Test and training data generation for object recognition in the railway domain

AI development and test is a data driven endeavor. To date it is magnitudes more laborious to collect and annotate training and test data, than to provide a problem matching architecture and train it. In the KI-LOK project, a case study seeks to validate an object recognition system to prevent potentially fatal behavior of autonomous train operations. To accommodate for the vast amount of possible scenarios a train could encounter during operation, we propose a tool chain to automatically generate labeled synthetic images and videos. We start from an ontology of elements such as: Tracks, houses, vehicles or signals, these elements are then sampled and modeled in 3D to represent a scenario. Since the objects and locations of the elements in a scenario are known, no manual annotation or labeling of the data is required. By sampling from an ontology it will be possible to build comprehensive and balanced datasets of scenarios to train and test AI, while adding the benefit of corner case generation by reducing to certain elements in the ontology. This article reports on the current status of the project and the goals it tries to achieve.