OSG-Q3 — Robust OpenSCENARIO Generation with LLMs

The idea

OpenSCENARIO represents complex driving situations in a strict, machine-readable format. Generating it with an LLM is not only a language task: the result also has to respect structure, scenario logic, and the expectations of downstream tools.

OSG-Q3 investigates how more deliberate generation strategies can make that process more robust.

Case study structure

This page is ready to become the complete project story. The final version should answer four questions:

1. What was unreliable about the previous or baseline generation approach?
2. Which strategies were introduced, and why were they selected?
3. How were generated scenarios validated?
4. What improved, and what limitations remain?

My contribution

Content to add: your role, the parts of the system you owned, collaborators, project dates, and the most important technical decision you made.

Approach

Content to add: a concise architecture or sequence diagram, model and prompt strategy, validation loop, OpenSCENARIO constraints, and one representative example from request to generated scenario.

Outcome

The public project page and an AEB scenario demo are available through the links above.

Content to add: benchmark design, success criteria, quantitative results, comparison with baselines, and the official publication or repository if one is available.

Reflection

Content to add: what worked unexpectedly well, where the LLM remained unreliable, and the next experiment you would run.