FATThe rise of driver assistance systems has paved the way for significant advancements in automated driving, reshaping future mobility and transportation. However, even with SAE L2 and L3 systems, human involvement remains crucial for ensuring safety. Maintaining adequate situation awareness is pivotal, yet challenging, especially in safety-critical scenarios. To address this, our project aimed to develop and validate an algorithm for dynamically assessing situation awareness in the context of SAE L2 and L3 automation. This involved modeling cognitive processes and observable correlates to infer and evaluate the driver's real-time situation awareness status, essential for designing adaptive driver-vehicle interactions. Our approach utilized dynamic Bayesian networks (DBNs) to capture the complexities of situation awareness, allowing for real-time assessment and validation in experimental studies conducted at the University of Ulm's Human Factors department's driving simulator.