  The VALERIE tool pipeline is a synthetic data generator developed with the
goal to contribute to the understanding of domain-specific factors that
influence perception performance of DNNs (deep neural networks). This work was
carried out under the German research project KI Absicherung in order to
develop a methodology for the validation of DNNs in the context of pedestrian
detection in urban environments for automated driving. The VALERIE22 dataset
was generated with the VALERIE procedural tools pipeline providing a
photorealistic sensor simulation rendered from automatically synthesized
scenes. The dataset provides a uniquely rich set of metadata, allowing
extraction of specific scene and semantic features (like pixel-accurate
occlusion rates, positions in the scene and distance + angle to the camera).
This enables a multitude of possible tests on the data and we hope to stimulate
research on understanding performance of DNNs. Based on performance metric a
comparison with several other publicly available datasets is provided,
demonstrating that VALERIE22 is one of best performing synthetic datasets
currently available in the open domain.
