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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 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.