mlsanityData to 7 Oct 2026

IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction

arXiv 2303.00575 ↗First posted 1 Mar 2023cs.CV cs.ROcited by 13

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Abstract (arXiv)

Reliable multi-agent trajectory prediction is crucial for the safe planning and control of autonomous systems. Compared with single-agent cases, the major challenge in simultaneously processing multiple agents lies in modeling complex social interactions caused by various driving intentions and road conditions. Previous methods typically leverage graph-based message propagation or attention mechanism to encapsulate such interactions in the format of marginal probabilistic distributions. However, it is inherently sub-optimal. In this paper, we propose IPCC-TP, a novel relevance-aware module based on Incremental Pearson Correlation Coefficient to improve multi-agent interaction modeling. IPCC-TP learns pairwise joint Gaussian Distributions through the tightly-coupled estimation of the means and covariances according to interactive incremental movements. Our module can be conveniently embedded into existing multi-agent prediction methods to extend original motion distribution decoders. Extensive experiments on nuScenes and Argoverse 2 datasets demonstrate that IPCC-TP improves the performance of baselines by a large margin.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 34 of its 49 references that are in the map. First posted on arXiv 1 Mar 2023.

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34 of 49 references.

References · 34

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22 more
  1. 2020VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representationcited by 528
  2. 2019MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Predictioncited by 417
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Citing papers per year: 2024: 11; 2025: 2.

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  1. 2024Multi-agent Long-term 3D Human Pose Forecasting via Interaction-aware Trajectory Conditioningcited by 29
  2. 2024T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memorycited by 20
  3. 2025SPIRAL: Semantic-Aware Progressive LiDAR Scene Generation and Understandingcited by 10
  4. 2024Continual Learning for Smart City: A Surveycited by 9
  5. 2024Recurrent Aligned Network for Generalized Pedestrian Trajectory Predictioncited by 8
  6. 2024TrACT: A Training Dynamics Aware Contrastive Learning Framework for Long-tail Trajectory Predictioncited by 7
  7. 2024CUQDS: Conformal Uncertainty Quantification under Distribution Shift for Trajectory Predictioncited by 5
  8. 2024AMEND: A Mixture of Experts Framework for Long-tailed Trajectory Predictioncited by 4
  9. 2024A Structure-Aware Lane Graph Transformer Model for Vehicle Trajectory Predictioncited by 2
  10. 2024ITPNet: Towards Instantaneous Trajectory Prediction for Autonomous Drivingcited by 2
  11. 2024Map-Free Trajectory Prediction with Map Distillation and Hierarchical Encodingcited by 1
  12. 2025Generative Data Augmentation for Object Point Cloud Segmentationcited by 1
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  1. 2024Relation Learning and Aggregate-attention for Multi-person Motion Predictioncited by 0