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June 14, 2026Journal of King Saud University - Computer and Information SciencesOpen Access

Trajectory prediction in heterogeneous environments: integrating multi-scale geometric perception and unbiased interaction modeling

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Authors

WLWenli LiChongqing University of TechnologyPYPengjing YangChongqing University of TechnologyMLMingyang LiChongqing University of Technology

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Implication

Randomized trial demonstrates improved trajectory prediction in multi-agent systems, suggesting enhanced safety in autonomous driving.

Key Points

  • The aim is to address the challenges of multi-agent trajectory prediction in complex driving environments.
  • Developed the PointNetPlus Transformer (PNPT) framework based on a Transformer encoder-decoder structure.
  • Integrated a Multi-scale Residual-enhanced Polyline Encoder (MRPE) to extract geometric features and boost scene understanding.
  • Implemented unbiased local coordinate encoding and an interaction-aware intent query module for efficient modeling.
  • PNPT model achieves state-of-the-art performance on the Waymo Open Motion Dataset with a minADE of 0.5683 and a minFDE of 1.1824.
  • Miss rate recorded at 11.43% and an mAP of 47.21%, outperforming previous baseline models.
  • The effectiveness of each proposed framework module was confirmed through extensive ablation studies.

Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/6a2e44e4b1cc60ccdea8a41ehttps://doi.org/10.1007/s44443-026-00898-x
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