确保安全运行。当前方法主要集中于单目标检测或分类，或采用感知理解有限的简化传感输出，缺乏将传感器数据转化为安全关键洞察所必需的快速端到端处理。相比之下，本研究利用雷达技术，通过新颖的端到端语义分割方法处理空中点云，同时识别多种碰撞风险。通过对PointNet架构进行适配与优化，并融入空中领域知识，我们的框架能够区分五类目标：移动无人机（大疆M300和大疆Mini）、固定翼飞机（Ikarus C42）以及静态回波（地面与基础设施），从而为无人机提供增强的态势感知能力。据我们所知，这是首个在空中环境中同时识别多种碰撞威胁的方法，实现了稳健的94%准确率。本工作突显了雷达技术在提升无人机态势感知能力、促进安全高效BVLOS运行方面的潜力。