In order to be able to reliably safeguard autonomous or semi-autonomous vehicles, many thousands of scenarios are needed that are as realistic as possible and also include rare events. The manual creation of so-called "rare events" in special editors is enormously time-consuming. With a service for scenario generation, dSpace now wants to transfer the complexity of the real world to the simulation and thus enable the safeguarding with critical simulation scenarios.
The Scenario Generation Service from understand.ai and dSpace draws on existing data sets recorded during measurement runs. In a highly automated process, understand.ai's AI-based annotation solutions extract the relevant information from the raw data of the vehicle sensors. In this way, realistic and consistent simulation scenarios are created. Optionally, data from object lists can be used for scenario generation.
