{"type":"ScholarlyArticle","title":"Pivot-Centric Trajectory Prediction: Bridging Long Horizons via Dynamical Guidance","authors":[{"name":"Xiucong Zhao","slug":"xiucong-zhao","given":"Xiucong","family":"Zhao","affiliation":""},{"name":"Jindong Tian","slug":"jindong-tian","given":"Jindong","family":"Tian","affiliation":""},{"name":"Hao Miao","slug":"hao-miao","given":"Hao","family":"Miao","affiliation":""}],"abstract":"Forecasting precise future motion of surrounding agents is essential for reliable autonomous vehicles. However, as the demand for longer prediction horizons increases, existing endpoint-completion or iterative-refine methods increasingly struggle with weak guidance and compounding errors. To tackle the long-horizon prediction challenge, we propose Pivot-Centric Trajectory Prediction (PCTP). By introducing \"pivots\" and focusing on predicting pivot points along extended trajectories, we divide the long-term prediction task into short-term sub-tasks at various scales. Specifically, PCTP decouples the long-term trajectory predicting process into two processes: pivot prediction and pivot-based trajectory refinement. The pivot prediction process aims to utilize global map context and agent-to-agent interactions to identify these \"pivot points\", while the pivot-based trajectory refinement process focuses on local map details and refines the short-term trajectory based on predicted \"pivot points\". Compared with existing methods, PCTP provides more intermediate guidance while reducing compounding errors. Moreover, PCTP is a flexible approach that can be integrated into most state-of-the-art trajectory prediction models. Experimental results show that PCTP improves the prediction accuracy of leading models on both Argoverse I and Argoverse II datasets with minimal impact on model size. Specifically, PCTP combined with QCNet outperforms all published ensemble-free methods on the Argoverse II leaderboard at submission.","doi":"10.24963/ijcai.2026/364","url":"https://ijcai.org/proceedings/2026/364","pdf":"https://ijcai.org/proceedings/2026/0364.pdf","markdown":"https://ijcai.org/proceedings/2026/364.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/364","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":364,"pages":{"first":3276,"last":3284},"keywords":["AI: Machine Learning","Data Mining: Mining spatial and/or temporal data"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}