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.

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.

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.

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.

Comments on the structure and content of the draft reference document on artificial intelligence in regulated automotive safety systems. Notes a six-month timeline for adoption by WP.29 in June 2027, with formal submissions due by March 2027. Recommends prioritizing a future-proof repository of information on potential AI-related risks and risk management that can be maintained through standard WP.29 processes. States current regulatory frameworks are sufficient to address unreasonable safety risks from AI use in vehicles. Suggests the document should include introductory explanatory text for non-specialists, clear definitions of technical terms, accessible risk summaries, and a developed risk-management section to support WP.29 regulatory activities.

Presentation proposing an AI Management System (AIMS) as a complementary organizational layer to existing safety, cybersecurity, and software update management systems. AIMS would regulate AI through organizational processes rather than technical requirements, covering data lifecycle, model development, verification and validation, change control, and operational monitoring. The system is technology-neutral, lifecycle-based, and aligned with industry standards including ISO 26262, ISO 21448, and ISO/IEC 42001, avoiding the need for product-specific AI test procedures and re-approval of model updates.

Proposal to conduct safety analysis based on vehicle and system architecture to identify and decompose data and model-related safety requirements, and delete the requirement for maintenance or reassessment of assumptions as the system or environment changes.

Proposal to add principles and stages of risk-driven lifecycle approach to para. 4.2.1.1, replacing ‘risk informed lifecycle approaches explanation’ with ‘Risk-driven lifecycle approach explanation’ and adding explicit reference to principles of risk-driven lifecycle approach and stages of risk-driven lifecycle approach.

Change request to revise the driving function name from ‘Acceleration Control Pedal Error’ to ‘Acceleration Control for Pedal Error’ in Section 1.2.4, based on UNECE guidance that the original term is not a correct driving function name.

Proposal to add illustrative real-world application examples under Automated Driving Systems in Annex 1, items 1.2.4 and 1.3, covering low-speed unmanned delivery and sanitation vehicles in closed or semi-open areas and urban non-motorized lanes, robotaxi operation on urban roads, and autonomously-operated vehicles deployed within mining sites, ports and industrial parks.

Proposal to add to illustrative practices in para. 4.2.6.3 a reference to scenario coverage assessment based on ISO 34502 and test case generation based on ISO 34505 as sources of evidence for AI system assurance, and to add ISO 34501-34505 to Annex 4 A standards list.

Proposal to add a new risk item addressing compromised or unavailable infrastructure-sourced inputs in para. 3.2.9.5, add a corresponding extended description in Annex 2 (item 6.5), add a new table row in Annex 3, and add the term ‘Roadside Perception’ to Annex 5. AI systems that rely on roadside perception, map data, or remotely-delivered data may encounter communication outages, latency, or maliciously-altered incoming data, resulting in perception gaps or erroneous decisions.

Proposal to introduce the nine data-related safety properties defined in ISO/PAS 8800:2024 (Accuracy, Completeness, Correctness, Independence of datasets, Integrity, Representativeness, Temporality, Traceability and Verifiability) as a common verification thread across rows 4.1–4.5 in Annex 3 and Section 4.2.5, add an anchoring practice for row 4.4 (Distribution Shift) requiring periodic monitoring and reassessment of data–model drift with re-evaluation of the safety argument where drift is detected, and list digital watermarking and AI-based automated audit as candidate illustrative practices.

Proposal to insert new section 4.2.10 defining human oversight and system controllability as complementary dimensions addressing the human-machine interaction layer and engineering design layer. The concept establishes that human oversight refers to the ability of natural persons to monitor, understand, intervene in, and override AI system outputs affecting safety, while system controllability refers to the ability of external agents to influence AI system behavior to prevent harm. For driving automation functions, human oversight and fallback vary with automation level as defined in SAE J3016 / ISO/SAE 22736. For non-driving safety functions, controllability may be achieved through audible or visual warnings, output degradation, or fail-safe behavior. System-level controllability measures include dedicated safety monitors, redundant model architectures, and structured fallback or fail-safe degradation strategies.

