VALERIE22 -- A photorealistic, richly metadata annotated dataset of urban environments
This provides a tool for researchers and developers in automated driving to better understand and test DNN performance, though it is incremental as it builds on existing synthetic data methods.
The authors tackled the need for validating deep neural networks in pedestrian detection for automated driving by creating VALERIE22, a photorealistic synthetic dataset with rich metadata, and demonstrated it as one of the best-performing synthetic datasets available.
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.