General guidance for drafting the baseline document on artificial intelligence in the automotive sector. The document emphasizes focusing revision on green text in AI-11-03, using change requests for substantive modifications processed during IWG plenary sessions, and integrating successful requests into a single master document. Four sections are addressed: Introduction (AAPC lead) to clarify purpose and scope; AI Uses & Benefits (OICA/CLEPA lead) to provide intuitive understanding and define terms; Risks (modifications by change request); and Risk Management (China and EME leads) to clarify operationalization in certification regimes while maintaining agreement-neutral, conceptual language at reference document level, avoiding prescriptive guidance.

The session covers adoption of the agenda and brief report of the 11th session, discussion of work plan for formal document submission to the June 2027 WP.29 session, discussion of draft reference document AI-11-03 including modifications and change requests to sections 1 through 4 and annexes, presentations on the use of AI in the automotive sector, discussion of timeline and future sessions, and small drafting group discussions on 28 October.

Report of the 11th session of the IWG on AI held on 2 September 2026 online. The session discussed the status of the draft reference document, with the USA formally objecting to the refresh plan (AI-11-03, AI-10-04) and insisting that AI-09-06/Rev.3 presented to WP.29 was the only valid baseline document. The co-chairs and majority of experts, including the UK, Germany, France, Canada, the Netherlands, EU Commission, OICA/CLEPA, and SAE, supported using AI-11-03 as the baseline. The group decided to establish two small drafting groups to address green text in sections 2 and 4, with changes submitted through change request forms for review during plenary sessions.

Study on image generation method to support evaluation of automated driving systems. Fine-tuned an image generator on the Zenseact Open Dataset using 100,000 road-scene images annotated with 3D bounding boxes. Demonstrated ability to generate plausible images reflecting scenario specifications and to create images causing detection errors in object detectors. Next steps include quantitatively evaluating validity and realism of generated images and developing methods to automatically identify scenario specifications that degrade object detector performance.