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BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous

Ruochen Li, Stamos Katsigiannis, Tae-Kyun Kim

    IEEE Transactions on Neural Networks and Learning Systems
    |March 21, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces behavioral pseudo-labels to improve trajectory prediction for autonomous vehicles and surveillance. These labels capture agent behaviors from motion data, enhancing prediction accuracy without costly annotations.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Robotics

    Background:

    • Trajectory prediction is crucial for autonomous vehicles (AVs) and surveillance, but current methods struggle with heterogeneous agents (vehicles, cyclists) or rely on expensive labels.
    • Pedestrian-focused models are limited in mixed-traffic scenarios, while methods using class labels are costly and lack intra-class behavioral nuance.

    Purpose of the Study:

    • To develop a novel approach for trajectory prediction that accurately models diverse agent behaviors using only motion features.
    • To introduce 'behavioral pseudo-labels' that capture behavior distributions for both pedestrians and heterogeneous agents.
    • To propose and validate a framework, the behavioral pseudo-label informed sparse graph convolution network (BP-SGCN), for improved trajectory prediction.

    Main Methods:

    • Developed behavioral pseudo-labels derived solely from motion features to represent agent behavior distributions.
    • Proposed the behavioral pseudo-label informed sparse graph convolution network (BP-SGCN) to learn these pseudo-labels and integrate them into a trajectory prediction model.
    • Implemented a cascaded training scheme: unsupervised pseudo-label learning followed by supervised end-to-end fine-tuning for trajectory prediction accuracy.

    Main Results:

    • Behavioral pseudo-labels effectively model distinct behavior clusters within agent trajectories.
    • The BP-SGCN framework significantly improves trajectory prediction accuracy compared to existing methods.
    • Demonstrated superior performance on both pedestrian-only (ETH/UCY, SDD) and heterogeneous agent datasets (SDD, Argoverse1).

    Conclusions:

    • Behavioral pseudo-labels offer a powerful, annotation-free method for capturing agent behavior in trajectory prediction.
    • The BP-SGCN model provides a robust and accurate solution for trajectory prediction in complex, heterogeneous traffic environments.
    • This work advances the state-of-the-art in trajectory prediction, enabling more reliable decision-making for autonomous systems.