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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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STGlow: A Flow-Based Generative Framework With Dual-Graphormer for Pedestrian Trajectory Prediction.

Rongqin Liang, Yuanman Li, Jiantao Zhou

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    |July 26, 2023
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    We introduce STGlow, a novel generative flow framework for pedestrian trajectory prediction. This method accurately models pedestrian motion by optimizing exact log-likelihood, outperforming existing generative adversarial networks and conditional variational autoencoders.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Robotics

    Background:

    • Pedestrian trajectory prediction is crucial for intelligent systems like autonomous driving and robot navigation.
    • Accurate prediction is challenging due to diverse motion behaviors and complex social interactions.
    • Existing methods like GANs and CVAEs have limitations in modeling data distributions, leading to biased or inaccurate trajectories.

    Purpose of the Study:

    • To propose a novel generative flow-based framework, STGlow, for more accurate pedestrian trajectory prediction.
    • To address the limitations of existing generative models in accurately capturing underlying data distributions.
    • To enhance the modeling of temporal dependencies and spatial interactions among pedestrians.

    Main Methods:

    • Developed a generative flow-based framework (STGlow) that optimizes the exact log-likelihood of motion behaviors.
    • Implemented a forward process to simplify complex behaviors and a reverse process to evolve simple behaviors.
    • Introduced a dual-graphormer with graph structures to model temporal dependencies and spatial interactions.

    Main Results:

    • STGlow precisely models the underlying data distribution of pedestrian motion.
    • The framework offers clear physical interpretations for simulating motion evolution.
    • Experimental results on benchmarks show significantly improved performance over state-of-the-art methods.

    Conclusions:

    • STGlow provides a more accurate and robust approach to pedestrian trajectory prediction.
    • The generative flow and dual-graphormer effectively capture complex motion dynamics and interactions.
    • This advancement has significant implications for safety and efficiency in intelligent systems.