Over the last years, TNO has developed a methodology, StreetWise, to build and maintain a real-world scenario database, suitable for testing and validation of CAD systems. Currently, StreetWise provides a solution on how to identify, parameterize and characterize scenarios from object-level in-vehicle sensor data provided from a fleet of vehicles driving on public roads.
In this TKI project, StreetWise+, TNO extends this method to identify and characterize scenarios using roadside-based camera systems. Moreover, TNO will integrate the effects of communication signals in the scenario definition, to accommodate assessment of communication-based automated systems, such as C-ACC (platooning), C-AEB (communication enhanced autonomous emergency braking), or Intelligent Speed Adaptation (ISA) functionality. Furthermore, TNO will extend the scenario models to allow multiple interacting actors to be part of the description of complex scenarios.
Siemens DISW will use Simcenter Prescan and HEEDS to run a large variety of virtual tests resulting from the StreetWise methodology. The aim is to demonstrate virtual testing and safety assessment for the use case of a combined function of Advanced Cruise Control (ACC) and Intelligent Speed Assist (ISA) to relevant stakeholders.
Itility will ensure efficient cloud-based data processing, and will develop a data stream quality check using their expertise in anomaly detection methods. Working closely together with TNO and Siemens DISW data scientists and software engineers, Itility will support the development of the statistical driver models and relevant APIs.
As the research proposed in this project has a good match with the HTSM Automotive Roadmap, the project received TKI funding. The project will run until December 2022.
Back to overview