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Highly automated driving is developed on computers and in simulations, but it must prove itself on the road and on rail. Using minimum viable products and real test vehicles, CONTROL brings new methods into practical testing at an early stage. This makes it possible to validate the handling of uncertainties under real‑world conditions and provides valuable feedback for further development. Especially for the complex software‑defined vehicle, this step is crucial: only real‑world testing builds trust and accelerates innovation.
Dr. Sanwardhini Pantawane
Project Co‑Coordinator, Valeo Schalter und Sensoren GmbH
RESEARCH MEETS PRACTICE
With CONTROL, the developed tools are put to use in practice, enabling direct transfer from research to real‑world application.
► The approaches developed in CONTROL are applied in real‑world use cases on both road and rail. The entire perception chain, from sensing to data processing to evaluation, is considered as an integrated system. Influences such as weather, environment, or technical disturbances are modelled and accounted for through adaptive methods.
► The metrics, models, and tools emerging from the project, including abstraction and uncertainty models, support the development, assessment, and validation of highly automated systems. They provide the foundation for a cross‑industry safety argumentation framework and are tested in various environments: in computer‑based simulations, in laboratory setups such as test rigs, and in demonstrators and test vehicles that replicate real‑world operating scenarios.
► Through robust systems, accelerated innovation, and practical safety, CONTROL delivers direct benefits for industry, research, and society.
RESEARCH MEETS PRACTICE
With CONTROL, the developed tools are put to use in practice, enabling direct transfer from research to real‑world application.
► The approaches developed in CONTROL are applied in real‑world use cases on both road and rail. The entire perception chain, from sensing to data processing to evaluation, is considered as an integrated system. Influences such as weather, environment, or technical disturbances are modelled and accounted for through adaptive methods.
► The metrics, models, and tools emerging from the project, including abstraction and uncertainty models, support the development, assessment, and validation of highly automated systems. They provide the foundation for a cross‑industry safety argumentation framework and are tested in various environments: in computer‑based simulations, in laboratory setups such as test rigs, and in demonstrators and test vehicles that replicate real‑world operating scenarios.
► Through robust systems, accelerated innovation, and practical safety, CONTROL delivers direct benefits for industry, research, and society.
Minimum Viable Product (MVP)
CONTROL follows an iterative approach using a Minimum Viable Product. This MVP represents the first functional version of the demonstrators and integrates the core methods from all subprojects. It enables early testing of results, gathering feedback, and further developing the concepts under real‑world conditions. This allows the project to respond quickly to new requirements and ensures that the solutions developed for the safety assurance of automated systems in open environments are practical and robust.
Demonstrators: Simulations and Vehicles
Validation of the CONTROL methods takes place using both real and virtual demonstrators for road and rail. These include hybrid test rigs with miniature vehicles, hardware‑in‑the‑loop tests, and simulation‑based scenarios. In addition, test vehicles and rail platforms are used to evaluate the developed approaches under real‑world conditions. These demonstrators show how uncertainties in the perception chain are measured, assessed, and integrated into safety argumentations, an essential step toward the safe operation of automated systems.