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Direct distortion prediction method for AR-HUD dynamic distortion correction.

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    |September 14, 2023
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    This study introduces a novel neural network framework for predicting dynamic distortion in automotive augmented reality head-up displays (AR-HUDs). The method enhances distortion correction accuracy without increasing computational load.

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    Area of Science:

    • Computer Vision
    • Automotive Technology
    • Display Systems

    Background:

    • Dynamic distortion significantly degrades the user experience in automotive augmented reality head-up displays (AR-HUDs).
    • The wide field of view and large display area of AR-HUDs lead to complex distortion patterns.
    • Current methods often rely on pre-distorted data, introducing potential errors.

    Purpose of the Study:

    • To propose a novel distortion prediction framework for AR-HUDs.
    • To enable dynamic adaptation for AR-HUD distortion correction.
    • To improve the accuracy of distortion prediction and correction.

    Main Methods:

    • A neural network framework is proposed that directly trains on distorted data, avoiding coordinate interpolation errors.
    • The framework predicts distortion offsets rather than distortion coordinates.
    • A field of view (FOV)-weighted loss function, considering spatial variance, is introduced to enhance prediction accuracy.

    Main Results:

    • The proposed method achieves improved prediction accuracy for AR-HUD dynamic distortion.
    • The framework successfully adapts dynamically to AR-HUD distortion.
    • No increase in network complexity or data processing overhead was observed.

    Conclusions:

    • The developed distortion prediction framework offers a more accurate and efficient solution for AR-HUD distortion correction.
    • Directly training with distorted data and predicting offsets simplifies the process and reduces errors.
    • The FOV-weighted loss function further refines prediction accuracy, enhancing the AR-HUD experience.