Towards Scenario-based Safety Validation for Autonomous Trains with Deep
  Generative Models
Thomas Decker Ananta R. Bhattarai Michael Lebacher
Modern AI techniques open up ever-increasing possibilities for autonomous
vehicles, but how to appropriately verify the reliability of such systems
remains unclear. A common approach is to conduct safety validation based on a
predefined Operational Design Domain (ODD) describing specific conditions under
which a system under test is required to operate properly. However, collecting
sufficient realistic test cases to ensure comprehensive ODD coverage is
challenging. In this paper, we report our practical experiences regarding the
utility of data simulation with deep generative models for scenario-based ODD
validation. We consider the specific use case of a camera-based rail-scene
segmentation system designed to support autonomous train operation. We
demonstrate the capabilities of semantically editing railway scenes with deep
generative models to make a limited amount of test data more representative. We
also show how our approach helps to analyze the degree to which a system
complies with typical ODD requirements. Specifically, we focus on evaluating
proper operation under different lighting and weather conditions as well as
while transitioning between them.