  A novel unsupervised outlier score, which can be embedded into graph based
dimensionality reduction techniques, is presented in this work. The score uses
the directed nearest neighbor graphs of those techniques. Hence, the same
measure of similarity that is used to project the data into lower dimensions,
is also utilized to determine the outlier score. The outlier score is realized
through a weighted normalized entropy of the similarities. This score is
applied to road infrastructure images. The aim is to identify newly observed
infrastructures given a pre-collected base dataset. Detecting unknown scenarios
is a key for accelerated validation of autonomous vehicles. The results show
the high potential of the proposed technique. To validate the generalization
capabilities of the outlier score, it is additionally applied to various real
world datasets. The overall average performance in identifying outliers using
the proposed methods is higher compared to state-of-the-art methods. In order
to generate the infrastructure images, an openDRIVE parsing and plotting tool
for Matlab is developed as part of this work. This tool and the implementation
of the entropy based outlier score in combination with Uniform Manifold
Approximation and Projection are made publicly available.
