The Homework Team on ADB/AFS status report to the GRE Taskforce “Glare prevention” documenting timeline activities from February to June 2026, including seven HWT meetings and three TFGP sessions. The team listed system descriptions, benefits, disadvantages, conducted literature review, identified elements causing glare, negative effects to ADB/AFS performance, and improvable requirements. The team drafted recommendations addressing enforcement of headlamp cleaning, reduction of 2000lm limit, manual override requirements, improved detection and camera specifications, motorway ADB usage, cleaning systems, beam pattern dimming, and adaptive beam patterns for various driving conditions.
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.
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.
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 generic images of various door handle designs, including door handles in door pockets, button type door handles, electric push buttons, electric touch type door handles, and flush buttons.
This document presents the draft agenda for the 8th meeting of the GRE Task Force on Glare Prevention, scheduled for 9 June 2026 at CLEPA Headquarters in Brussels. The agenda includes welcome remarks, adoption of the agenda, approval of the previous meeting report, feedback from GRE-94, and status updates on HWT on PTI, ADB/AFS, and Regulation 48. Discussion topics cover horizontal adjustment of passing- and driving-beam and AFS-ADB, vehicle preparation for passing-beam measurement, and updates from the GTB Task Force Glare Control. Future meetings are scheduled for Tokyo in September 2026 and Brussels in December 2026.
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.
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