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    This study introduces a dual-model crowd simulation framework. It combines a small model for speed and a large model for complex scenarios, improving accuracy with incomplete data.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Cognitive Psychology

    Background:

    • Data-driven crowd simulation is vital for realistic modeling in diverse fields.
    • Existing methods struggle with incomplete trajectory data and generalizing to new scenarios.

    Purpose of the Study:

    • To develop a novel crowd simulation framework addressing data incompleteness and generalization limitations.
    • To leverage a dual-model approach inspired by cognitive decision-making for enhanced simulation.

    Main Methods:

    • A collaborative framework using a small model (fast, reactive behavior generation) and a large model (simulation correction).
    • The small model is trained on incomplete real-world trajectory data.
    • The large model refines behaviors in complex scenarios using past experiences.

    Main Results:

    • The proposed framework significantly improves simulation accuracy with missing trajectory data.
    • Enhanced generalization capabilities to unfamiliar crowd scenarios were observed.
    • The dual-model approach effectively balances efficiency and complex reasoning.

    Conclusions:

    • The collaborative small and large model framework offers a robust solution for realistic crowd simulation.
    • This approach enhances the reliability of crowd simulations in the presence of data limitations.
    • The findings have implications for improving simulations in entertainment, urban planning, and safety assessment.