GenSUMO: GenAI-Creation of Critical Scenarios for Autonomous Vehicle Testing

Recent advancements in autonomous vehicle re- search highlight the importance of Machine Learning (ML) models in tasks like motion planning, trajectory prediction, and emergency management. To support AI development, we propose a novel approach for generating on-demand datasets using the Simulator of Urban Mobility (SUMO) and a Generative Adversarial Network (GAN). Our method focuses on capturing critical events such as sudden pedestrian crossings, near-misses, and collisions, providing essential data to improve vehicle models’ responses to emergency situations.