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Advanced Driver Assistance Systems (ADAS) require an understanding of complex traffic situations. Such complexity can be handled by decomposing traffic situations into analyzeable subsets called Situation Aspects (SA). Since lots of situation analyzing problems result in classification tasks, the Scenario Based Random Forest (SBRF) algorithm is introduced into the field of ADAS situation analysis research. This classification method is designed to handle feature sets that develop over time and classification results that can be judged only by using complete scenarios instead of single time snap-shots. Furthermore, it has the advantage of using the out of bag (oob) estimation technique in order to perform feature selection. The problem of detecting a convoy merging traffic situation in real traffic scenarios serves as example to show the process of situation aspect modelling, feature selection and classification using the above mentioned methodology. It is demonstrated how the challenge of labelling changing SA can be solved using an undefined transition class and how this effects classification results. Because unbalanced data sets often occur in ADAS situation analysis, results on over- and downsampling strategies are described as well.