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 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.
Draft agenda for the 54th session of TFTA on 9 November 2026. The meeting will address finalising C1 and C2 provisions for GRBP Working Document 85th session, the status of an ongoing test campaign, and a follow-up brainstorming session. Action items include requesting a French representative to join the next meeting and reviewing possibilities for further defining tyre types by grouping them and optimizing the number of physical tests through simulations and models. The agenda also covers funding and resource allocation for technical activities.
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.