---
title: "Pivot-Centric Trajectory Prediction: Bridging Long Horizons via Dynamical Guidance"
authors:
  - "Xiucong Zhao"
  - "Jindong Tian"
  - "Hao Miao"
doi: "10.24963/ijcai.2026/364"
url: "https://ijcai.org/proceedings/2026/364"
pdf: "https://ijcai.org/proceedings/2026/0364.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "3276-3284"
track: "Main Track"
keywords:
  - "AI: Machine Learning"
  - "Data Mining: Mining spatial and/or temporal data"
---

# Pivot-Centric Trajectory Prediction: Bridging Long Horizons via Dynamical Guidance

Xiucong Zhao, Jindong Tian, Hao Miao

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 3276-3284. DOI: [10.24963/ijcai.2026/364](https://doi.org/10.24963/ijcai.2026/364)

## 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.