The development of driving functions for advanced driver assistance systems and automated/autonomous driving relies on the interaction between the driving function and its environment. While driving function objectives can be well-defined, systematic descriptions of relevant scenarios are often lacking. A comprehensive scenario catalog remains challenging due to uncertainties in identifying critical environmental elements. Sensor modeling is crucial for driving function validation, as sensor reliability directly impacts vehicle homologation. Realistic sensor behaviour models enable closed-loop simulations, integrating driving functions, sensor dynamics, and known scenarios. This approach accelerates validation and enhances system understanding. This study presents an approach to accurately model both sensor impairments and the vehicle environment for localization with a camera sensor. Camera behaviour models are implemented within the software library sensor models, with parameters derived from datasheets or precise test setups. The environment is represented by a digital twin of the city of Kempten, independent of specific vehicle applications. Scenarios are modeled and then simulated with the Python-based software library osi3test in a fixed time-step loop, visualized via software library panda3d. Standardized interfaces such as ASAM OSI, FMI and OGS standards facilitate interoperability and deeper insights into model interactions that indicate real interactions worth investigating.