Quo-Vadis Multi-Agent Automotive Research? Insights from a Participatory Workshop and Questionnaire

TL;DR

This paper explores the current state and challenges of multi-agent research in automotive environments, emphasizing the need for interdisciplinary approaches, improved tools, and realistic simulations to advance human-centered automotive research.

Contribution

It provides empirical insights from a workshop and questionnaire, identifying barriers and opportunities in multi-agent automotive research and proposing directions for future development.

Findings

Multi-agent approaches are valued but face practical barriers.

Interdisciplinary methods are needed for progress.

Better tools and simulation environments are essential.

Abstract

The transition to mixed-traffic environments that involve automated vehicles, manually operated vehicles, and vulnerable road users presents new challenges for human-centered automotive research. 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 AutomotiveUI '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 findings 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 interdisciplinary methods, better tools, and simulation environments that support scalable, realistic, and ethically informed multi-agent research.