  Current validation methods often rely on recorded data and basic functional
checks, which may not be sufficient to encompass the scenarios an autonomous
vehicle might encounter. In addition, there is a growing need for complex
scenarios with changing vehicle interactions for comprehensive validation. This
work introduces a novel synchronous multi-agent simulation framework for
autonomous vehicles in interactive scenarios. Our approach creates an
interactive scenario and incorporates publicly available edge-case scenarios
wherein simulated vehicles are replaced by agents navigating to predefined
destinations. We provide a platform that enables the integration of different
autonomous driving planning methodologies and includes a set of evaluation
metrics to assess autonomous driving behavior. Our study explores different
planning setups and adjusts simulation complexity to test the framework's
adaptability and performance. Results highlight the critical role of simulating
vehicle interactions to enhance autonomous driving systems. Our setup offers
unique insights for developing advanced algorithms for complex driving tasks to
accelerate future investigations and developments in this field. The
multi-agent simulation framework is available as open-source software:
https://github.com/TUM-AVS/Frenetix-Motion-Planner
