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

Institut

Professur für autonome Fahrzeugsysteme (TUM-ED)

Typ

Semesterarbeit / Masterarbeit /

Inhalt

experimentell / theoretisch /  

Beschreibung

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.

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Goal

Develop an end-to-end toolchain to:

1. Build or curate a dataset of OpenSCENARIO scenarios (templates + variants + execution outcomes)

2. Fine-tune an LLM (or instruction-tune a smaller model) to generate valid .xosc scenarios from structured prompts

3. Integrate automatic XML/schema validation + rule checks + CARLA execution feedback

4. 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

 

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.
Please send me an email along with a current transcript of records and your resume, to: yuan_avs.gao@tum.de

 

Tags

AVS Gao

Möglicher Beginn

sofort

Kontakt

Yuan Gao
yuan_avs.gaotum.de