In this insightful presentation, neurocat will delve into the innovative approach of augmented data generation, a method distinct from traditional synthetic data approaches and simulation-based techniques, offering enhanced fidelity and relevance for autonomous driving (AD) development. Addressing the critical challenge of creating highly realistic datasets, especially under adverse weather conditions like rain, fog, and snow, neurocat introduces novel augmentation techniques designed to improve the robustness of perception functions in AD systems. Through empirical evidence and collaborations with industry-leading teams, the session will highlight the significant benefits of using augmented data for training and validating perception functions, particularly in handling edge cases.