Applications for development and engineering
Generative artificial intelligence opens up new possibilities for development and engineering in the automotive industry. In this concise blended-learning workshop, you will gain a practical overview of current areas of application, learn how to use modern AI tools safely within the regulated automotive environment, and gain initial hands-on experience with synthetic data generation and prompt engineering.
Learning outcomes and content
Preparation (self-study):
- Introductory tutorial video (approx. 60–90 mins): How generative AI works (LLMs, diffusion models, multimodal models), the 2026 tool landscape (open source vs. commercial), gold standards for AI deployment (data classification, prompt engineering, output validation, documentation, compliance/EU AI Act, reproducibility)
Live morning session (15 September 2026):
- First session: questions on the tutorial video, data classification using automotive examples (handling confidential development and customer data)
- Automotive GenAI Landscape 2026: seven main application areas – synthetic data for perception, design & concept generation, manufacturing quality control, cockpit assistants, predictive maintenance, technical documentation, code generation
- Deep dive into synthetic data generation: edge case problem, diffusion models vs. GANs, sim-to-real gap; Live demo with Flux/ComfyUI (road scene in five weather/lighting variants with a fixed seed)
- Gold Standards for Automotive AI: ISO 26262 + SOTIF, Tool Qualification (TCL), Data Provenance, Coverage Testing
- Hands-on: creating your own prompts for scene variations in two groups (weather / time-of-day lighting)
Target group and prerequisites
The workshop is aimed at specialists and managers at automotive suppliers – particularly those in development, engineering, quality assurance and testing – who wish to evaluate and utilise generative AI for tasks such as synthetic data generation, technical documentation or software development. The workshop is also specifically aimed at staff with no prior experience of AI; the basics will be covered in advance via a tutorial video. Participation is free of charge for automotive suppliers through the AuToS SW-BW project.
Prerequisites
Admission to a face-to-face course generally requires a university degree or vocational training in a relevant profession, as well as at least one year’s professional experience.
Format
Qualification
Lecturers
External link opens in a new window:Dimitri Schultheis, Dipl.-Ing.
Member of Projekt AuToS academic staff
Registration
Please register by email at: Email application is started:hfu-akademie[at]hs-furtwangen.de
When registering, please provide your full name and date of birth so that we have all the information needed to issue your certificate of participation.
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