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The transition to mixed-tra!c environments that involve auto- mated vehicles, manually operated vehicles, and vulnerable road users presents new challenges for human-centered automotive re- search. Despite this, most studies in the domain focus on single- agent interactions. This paper reports on a participatory workshop (N = 15) and a questionnaire (N = 19) conducted during the Automo- tiveUI ’24 conference to explore the state of multi-agent automotive research. The participants discussed methodological challenges and opportunities in real-world settings, simulations, and computational modeling. Key "ndings reveal that while the value of multi-agent approaches is widely recognized, practical and technical barriers hinder their implementation. The study highlights the need for in- terdisciplinary methods, better tools, and simulation environments that support scalable, realistic, and ethically informed multi-agent research. 

In this review, we analyze the current state of the art of compu- tational models for in-vehicle User Interface (UI) design. Driver distraction, often caused by drivers performing Non Driving Re- lated 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 au- tomate 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 ap- proaches 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 pro- cesses. 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. 

In the future, roads will host a complex mix of automated and manually operated vehicles, along with vulnerable road users. However, most automotive user interfaces and human factors research focus on single-agent studies, where one human interacts with one vehicle. Only a few studies incorporate multi-agent setups. This workshop aims to (1) examine the current state of multi-agent research in the automotive domain, (2) serve as a platform for discussion toward more realistic multi-agent setups, and (3) discuss methods and practices to conduct such multi-agent research. The goal is to synthesize the insights from the AutoUI community, creating the foundation for advancing multi-agent traffic interaction research.