Creation of digital models for accelerated and reliable testing of automated systems in adverse weather

Automated systems are becoming widespread in many fields, e.g. transportation, exploration, defence, rescue, etc. These systems need to build a comprehensive and robust situational awareness, detailed in terms of spatial and temporal resolution. This situational awareness is based on the data provided by a suite of perception sensors (e.g. camera, LiDAR, RADAR, etc.). Due to internal and external noise factors, the quality of the sensor data can be heavily compromised. <br/> It is impossible to test the systems and the sensor suite under all possible environmental conditions and safety critical cases. To tackle the testing complexity and speed up the testing procedures, digital twins and models of the systems and test environments are needed to enable accelerated and thorough testing in virtual and/or mixed environments under a wide variety of non-ideal conditions. In order to use virtual/mixed testing to properly assess system performance and its safety, the simulation-to-reality gap needs to be reduced as much as possible, using high-fidelity digital models in combination with validated sensor noise models to reproduce accurately the data that real sensors would produce. <br/> This work discusses the development and validation of two high-fidelity digital models of one outdoor and one indoor testing facilities, offering rain and fog emulation on site. By the usage of high-resolution and geo-referenced point clouds and images combined with photogrammetry and 3D modelling, a semi-automatic 3D reconstruction and material creation process is presented. The created digital models, combined with real perception sensors data collection and the development of sensor noise models, enable the validation of these models and the production of trustworthy and realistic virtual sensor data. In turn, this data allows numerous and safety critical tests to be executed reliably. <br/> The hereby described digital models have been developed as a part of the EU Horizon ROADVIEW project∗ and will be made openly available.