SA/MA: Diffusion-based safety-critical scenario generation

13.02.2026, Abschlussarbeiten, Bachelor- und Masterarbeiten

Motivation
Safety-critical scenario generation is a cornerstone of validating autonomous driving and ADAS systems. Real-world datasets such as nuScenes capture valuable long-tail events, but they are inherently limited in coverage, diversity, and controllability. At the same time, classical simulation scenarios often lack photorealism, limiting their usefulness for perception-centric evaluation.

This thesis explores diffusion-based methods to generate and transform safety-critical driving scenarios, bridging the gap between simulation, real-world data, and perception-level validation.

Goal
Develop an end-to-end pipeline to generate safety-critical driving scenarios using diffusion models, focusing on both dataset editing and photorealistic synthesis.
Specifically, the thesis aims to:
>>Generate new safety-critical scenarios by editing existing real-world datasets (e.g., nuScenes)
>>Transform abstract or synthetic scenarios into photorealistic sensor representations
>>Enable controllable generation based on risk-related constraints (e.g., proximity, collision likelihood, agent interactions)

Expected Deliverables
>>Diffusion-based scenario generation / editing pipeline
>>Safety-critical scenario dataset (edited real-world + synthesized scenarios)
>>Training and inference scripts with clear documentation
>>Evaluation report on realism, diversity, and safety relevance

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

Required Skills
>>Excellent English or German proficiency.
>>Strong python skills; familiarity with Pytorch and basic computer-vision/ML.
>>Interest in generative models (diffusion models, conditioning, evaluation)
>>Familiarity with autonomous driving datasets (nuScenes) and simulator (CARLA) is helpful but not mandatory

Start
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