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Accurate assessment of driver visibility is crucial in automotive design and safety enhancement, particularly in situations where A-pillars obstruct the driver’s field of view. To address this challenge, this research develops a multi-fidelity Gaussian Process (MF-GP) modeling framework to enhance visibility prediction by integrating low-fidelity (LF) image segmentation data with high-fidelity digital human modeling (DHM) simulations. By leveraging a limited set of high-fidelity samples, the proposed MF-GP framework systematically calibrates low-fidelity data to improve predictive accuracy while reducing computational costs. Two A-pillar cutout designs (3.75 cm and 5 cm) were analyzed under varying HF sampling densities of 3%, 7%, and 10%. Results indicate that the 3.75 cm cutout is more sensitive to sparse HF sampling, requiring a denser HF dataset to achieve stable calibration. In contrast, the 5 cm cutout, benefiting from improved LF-HF alignment, achieves comparable accuracy with fewer HF samples. Model validation using root mean square error (RMSE) and coefficient of determination (R2) confirms that increasing HF sampling enhances surrogate model accuracy, with the effect being more pronounced in cases where model performance is susceptible to high-fidelity data. The proposed framework provides a computationally efficient methodology for driver visibility prediction and human-in-the-loop design applications. Future research could explore adaptive HF sampling strategies and ensemble surrogate modeling techniques to further enhance multi-fidelity learning efficiency.