Proposal to insert an introductory paragraph before para. 4.2.1 to establish that the depth and rigor of applying common AI risk management concepts should be commensurate with the safety risk level of the specific AI application, with risk levels informed by ISO 26262 ASIL classification and ISO 21448 SOTIF risk assessment, as well as the degree of AI system autonomy and adaptability, and to elevate the proportionality principle from a sub-point within para. 4.2.1.1 to a framing principle for the entire Section 4.2.

Proposal to insert new sections 2.3.7 and 2.3.8 to clarify the classification criteria and boundaries of the three modes of AI use and provide representative examples illustrating their practical application, establishing that classification should be based on the role and purpose of AI in specific activities rather than assigning a fixed mode to a particular model or process, and that the same AI-related model, system, artefact or process may correspond to different modes at different stages or when used for different purposes.

Add one NB note beneath the “AI Safety” definition in Annex 5, Section 1.1, item 4, clarifying that the definition uses “safety” in the functional-safety sense and that hazards affecting AI-based safety functions may be initiated by malicious actions or arise from personal data processing, and that assurance of AI systems interacts with cybersecurity and data-privacy requirements such as UN R155 and ISO/SAE 21434.

Proposal to modify para. 4.2.5.2 to replace ‘management of changes in data and operational distributions’ with ‘management of changes in data and process assurance’. Where a closed-loop data chain is applied to vehicle AI systems safety development activities, evidence of safety assurance capability of the complete closed-loop data chain shall be provided, including safety assurance descriptions for data collection, data processing, and data annotation processes. Where data training is used as an improvement measure for functional insufficiency, relevant analysis shall be conducted and the rationality of the data shall be justified to ensure the improved system meets system-level safety-related requirements.

Proposal to insert a sentence in para. 2.2.1 clarifying that decision trees can be learned from data and not all are manually specified rule systems, and to modify para. 2.2.5 to state that Generative AI models can use different architectures, with some being Transformer-based and potentially foundation or general-purpose AI models.

Proposal to modify the definition of concept drift in para. 3.2.9.2 and Annex 2, para. 6.2 to clarify that concept drift arises when changes alter the relationship between inputs and target outputs, and that a change in input distribution alone does not necessarily imply a change in this relationship.

Proposal to modify the definition of Validation Data to replace ‘a final’ with ‘candidate’ and append clarification that it is used for model selection or tuning while independent test data used for final performance reporting are not used for model selection or tuning, and add a definition of Test Data as data used to evaluate the final selected model and not used for fitting, model selection or tuning, with a note on dataset separation to avoid leakage between sets.

Proposal to modify Annex 1, 1.1.2 to replace the clause beginning ‘and such applications fall within the scope’ with language clarifying that applications fall within the first mode of AI use only where AI is part of the regulated product and directly performs or supports a safety-relevant function, and to add that AI tools used solely for development or assessment fall under section 2.3.3.

Proposal to add working definitions for uncertainty quantification, aleatoric uncertainty, epistemic uncertainty, and uncertainty-aware action at the end of section 1.3 of AI-11-03, Annex 5. These definitions do not replace definitions in applicable standards and support general discussion of uncertainty and its effects on action.

Proposal to append sentences to paragraph 2 addressing uncertainty-related risks propagated from upstream to downstream AI modules and supporting uncertainty-aware action, and to paragraph 3 considering whether uncertainty information remains meaningful when used by downstream AI modules and how it affects the resulting action.

Proposal to add text to para. 3.2.4.6 clarifying that uncertainty-related risks may propagate from upstream to downstream AI modules when uncertainty information is inadequately communicated or used, affecting the resulting action, and to add text to Annex 2, item 1.6 clarifying that inadequate communication or use of uncertainty information across AI modules can propagate or amplify uncertainty-related risks and affect the resulting action, even when the upstream estimate is informative.

Proposal to add text to para. 3.2.4.6 clarifying that uncertainty-related risks may propagate from upstream to downstream AI modules when uncertainty information is inadequately communicated or used, affecting the resulting action, and to add text to Annex 2, item 1.6 clarifying that inadequate communication or use of uncertainty information across AI modules can propagate or amplify uncertainty-related risks and affect the resulting action, even when the upstream estimate is informative.

