The French Mobilities Alliance is a federation bringing together entrepreneurs in the autotech and new mobility sectors driving innovation toward a low-carbon, digital, and universally accessible form of mobility. It has more than 100 members organized across 10 working groups covering accessibility, batteries, electrification, elected officials, carsharing, data and AI, free-floating, carpooling, supply chain, and corporate fleets. The Alliance is the 19th branch of Mobilians, a federation which brings together more than 180,000 companies across 25 mobility-related sectors.
The 47th session of the Task Force on Tyre Abrasion convened on 2 April 2026. The review committee prepared final consolidation of a new UN Regulation on Tyre Abrasion for WP.29. TFTA reviewed the workplan including data assessment, tyre batch use, and equivalence assessment governance. For C2, ETRTO presented findings on the vehicle test method at 10,000 km, while JASIC reported on indoor drum test development. Discussion on C3 Heavy-Duty Vehicle tyre applications was deferred. The next session is scheduled for 11 May 2026.
Consolidated comments on table 1 of AI-06-07 from AI-08-05, AI-08-06, AI-08-09, AI-08-11, AI-08-12, and AI-08-13. The document provides a prospective catalogue of risks related to the use of AI in the automotive sector, organized according to AI lifecycle stages: AI & Use Case Specification; AI Model Architecture & Training Processes; Data Specification & Management; Verification & Validation; and Operation/In-Use Monitoring. For each identified risk, high-level and sub-level descriptions, brief risk descriptions, and potential management and mitigation approaches are provided.
Proposal to add a new section on Cross-Cutting Organisational and Assurance Risks to AI-07-06. Six risks are proposed: insufficient organisational governance for AI safety management; inadequate definition of roles, responsibilities, and competencies; lack of independence between development and verification activities; degradation of AI safety processes over time; ineffective change management for AI-related artefacts and processes; and inadequate management of AI supply-chain risks. Each risk addresses factors across the lifecycle that can influence the effectiveness of technical AI risk mitigations.
This consolidated draft reference document, based on WP.29-195-20, addresses Artificial Intelligence in the automotive sector. It defines AI-based systems as connectionist systems trained using machine learning algorithms. The document identifies AI use cases for driving functions including perception, planning, motion control, and end-to-end systems, plus non-driving functions like driver assessment. It establishes prospective risks across five AI lifecycle stages: specification, model architecture and training, data management, verification and validation, and in-use monitoring. The document provides initial risk management approaches informed by literature review, noting that current regulatory provisions may require evaluation to address AI-specific testing and updating needs.
This document provides a prospective catalogue of risks related to AI use in the automotive sector, organized across five AI lifecycle stages: AI & Use Case Specification, AI Model Architecture & Training Processes, Data Specification & Management, Verification & Validation, and Operation/In-Use Monitoring. For each of 23 identified risks, the document lists relevant ISO/PAS 8800, NIST AI RMF, EU AIA, and other standards or frameworks that address potential management and mitigation approaches. Extended descriptions explain how risks such as blackbox behaviour, data poisoning, distribution shift, insufficient test coverage, and concept drift may compromise safety in automotive AI systems.