  A modification of the Random Forest algorithm for the categorization of
traffic situations is introduced in this paper. The procedure yields an
unsupervised machine learning method. The algorithm generates a proximity
matrix which contains a similarity measure. This matrix is then reordered with
hierarchical clustering to achieve a graphically interpretable representation.
It is shown how the resulting proximity matrix can be visually interpreted and
how the variation of the methods' metaparameter reveals different insights into
the data. The proposed method is able to cluster data from any data source. To
demonstrate the methods' potential, multiple features derived from a traffic
simulation are used in this paper.
  The knowledge of traffic scenario clusters is crucial to accelerate the
validation process. The clue of the method is that scenario templates can be
generated automatically from actual traffic situations. These templates can be
employed in all stages of the development process. The results prove that the
procedure is well suited for an automatic categorization of traffic scenarios.
Diverse other applications can benefit from this work.
