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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. 

Abstract Computer-aided design (CAD) is a standard design tool used in engineering practice and by students. CAD has become increasingly analytic and inventive in incorporating artificial intelligence (AI) approaches to design, e.g., generative design (GD), to help expand designers' divergent thinking. However, generative design technologies are relatively new, we know little about generative design thinking in students. This research aims to advance our understanding of the relationship between aspects of generative design thinking and traditional design thinking. This study was set in an introductory graphics and design course where student designers used Fusion 360 to optimize a bicycle wheel frame. We collected the following data from the sample: divergent and convergent psychological tests and an open-ended response to a generative design prompt (called the generative design reasoning elicitation problem). A Spearman's rank correlation showed no statistically significant relationship between generative design reasoning and divergent thinking. However, an analysis of variance found a significant difference in generative design reasoning and convergent thinking between groups with moderate GD reasoning and low GD reasoning. This study shows that new computational tools might present the same challenges to beginning designers as conventional tools. Instructors should be aware of informed design practices and encourage students to grow into informed designers by introducing them to new technology, such as generative design. 

During the design process, designers must satisfy customer needs while adequately developing engineering objectives. Among these engineering objectives, human considerations such as user interactions, safety, and comfort are indispensable during the design process. Nevertheless, traditional design engineering methodologies have significant limitations incorporating and understanding physical user interactions during early design phases. For example, Human Factors methods use checklists and guidelines applied to virtual or physical prototypes at later design stages to evaluate the concept. As a result, designers struggle to identify design deficiencies and potential failure modes caused by user-system interactions without relying on the use of detailed and costly prototypes. The Function-Human Error Design Method (FHEDM) is a novel approach to assess physical interactions during the early design stage using a functional basis approach. By applying FHEDM, designers can identify user interactions required to complete the functions of the system and to distinguish failure modes associated with such interactions, by establishing user-system associations using the information of the functional model. In this paper, we explore the use of data mining techniques to develop relationships between component, functions, flows and user interactions. We extract design information about components, functions, flows, and user interactions from a set of distinct coffee makers found in the Design Repository to build associations rules. Later, using a functional model of an electric kettle, we compared the functions, flows, and user interactions associations generated from data mining against the associations created by the authors, using the FHEDM. The results show notable similarities between the associations built from data mining and the FHEDM. We are suggesting that design information from a rich dataset can be used to extract association rules between functions, flows, components, and user interactions. This work will contribute to the design community by automating the identification of user interactions from a functional model.