Situation aware applications rely on high level situations as context to adapt their behavior at runtime. Situations can be detected by aggregating and processing data and information from various sources. Just like its underlying data, situation information is uncertain and differs in quality. This thesis presents generalistic methods that can be used to approximate the quality of detected situations. Its main contributions are a standalone fleet telematics simulation project as well as extensions to the SitOPT project which were created to implement the presented quality concept. The implementation has been tested with a simulated, non-trivial fleet telematics scenario to evaluate selected methods for their effectiveness. It has been shown that forming a weighted average from quality scores of multiple context information yields good results to quantify the quality of a detected situation. This simple assessment strategy can be improved by altering the quality of context scores in preprocessing steps.