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Description: This is supplemental material for the paper "Computational Models for In-Vehicle User Interface Design: A Systematic Literature Review". In this review, we analyze the current state of the art of computational models for in-vehicle User Interface (UI) design. Driver distraction, often caused by drivers performing Non Driving Related Tasks (NDRTs), is a major contributor to vehicle crashes. Accordingly, in-vehicle UIs must be evaluated for their distraction potential. Computational models are a promising solution to automate this evaluation, but are not yet widely used, limiting their real-world impact. We systematically review the existing literature on computational models for NDRTs to analyze why current approaches have not yet found their way into practice. We found that while many models are intended for UI evaluation, they focus on small and isolated phenomena that are disconnected from the needs of automotive UI designers. In addition, very few approaches make predictions detailed enough to inform current design processes. Our analysis of the state of the art, the identified research gaps, and the formulated research potentials can guide researchers and practitioners toward computational models that improve the automotive UI design process. Martin Lorenz, Tiago Amorim, Debargha Dey, Mersedeh Sadegi, and Patrick Ebel. 2024. 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI '24), September 22-25, 2024, Stanford, CA, USA. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3640792.3675735