用激光雷达数据识别飞机尾流涡旋，让模型决策过程透明可懂。
Explainable LiDAR 3D Point Cloud Segmentation and Clustering for Detecting Airplane-Generated Wind Turbulence
- 基于动态图卷积网络分割点云，再用聚类优化识别结果。
- 实测与仿真数据对比验证，性能优于四种基线方法。
- 引入扰动解释技术，帮助空管人员理解模型判断依据。
尾流涡旋——由飞机产生的强而有序的空气湍流——对航空安全构成重大威胁，因此需要准确可靠的检测方法。本文提出一种先进的可解释机器学习方法，利用激光雷达（LiDAR）数据实现有效的尾流涡旋检测。该方法采用动态图卷积神经网络（DGCNN）进行语义分割，将3D LiDAR点云划分为有意义的区域，再通过聚类技术进一步优化。研究创新之处在于使用基于扰动的解释技术，揭示模型决策过程，增强空中交通监管者与管制员对系统的信任。实验基于实测和模拟的LiDAR扫描数据，对比四种基线方法，验证了本方法在准确性与可靠性上的显著优势。该语义分割与聚类相结合的实时尾流追踪方案，显著提升了航空安全水平，同时确保方法有效且可解释。
原文摘要 · Abstract (English)
Wake vortices - strong, coherent air turbulences created by aircraft - pose a significant risk to aviation safety and therefore require accurate and reliable detection methods. In this paper, we present an advanced, explainable machine learning method that utilizes Light Detection and Ranging (LiDAR) data for effective wake vortex detection. Our method leverages a dynamic graph CNN (DGCNN) with semantic segmentation to partition a 3D LiDAR point cloud into meaningful segments. Further refinement is achieved through clustering techniques. A novel feature of our research is the use of a perturbation-based explanation technique, which clarifies the model's decision-making processes for air traffic regulators and controllers, increasing transparency and building trust. Our experimental results, based on measured and simulated LiDAR scans compared against four baseline methods, underscore the effectiveness and reliability of our approach. This combination of semantic segmentation and clustering for real-time wake vortex tracking significantly advances aviation safety measures, ensuring that these are both effective and comprehensible.
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