{"type":"ScholarlyArticle","title":"TrajAR: Long-Term Trajectory Prediction at Urban Intersections via Multi-scale Interaction Perception","authors":[{"name":"Letian Gong","slug":"letian-gong","given":"Letian","family":"Gong","affiliation":""},{"name":"Yan Lin","slug":"yan-lin","given":"Yan","family":"Lin","affiliation":""},{"name":"Xinyue Zhang","slug":"xinyue-zhang","given":"Xinyue","family":"Zhang","affiliation":""},{"name":"Yiwei Shuang","slug":"yiwei-shuang","given":"Yiwei","family":"Shuang","affiliation":""},{"name":"Guanyu Yao","slug":"guanyu-yao","given":"Guanyu","family":"Yao","affiliation":""},{"name":"Cheng Long","slug":"cheng-long","given":"Cheng","family":"Long","affiliation":""},{"name":"Shengnan Guo","slug":"shengnan-guo","given":"Shengnan","family":"Guo","affiliation":""},{"name":"Youfang Lin","slug":"youfang-lin","given":"Youfang","family":"Lin","affiliation":""},{"name":"Huaiyu Wan","slug":"huaiyu-wan","given":"Huaiyu","family":"Wan","affiliation":""}],"abstract":"Accurate trajectory prediction of multiple road users at urban intersections--including motorized and nonmotorized vehicles and pedestrians--is critical for cooperative vehicle-infrastructure systems and intelligent transportation systems. This study focuses on multiple road users' trajectory prediction in urban intersections. Such predictions face two key challenges: modeling interactions among multiple road users in complex scenarios and achieving accurate long-term prediction. Existing solutions inadequately incorporate these interactions and suffer from error accumulation or overall bias when performing long-term prediction. To overcome these challenges, we propose a long-term Trajectory prediction multi-scale AutoRegressive framework (TrajAR), which follows an encoder-decoder structure. The encoder dynamically perceives road users' motion trends, types, signals, and implicit interactions for effective handling of complex scenarios. The decoder employs a multi-scale prediction strategy that progressively refines the predicted trajectory from coarse to fine scales, where coarse-scale predictions establish long-term path backbones and finer scales enhance precision. These designs allow TrajAR to achieve accurate long-term prediction in real-world scenarios, demonstrated through experiments on four real-world urban datasets from different intersections. Our results show an average improvement of 23.8%. We provide the code of TrajAR at https://github.com/LetianGong/TrajAR.","doi":"10.24963/ijcai.2026/293","url":"https://ijcai.org/proceedings/2026/293","pdf":"https://ijcai.org/proceedings/2026/0293.pdf","markdown":"https://ijcai.org/proceedings/2026/293.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/293","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Data Mining","track":"Main Track","number":293,"pages":{"first":2635,"last":2643},"keywords":["Data Mining: Mining spatial and/or temporal data"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}