Risk Management
CARIAD needed a faster and safer way to validate automated driving functions across large numbers of traffic, weather, and road scenarios, because proving reliability, functional correctness, and regulatory readiness for automated vehicles is difficult with manual or fragmented testing approaches.
Reported impact
CARIAD made automated-driving validation faster and more collaborative, improved confidence that automated systems behave safely across diverse scenarios, and supported more efficient regulatory-approval work for Volkswagen Group automated driving programs.
CARIAD and DXC built a verification and validation framework to industrialize testing for Volkswagen Group automated driving functions. The framework creates realistic virtual driving scenarios so engineering teams can test how automated systems behave under varied road, traffic, and weather conditions before vehicles reach the road. A domain-specific language called stiEF gives users a natural-language editor for describing scenarios such as stopping at a red light or reacting to sudden lane changes, and the editor translates those descriptions into visual simulations. This lets development and test teams collaborate on the same scenario definitions instead of working through separate specialist workflows. By turning scenario creation and execution into a repeatable digital process, the platform helps CARIAD validate automated driving behavior faster, improve safety confidence, and make regulatory approval work more efficient.
Engineers describe driving situations in the stiEF editor using natural language, and the editor converts those inputs into visual simulations. The verification and validation framework then runs the scenarios against automated driving functions under different traffic, weather, and road conditions so CARIAD teams can review safety and functional behavior before release.
"The new framework makes software validation faster and more collaborative, enabling the CARIAD teams to iterate quickly and improve system reliability."
"The testing framework uses a domain specific language called stiEF that allows users to specify driving scenarios in natural language through an editor."
"These innovations include a verification and validation framework that makes it easy for engineers to create realistic, virtual scenarios that test how automated systems respond in real-time."
"This editor translates natural language inputs such as 'stopping at a red light' or 'reacting to sudden lane changes' into visual simulations."
"This kind of testing is critical to ensure the technology behaves as expected — not only to meet stringent regulatory standards but also gain end-user trust."
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Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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