The IWG on AI reports on three meetings held since June 2026 and future meetings scheduled through January 2027. The IWG has compiled literature on regulated automotive safety systems using AI, created terms and definitions for AI, listed AI use cases for regulated automotive safety systems, and is working on an AI reference document (latest draft AI-11-03). An AI reference document is scheduled for submission as a formal working document to the June 2027 WP.29 session, based on WP.29-199-26 with structural and conceptual improvements. Small drafting groups will propose document updates via change request forms using AI-11-04, with only updates receiving majority consensus during plenary meetings incorporated into the baseline.

This document provides a draft form for submitting AI reference document change requests. Each request requires a separate submission. The form collects information including the date, agenda item, submitter affiliation, a title of twenty-five words or less, the location of the impacted section or paragraph number, original text, revised text, rationale, and discussion notes. The form includes fields for secretariat use to record whether requests are accepted, accepted as amended, deferred for further discussion, not accepted, or withdrawn.

The agenda includes discussion of the status of the draft reference document for transformation into a formal document for the June 2027 WP.29 session, discussion of timeline for formal document submission to WP.29, and upcoming meetings including sessions planned for October 26-27 in Tokyo, November 24-25 online, and January 26-28 in Washington DC.

AI IWG 12th session will be held October 26–27, 2026, at the Zennihon Truck Sogo Kaikan, 3rd Floor, Tokyo. October 26 runs 10:00–17:30 with reception at 18:30; October 27 runs 09:30–17:00. The meeting room opens 9:30 a.m. October 26 and 9:00 a.m. October 27. In-person attendance is limited to the first 50 registrants. Participants must complete the survey by September 25. JASIC staff contact information and accommodation recommendations are provided.

Document AI-11-03 illustrates a refreshed approach to the draft reference document AI-09-06/Rev.3, based on outcomes from the 10th IWG on AI session (AI-10-04). The document addresses AI uses and potential risks in regulated automotive safety systems. It identifies AI-related risks across lifecycle stages—specification, model training, data management, verification, and operation—and describes risk management practices drawn from standards including ISO 26262, ISO 21448, ISO/PAS 8800, and UN R155/R156. Annex 1 provides illustrative AI use cases in perception, planning, motion control, driver assessment, and vehicle monitoring. Annex 3 tabulates illustrative practices for managing identified risks. The document establishes foundational concepts and terminology to support manufacturers, suppliers, and regulatory authorities in addressing safety implications of AI in automotive systems.

The 10th session of the IWG on AI was held on 23 July 2026. The agenda and the brief report of the 9th session (AI-10-02/Rev.1) were adopted. The chair briefed experts on the June 2026 WP.29 session results, noting a 1-year extension of the IWG on AI mandate to prepare a formal document for submission in June 2027, and a 12-week submission deadline for working documents. The proposed schedule (AI-10-03) covering activities until the June 2027 WP.29 session was discussed with no objections. The chairs offered support for a refresh of the draft reference document (AI-10-04), addressing structural shortcomings and separation of core concepts from implementations. Discussion focused on use case location, scope of AI in regulated automotive safety systems, drafting teams, and workflow for expert input. The 12th IWG on AI session is scheduled for 26–27 October 2026 in Tokyo, Japan and online.

The 9th session of the Informal Working Group on AI convened 3–4 June 2026 in London. Experts adopted the agenda and reviewed the consolidated draft reference document AI-09-06, which was substantially revised through two iterations (AI-09-06/Rev.1 and AI-09-06/Rev.2) before final submission to WP.29 as AI-09-06/Rev.3. Key modifications included limiting use cases in section 3 to driving functions, removing section 6 and the “other use cases” section, and incorporating lifecycle risks in sections 4 and 5. The IWG received a presentation on ISO/PAS 8800/2024 AI safety standards. A 1-year mandate extension was requested to submit a formal document for the June 2027 WP.29 session.

