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Updated: Jan 7, 2026

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HUGSIM: A Real-Time, Photo-Realistic and Closed-Loop Simulator for Autonomous Driving.

Hongyu Zhou, Longzhong Lin, Jiabao Wang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |December 24, 2025
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    Summary
    This summary is machine-generated.

    HUGSIM is a novel, photo-realistic simulator for evaluating autonomous driving systems. It offers a closed-loop environment and comprehensive benchmarks for improved algorithm assessment and fine-tuning.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Artificial Intelligence

    Background:

    • Autonomous driving algorithms have advanced significantly in perception, planning, and control.
    • Evaluating individual components is insufficient; holistic assessment methods are needed.

    Purpose of the Study:

    • Introduce HUGSIM, a closed-loop, photo-realistic, real-time simulator for evaluating autonomous driving algorithms.
    • Address limitations in current assessment methods by providing a holistic evaluation platform.

    Main Methods:

    • Utilize 3D Gaussian Splatting to lift 2D RGB images into 3D space for enhanced rendering quality.
    • Develop a closed-loop simulation environment that dynamically updates states and observations.
    • Implement novel view synthesis techniques for viewpoint extrapolation and 360-degree vehicle rendering.

    Main Results:

    • Achieve improved rendering quality in closed-loop scenarios.
    • Enable dynamic state and observation updates based on control commands.
    • Provide a comprehensive benchmark using over 70 sequences from major datasets (KITTI-360, Waymo, nuScenes, PandaSet) and over 400 scenarios.

    Conclusions:

    • HUGSIM offers a fair and realistic evaluation platform for autonomous driving algorithms.
    • The simulator facilitates intuitive evaluation and fine-tuning in a photo-realistic closed-loop setting.