{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dstigcn-deformable-spatial-temporal","title":"DSTIGCN: Deformable Spatial-Temporal Interaction Graph Convolution Network for Pedestrian Trajectory Prediction","arxiv_id":null,"date":"2025-01-16","proceeding":"IEEE Transactions on Intelligent Transportation Systems 2025 1","authors":["Wangxing Chen","Haifeng Sang","Jinyu Wang","Zishan Zhao"],"abstract":"Accurate and reliable pedestrian trajectory prediction can reduce the risk of human-vehicle collisions and predict accidents in advance, which is crucial for developing autonomous driving and intelligent monitoring. Previous trajectory prediction methods face two common problems: 1. ignoring the joint modeling of pedestrians’ complex spatial-temporal interactions, and 2. suffering from the long-tail effect, which prevents accurate capture of the diversity of pedestrians’ future movements. To address these problems, we propose a Deformable Spatial-Temporal Interaction Graph Convolution Network (DSTIGCN). First, we construct a spatial graph and employ the attention mechanism to preliminarily describe the spatial interactions of pedestrians at each moment. To solve problem 1, we design a deformable spatial-temporal interaction module. The module autonomously learns the spatial-temporal interaction relationships of pedestrians through the offset of multiple asymmetric deformable convolution kernels in both spatial and temporal dimensions, thereby achieving joint modeling of complex spatial-temporal interactions. Next, we obtain trajectory representation features through graph convolution and then predict the two-dimensional Gaussian distribution parameters of future trajectories using the Temporal Attention-Gated Temporal Convolution Network (TAG-TCN). To address problem 2, we introduce Latin hypercube sampling to sample the two-dimensional Gaussian distribution of future trajectories, thereby improving the multi-modal prediction effect of the model under limited samples. Experiments on ETH, UCY, and SDD datasets have verified that our method can achieve high-precision prediction of pedestrian future trajectories under limited parameters.","url_abs":"https://ieeexplore.ieee.org/abstract/document/10843981","url_pdf":"https://ieeexplore.ieee.org/abstract/document/10843981","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dstigcn-deformable-spatial-temporal","repo_url":"https://github.com/Chenwangxing/DSTIGCN_Master","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"pedestrian-trajectory-prediction","task_name":"Pedestrian Trajectory Prediction"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"tag","task_name":"TAG"},{"task_slug":"trajectory-prediction","task_name":"Trajectory Prediction"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"deformable-convolution","method_name":"Deformable Convolution"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}