Metamodels and Vision Obstruction: A New Lens on Driver Visibility

Abstract Driver visibility studies, particularly focusing on the impact of A-pillar designs and cutout modifications, are a critical challenge in the automotive industry. Utilizing Gaussian Process Regression (GPR) modeling, we comprehensively analyze how different A-pillar cutout sizes influence visibility in various traffic scenarios. Our research compares two vehicle types, SUVs and sedans, across a spectrum of real-world situation, including interactions with traffic lights, bicycles, and pedestrian crossings. The uniqueness of our approach lies in the application of digital human modeling data to construct surrogate models, which facilitates a nuanced understanding of the relationship between driver anthropometry, visibility, and cutout design. The results reveal a paradox in A-pillar design, where increasing the number of cutouts does not linearly correlate with enhanced visibility, thus challenging conventional design practices. Additionally, the study highlights the multimodal nature of visibility across different driver heights, suggesting the need for a tailored approach in automotive design. Looking forward, our research sets the foundation for future work that aims to optimize cutout designs using techniques like Bayesian Optimization, with a particular focus on improving visibility for drivers of smaller stature. This study contributes significantly to the evolution of automotive design, advocating for a shift towards more inclusive and ergonomic vehicles that cater to the diverse needs of drivers, thereby enhancing overall road safety.