This technical analysis examines the implementation of Generative Artificial Intelligence (AI) in creating synthetic training data for autonomous vehicles (AVs), with a particular focus on adverse weather conditions. The article explores how generative models address the critical challenge of data scarcity in autonomous driving systems by synthesizing realistic training scenarios. The article evaluates various aspects including sensor fusion architectures, data validation frameworks, and performance optimization techniques. The analysis demonstrates the effectiveness of synthetic data generation in enhancing perception, decision-making, and sensor fusion capabilities while significantly reducing development cycles and data collection costs. The article indicates substantial improvements in model generalization, environmental condition simulation, and safety validation accuracy through the integration of synthetic data approaches.
Keywords: adverse weather conditions, autonomous vehicles, generative AI, sensor fusion, synthetic data generation
