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Predicting the future trajectories of multiple interacting pedestrians within a scene has increasingly gained importance in various fields, e.g., autonomous driving, human–robot interaction, and so on. The complexity of this problem is heightened due to the social dynamics among different pedestrians and their heterogeneous implicit preferences. In this paper, we present Information Maximizing Spatial-Temporal Graph Convolutional Attention Network (InfoSTGCAN), which takes into account both pedestrian interactions and heterogeneous behavior choice modeling. To effectively capture the complex interactions among pedestrians, we integrate spatial-temporal graph convolution and spatial-temporal graph attention. For grasping the heterogeneity in pedestrians’ behavior choices, our model goes a step further by learning to predict an individual-level latent code for each pedestrian. Each latent code represents a distinct pattern of movement choice. Finally, based on the observed historical trajectory and the learned latent code, the proposed method is trained to cover the ground-truth future trajectory of this pedestrian with a bi-variate Gaussian distribution. We evaluate the proposed method through a comprehensive list of experiments and demonstrate that our method outperforms all baseline methods on the commonly used metrics, Average Displacement Error and Final Displacement Error. Notably, visualizations of the generated trajectories reveal our method’s capacity to handle different scenarios.
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This content will become publicly available on April 7, 2026
Data-Driven Probabilistic Trajectory Learning with High Temporal Resolution in Terminal Airspace
Predicting flight trajectories is a research area that holds significant merit. In this paper, we propose a data-driven learning framework that leverages the predictive and feature extraction capabilities of the mixture models and seq2seq-based neural networks while addressing prevalent challenges caused by error propagation and dimensionality reduction. After training with this framework, the learned model can improve long-step prediction accuracy significantly given the past trajectories and the context information. The accuracy and effectiveness of the approach are evaluated by comparing the predicted trajectories with the ground truth. The results indicate that the proposed method has outperformed the state-of-the-art predicting methods on a terminal airspace flight trajectory dataset. The trajectories generated by the proposed method have a higher temporal resolution (1 time step per second vs 0.1 time step per second) and are closer to the ground truth.
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- Award ID(s):
- 2402689
- PAR ID:
- 10608844
- Publisher / Repository:
- AIAA
- Date Published:
- Journal Name:
- Journal of Aerospace Information Systems
- ISSN:
- 1940-3151
- Page Range / eLocation ID:
- 1 to 11
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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