MA: Fine-Tuning an LLM Agent for Automated OpenSCENARIO Generation and CARLA Execution

13.02.2026, Abschlussarbeiten, Bachelor- und Masterarbeiten

Motivation
Scenario-based testing is essential for validating autonomous driving and ADAS functions. OpenSCENARIO enables standardized, reproducible, and shareable scenario definitions, and CARLA provides a scalable simulation environment for execution.
However, authoring diverse OpenSCENARIO scenarios manually is time-consuming, error-prone, and hard to scale. Recent advances in LLM agents and domain fine-tuning make it feasible to generate structured artifacts (e.g., XML) automatically — but robustness is still challenging: outputs must be schema-valid, semantically consistent, and executable in CARLA.

This thesis investigates a learning-based generation pipeline: fine-tune an LLM agent (and optionally combine it with retrieval and validation tools) to reliably generate novel, valid, and executable OpenSCENARIO scenarios at scale.

Goal
Develop an end-to-end toolchain to:
>>Build or curate a dataset of OpenSCENARIO scenarios (templates + variants + execution outcomes)
>>Fine-tune an LLM (or instruction-tune a smaller model) to generate valid .xosc scenarios from structured prompts
>>Integrate automatic XML/schema validation + rule checks + CARLA execution feedback
>>Export a scenario suite with metadata and quality metrics

Expected Deliverables
>>LLM-based scenario generation agent (with inference script)
>>Fine-tuning dataset (or dataset generation pipeline) + training config
>>OpenSCENARIO scenario library (templates + generated scenarios)
>>CARLA execution runner + logging and evaluation reports

We Offer
>>A dynamic and future-oriented research environment
>>Hands-on experience with a state-of-the-art software stack for autonomous driving (CommonRoad, CARLA)
>>Opportunity to publish a scientific paper (based on merit)
>>The thesis can be written in either English or German

Required Skills
>>Python, Git, basic software engineering
>>Interest in LLMs (fine-tuning, prompting, evaluation)
>>Familiarity with CARLA/OpenSCENARIO helpful but not mandatory

Start Date
Work can begin immediately.

Work can begin immediately. If you are interested in this topic, please first have a look at our recent survey paper: https://ieeexplore.ieee.org/document/11370877
Then send a brief cover letter explaining why you are fascinated by this subject, along with a current transcript of records and your CV to: yuan_avs.gao@tum.de

Kontakt: yuan_avs.gao@tum.de