DA-STGCN: 4D Trajectory Prediction Based on Spatiotemporal Feature Extraction

• URL: http://arxiv.org/abs/2503.04823v2
• Date: Thu, 13 Mar 2025 03:39:44 GMT
• Title: DA-STGCN: 4D Trajectory Prediction Based on Spatiotemporal Feature Extraction
• Authors: Yuheng Kuang, Zhengning Wang, Jianping Zhang, Zhenyu Shi, Yuding Zhang,
• Abstract summary: We propose DA-STGCN, an innovative graph convolutional network that integrates a dual attention mechanism.<n>Our model reconstructs adjacency matrix through a self-attention approach, enhancing the capture of node correlations.<n>Results demonstrate a notable improvement over current 4D trajectory prediction methods, achieving a 20% and 30% reduction in the Average Displacement Error (Attention) and Final Displacement Error (FDE)
• Score: 6.781509470656284
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The importance of four-dimensional (4D) trajectory prediction within air traffic management systems is on the rise. Key operations such as conflict detection and resolution, aircraft anomaly monitoring, and the management of congested flight paths are increasingly reliant on this foundational technology, underscoring the urgent demand for intelligent solutions. The dynamics in airport terminal zones and crowded airspaces are intricate and ever-changing; however, current methodologies do not sufficiently account for the interactions among aircraft. To tackle these challenges, we propose DA-STGCN, an innovative spatiotemporal graph convolutional network that integrates a dual attention mechanism. Our model reconstructs the adjacency matrix through a self-attention approach, enhancing the capture of node correlations, and employs graph attention to distill spatiotemporal characteristics, thereby generating a probabilistic distribution of predicted trajectories. This novel adjacency matrix, reconstructed with the self-attention mechanism, is dynamically optimized throughout the network's training process, offering a more nuanced reflection of the inter-node relationships compared to traditional algorithms. The performance of the model is validated on two ADS-B datasets, one near the airport terminal area and the other in dense airspace. Experimental results demonstrate a notable improvement over current 4D trajectory prediction methods, achieving a 20% and 30% reduction in the Average Displacement Error (ADE) and Final Displacement Error (FDE), respectively. The incorporation of a Dual-Attention module has been shown to significantly enhance the extraction of node correlations, as verified by ablation experiments.

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