Proposal to reorganize the draft reference document AI-09-06/Rev.3 by moving the risk-by-risk management practices table to an annex, substantively revising Section 1 to emphasize document purpose rather than drafting history, substantively revising Section 2 to explicitly define the scope of AI uses in regulated automotive safety systems and distinguish different modes of AI use, revising Section 5 to replace risk-by-risk presentation with thematic discussion of recurring concepts in international standards and practice, and optionally adding a conclusion and further detailed annexes as needed.

The Informal Working Group on Artificial Intelligence will hold its tenth session on Thursday, 23 July 2026 from 13.30 to 15.30 CET online. The agenda includes adoption of the agenda and brief report of the ninth session, discussion of results of the June WP.29 session presentation, exchange of views on timeline for formal document submission to the June 2027 WP.29 session, discussion of status of the draft reference document in view of its transformation into a formal document, and consideration of upcoming meetings. The eleventh session is planned for Thursday, 3 September 2026.

The AI IWG held two meetings since the last WP.29 session and has virtual meetings scheduled for 23 July 2026 and 3 September 2026. The IWG compiled literature on regulated automotive safety systems using AI (AI-09-04), created Terms & Definitions for AI (AI-09-05), listed AI use cases for regulated automotive safety systems (AI-09-03), and created a draft guiding questions document (AI-08-07). A working draft AI Reference Document (AI-09-06/Rev.3) exists but is not finished. The AI IWG requests an extension of its mandate to deliver the AI Reference Document as a working document no later than June 2027.

This draft reference document consolidates input from documents AI-07-06, AI-09-03, AI-09-04, AI-08-07, and AI-09-05 to fulfill the initial deliverable of the IWG on Artificial Intelligence pursuant to WP.29/1190. The document addresses AI uses and benefits in automotive, establishes AI use cases prioritizing regulated automotive safety applications, identifies potential risks across AI lifecycle stages, and presents illustrative risk management practices informed by literature review. Seven whole-system lifecycle risks are identified, including insufficient documentation and traceability, inadequate organizational governance, and process drift. Additional risks span AI and use case specification, model architecture and training, data management, verification and validation, and in-use monitoring. Risk management practices derive from standards including ISO 26262, ISO 21448, ISO 23894, and UN Regulations 155 and 156, complemented by research and policy frameworks.

This initial consolidate draft reference document for artificial intelligence in regulated automotive safety systems, based on WP.29-195-20, AI-07-08, AI-07-11, AI-07-09, AI-07-10, and AI-07-12, establishes AI use cases and identifies potential risks across the AI lifecycle. The document addresses driving functions including perception, planning and motion control, and end-to-end systems, as well as non-driving functions such as driver assessment and occupant monitoring. It presents six major AI lifecycle risk categories: whole system risks including insufficient documentation and governance; specification risks such as improper definition of intended use; model and training risks including blackbox behaviour and lack of robustness; data management risks such as data poisoning and distribution shift; verification and validation risks including insufficient test coverage; and operational risks including concept drift and limited failure detection. Illustrative risk management practices drawn from standards, research, and regulations are provided for each identified risk to support manufacturers, suppliers, and approval authorities.

AI-Enabled Road Vehicles Managing a Complex Standards Landscape presents the current landscape of standards and regulations for AI in road vehicles, structured overview of core AI governance, engineering, organisational and data governance standards required for EU AI Act conformity and UNECE compliance, and identifies gaps and challenges in alignment. ISO 8800 (2029) Road vehicles Safety of Artificial Intelligence is proposed as a common framework providing navigation and connection across horizontal AI standards with automotive sector-specific needs, reducing fragmentation and enabling transparent, traceable evidence-based assurance for regulators, industry and society while supporting compliance with EU AI Act and future UNECE requirements.

The USA expert submitted initial red-line comments on the consolidated reference document for artificial intelligence in regulated automotive safety systems, marking areas falling outside the Informal Working Group on AI’s terms of reference and bracketing sections pending internal consultations. The USA indicated it will provide additional technical comments following stakeholder consultations.

