Risk Assessment of UAS Events From the Perspective of Heterogeneous Graphs

With the widespread application of Unmanned Aerial Systems (UAS) in the aviation sector, accidents caused by drones have become increasingly frequent. As a result, numerous studies have been conducted by scholars on the risk assessment of UAS accidents. To address the challenge of evaluating the accident risks arising from the ever-changing operational environments and diverse tasks of UAS, this paper proposes a risk prediction framework for UAS accidents based on Heterogeneous Graph Embedding (HGE). By integrating various node attributes and the relationships between different factors, the framework constructs a heterogeneous graph from UAS aviation incident data. The complex heterogeneous relationships are transformed into homogeneous subgraphs through different path patterns, which are used to represent the data. Random walks are performed on each homogeneous subgraph to capture semantic information, and the Skip-Gram method is employed to embed the semantic attention-weighted walk results into high-dimensional vectors. These vectors are then integrated with the original graph embedding vectors to update the representations of incident nodes, enabling accurate prediction of accident severity and risk types. Furthermore, a meta-path-based filter module is designed to evaluate the key factors contributing to UAS accident risk by comparing the contributions of different meta-paths under various path patterns. Experimental results on a UAS incident dataset demonstrate that the proposed method effectively predicts UAS operational risks in real time, achieving an accuracy of 97.56% for accident severity prediction. Additionally, the method provides stable predictions when dealing with complex heterogeneous data. From the perspective of a novel graph structure, this research offers a new approach for the safety management of future UAS systems.