Generative AI for Testing of Autonomous Driving Systems: A Survey

Autonomous driving systems (ADS) have been an active area of research, with the potential to deliver significant benefits to society. However, before large-scale deployment on public roads, extensive testing is necessary to validate their functionality and safety under diverse driving conditions. Therefore, different testing approaches are required, and achieving effective and efficient testing of ADS remains an open challenge. Recently, generative AI has emerged as a powerful tool across many domains and is increasingly applied to ADS testing due to its ability to interpret context, reason about complex tasks, and generate diverse outputs. To understand its role in ADS testing, we systematically analyzed 92 relevant studies and synthesized their findings into six major application categories, primarily centered on scenario-based testing. We further evaluated the effectiveness of these approaches and compiled a comprehensive set of resources, including 37 datasets, 17 simulators, 61 ADS systems, 141 metrics, and 100 benchmarks used for evaluation. In addition, we identified 27 limitations in existing research and outlined six major directions for future work. This survey provides an overview and practical insights into the use of generative AI for testing ADS, highlights existing challenges, and outlines future research opportunities in this rapidly evolving field.