Women in Data Science Puget Sound 2024

There is a need for real-world driving data to understand how people drive in complex traffic situations. This presents a significant challenge to improving driver and road safety while developing more trustworthy vehicle automation technology. Therefore, this study addresses this gap by creating models that generate realistic driving scenarios to deepen understanding human driver decision-making using limited datasets. The models used in this research are based on explainable generative artificial intelligence, a combination of Generative Adversarial Networks (GANs) and explainable AI (xAI). This approach improves transparency and trustworthiness in understanding how the models operate. The goal is to simulate typical, rare, and critical driving scenarios, capturing a wide range of driver actions under various traffic conditions.