Evaluating rare but critical driving situations, such as hazardous road events or complex traffic interactions, is challenging yet essential. Physical tests for every possible scenario are expensive, difficult to manage reliably, and potentially dangerous. Consequently, virtual simulations have become fundamental resources for research in autonomous driving, traffic modeling, and safety analysis. Researchers take advantage of these environments to replicate diverse driving scenarios. Sensor responses allow them to analyze vehicle behavior in a controlled and highly adaptable setting. Among the virtual simulation tools, CARLA (Car Learning to Act) stands out as an open-source driving simulator widely used for autonomous vehicle research and other multidisciplinary studies.