Proposals for revisions to Section I including highlighted deletions, addition of two paragraphs (2.3, 2.4) in Section II with a request to add safety benefits to para. 2.6, editorial requests for text placement in Section III introducing use case content, small editorial proposals in Section IV’s introduction, merging of whole lifecycle and organizational risks, removal of para. 4.6 (systematic performance disparities) in favour of para. 4.5, revision of para. 6.2 (concept drift), removal or moving of para. 6.5 to whole lifecycle risks, and substantive suggestions in Section V for deletion, merging and restructuring of Table 2 with standard and title references only. Annex 1 critiques the need for extended descriptions.

Comments on the draft consolidated reference document for AI use in regulated automotive safety systems. The comments propose editorial changes to the introduction; minor editorial modifications; and substantive contributions to AI use cases in driving functions (section 3.2) and non-driving functions (section 3.3). Canada’s contributions include additions to risk descriptions such as lack of resilience, regression or catastrophic forgetting, bias as systematic performance disparities, and degradation of AI safety processes over time; expanded guidance on risk management practices with specific automotive examples; and clarification of verification, validation, data management, and operational monitoring requirements aligned with existing standards including ISO 26262, ISO 21448, ISO 23894, and UN Regulations 155 and 156.

The IWG on AI will hold its 9th session on June 3–4, 2026 in London. The agenda includes adoption of the agenda and brief report of the 8th session (AI-09-01), consideration of AI use cases, literature review and terms & definitions proposals, guiding questions for WP.29 submission, and discussion of the consolidated draft reference document. The IWG will receive presentations from stakeholders on AI use in the automotive sector and discuss future meetings and work direction beyond the initial mandate.

This document presents AAPC comments on AI-09-06, a consolidated reference document on artificial intelligence in regulated automotive safety systems. The proposed structure consolidates AI uses, use cases, and risk management into single chapters supported by annexes containing tables and bibliographies. The document should support future regulatory deliberations without pre-empting regulatory requirements, presenting factual statements in neutral language. Considerations should address risks not captured by conventional testing. Examples to illustrate system-specific aspects that impact whether the application of AI presents new concerns and determines the nature of responses include predictive window defogging, predictive vehicle maintenance, and AI vehicle knowledge systems.

France supports retaining risks 3.5 (unreliable uncertainty estimation) and 6.1 (adversarial input manipulation) in Table 1. France favours secretarial text for mitigations 3.1 (blackbox behaviour), 3.2 (lack of robustness), and 3.4 (model over/underfitting) in Table 2. In Annex I, para. 11 addressing lack of robustness, France favours deleting the industry proposal on explanation of mitigations, pending merger with the mitigations table subject to level of detail determination.

This document updates terminology and definitions for the WP.29 Informal Working Group on Artificial Intelligence, amending AI-01-02/rev.2 as presented at the IWG’s second session. It compiles technical terms for AI in regulated automotive safety systems across four sections: Fundamental AI Concepts & Terminology, AI Methods & Models, AI Characteristics, and Data-Related Concepts & Terminology. Definitions draw from ECE/TRANS/WP.29/1182 and ECE/TRANS/WP.29/1186, among other sources, and are arranged as a living reference document supporting harmonization of AI practices for road vehicles. The document introduces new terms including Convolutional Neural Network, Neural Network, and Transformer, alongside established definitions such as Machine Learning, Generative AI, and Trustworthiness.

This document provides a consolidated list of contributions by the IWG on AI experts to the literature review activity. References are organized by type: Standards including ASPICE 4.0, IEEE standards on robustness testing, algorithmic bias, and organizational governance of AI, ISO standards on safety of intended functionality, functional safety, AI concepts and terminology, ethical considerations, AI management systems, functional safety and AI systems, road vehicle safety and AI, cybersecurity engineering, and safety for automated driving systems, SAE standards on levels of driving automation, AI data, and AI use cases, and UL4600; Policy Papers and Research Articles addressing testing autonomous vehicles, trustworthy AI, ethics of connected and automated vehicles, and safety effectiveness; Terminology Databases including IEC Electropedia and ISO Online Browsing Platform; and Regulations including Council of Europe Framework Convention on AI, EU AI Act, UN Regulation No. 155 on cybersecurity, and UN Regulation No. 156 on software updates.