AEB systems are critical in preventing collisions, yet their effectiveness hinges on accurately estimating the distance between the vehicle and other road users, as well as understanding road conditions. Errors in distance estimation can result in premature or delayed braking and varying road conditions alter road-tire friction coefficients, affecting braking distances. The integration of advanced sensors like LiDARs has significantly enhanced distance estimation. Cameras and deep neural networks are also employed to estimate the road conditions. However, AEB systems face notable challenges in urban environments, influenced by complex scenarios and adverse weather conditions such as rain and fog. Therefore, investigating the error tolerance of these estimations is essential for the performance of AEB systems. To this end, we develop a digital twin of our test vehicle in the IPG CarMaker simulation environment, which includes realistic driving dynamics and sensor models. Our simulated test vehicle is equipped with a distance estimation algorithm and AEB system designed for eventual deployment in its real-world counterpart. We test the vehicle in various simulated test scenarios. This approach facilitates accurate measurement and adjustment of distance and road-tire friction coefficients. The testing protocol begins with the European New Car Assessment Programme (EU NCAP) AEB Car-to-Pedestrian standard. Additionally, our simulation encompasses realistic urban scenarios, featuring complex traffic conditions and diverse weather scenarios, including rain, fog, and varying road surfaces like dry, wet, snow-covered, and icy. Finally, we have determined the error tolerances for various conditions. The simulation process and results reveal that the major challenges involve creating critical scenarios, modeling environments and sensors, and constructing digital twins of test vehicles. Recommendations and insights derived from these findings are also provided.