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

• URL: http://arxiv.org/abs/2608.03521v1
• Date: Tue, 04 Aug 2026 12:05:15 GMT
• Title: Pivot-Centric Trajectory Prediction: Bridging Long Horizons via Dynamical Guidance
• Abstract summary: We propose Pivot-Centric Trajectory Prediction (PCTP) to tackle the long-horizon prediction challenge.<n>PCTP decouples the long-term trajectory predicting process into two processes: pivot prediction and pivot-based trajectory refinement.<n>Compared with existing methods, PCTP provides more intermediate guidance while reducing compounding errors.
• Score: 3.2590932088410374
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• SKETCH: Semantic Key-Point Conditioning for Long-Horizon Vessel Trajectory Prediction [3.870589001283509]
  We propose a semantic-key-point-conditioned trajectory modeling framework.<n>It decomposes long-horizon prediction into global semantic decision-making and local motion modeling.<n>Experiments on real-world AIS data demonstrate that the proposed method consistently outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2026-01-26T14:42:31Z)
• Adaptive Conformal Prediction Intervals Over Trajectory Ensembles [50.31074512684758]
  Future trajectories play an important role across domains such as autonomous driving, hurricane forecasting, and epidemic modeling.<n>We propose a unified framework based on conformal prediction that transforms sampled trajectories into calibrated prediction intervals with theoretical coverage guarantees.
  arXiv  Detail & Related papers  (2025-08-18T21:14:07Z)
• Data-Driven Forecasting of High-Dimensional Transient and Stationary Processes via Space-Time Projection [0.0]
  Space-Time Projection (STP) is introduced as a data-driven forecasting approach for high-dimensional and time-resolved data.<n>The method computes extended space-time proper modes from training data spanning a prediction horizon comprising both hindcast and forecast intervals.
  arXiv  Detail & Related papers  (2025-03-31T03:36:59Z)
• SSTP: Efficient Sample Selection for Trajectory Prediction [38.92588125424176]
  Training advanced trajectory prediction models on existing large-scale datasets is time-consuming and computationally expensive.<n>We propose the SSTP framework, which constructs a compact yet density-balanced dataset tailored to trajectory prediction.<n> Experiments show that SSTP achieves comparable performance to full-dataset training using only half the data.
  arXiv  Detail & Related papers  (2024-09-25T22:00:11Z)
• DeTPP: Leveraging Object Detection for Robust Long-Horizon Event Prediction [1.534667887016089]
  We introduce DeTPP, a novel approach inspired by object detection techniques from computer vision. DeTPP employs a unique matching-based loss function that selectively prioritizes reliably predictable events. The proposed hybrid approach enhances the accuracy of next event prediction by up to 2.7% on a large transactional dataset.
  arXiv  Detail & Related papers  (2024-08-23T14:57:46Z)
• HoTPP Benchmark: Are We Good at the Long Horizon Events Forecasting? [1.3654846342364308]
  We introduce HoTPP, the first benchmark specifically designed to rigorously evaluate long-horizon predictions.<n>We identify shortcomings in widely used evaluation metrics, propose a theoretically grounded T-mAP metric, and offer efficient implementations of popular models.<n>We analyze the impact of autoregression and intensity-based losses on prediction quality, and outline promising directions for future research.
  arXiv  Detail & Related papers  (2024-06-20T14:09:00Z)
• PBP: Path-based Trajectory Prediction for Autonomous Driving [3.640190809479174]
  Trajectory prediction plays a crucial role in the autonomous driving stack. Goal-based prediction models have gained traction in recent years for addressing the multimodal nature of future trajectories.
  arXiv  Detail & Related papers  (2023-09-07T14:45:41Z)
• IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction [73.25645602768158]
  IPCC-TP is a novel relevance-aware module based on Incremental Pearson Correlation Coefficient to improve multi-agent interaction modeling. Our module can be conveniently embedded into existing multi-agent prediction methods to extend original motion distribution decoders.
  arXiv  Detail & Related papers  (2023-03-01T15:16:56Z)
• You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction [52.442129609979794]
  Recent deep learning approaches for trajectory prediction show promising performance. It remains unclear which features such black-box models actually learn to use for making predictions. This paper proposes a procedure that quantifies the contributions of different cues to model performance.
  arXiv  Detail & Related papers  (2021-10-11T14:24:15Z)
• SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction [64.16212996247943]
  We present a Sparse Graph Convolution Network(SGCN) for pedestrian trajectory prediction. Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians. visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.
  arXiv  Detail & Related papers  (2021-04-04T03:17:42Z)
• From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting [54.273455592965355]
  Uncertainty in future trajectories stems from two sources: (a) sources known to the agent but unknown to the model, such as long term goals and (b)sources that are unknown to both the agent & the model, such as intent of other agents & irreducible randomness indecisions. We model the epistemic un-certainty through multimodality in long term goals and the aleatoric uncertainty through multimodality in waypoints& paths. To exemplify this dichotomy, we also propose a novel long term trajectory forecasting setting, with prediction horizons upto a minute, an order of magnitude longer than prior works.
  arXiv  Detail & Related papers  (2020-12-02T21:01:29Z)
• TPNet: Trajectory Proposal Network for Motion Prediction [81.28716372763128]
  Trajectory Proposal Network (TPNet) is a novel two-stage motion prediction framework. TPNet first generates a candidate set of future trajectories as hypothesis proposals, then makes the final predictions by classifying and refining the proposals. Experiments on four large-scale trajectory prediction datasets, show that TPNet achieves the state-of-the-art results both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2020-04-26T00:01:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.