The IWG will be informed on the status of identification of AI Use Cases, the revised draft Terms & Definitions Document, and the ongoing collection of proposals for the literature review. The IWG may wish to exchange views concerning guiding questions for AI in automotive, risks of the use of AI in automotive and their management and mitigation, and the consolidated draft for reference document. The 9th IWG on AI session is planned for June 3-4th, hybrid in London, UK and online.

The IWG on AI 7th session (March 12, 2026) adopted the agenda and 6th session report. The IWG secretariat presented updated AI use case document (AI-07-03) and consolidated literature review (AI-07-04). France presented additions to use cases (AI-07-13); China presented literature review proposals (AI-07-07, AI-07-08, AI-07-09, AI-07-10, AI-07-11, AI-07-12). The IWG expressed support for the guiding questions document (AI-07-05) and directed language amendments. Japan presented a draft reference document framework (AI-07-14); the prospective catalogue of risks (AI-07-06) received general support with requests for amendments addressing bias, risk management approaches, flexible non-exhaustive risk listing, and software update impacts. The secretariat was directed to compile a consolidated draft reference document for June WP.29 submission.

This document provides a consolidated list of contributions from the IWG on AI experts to a literature review activity on Artificial Intelligence. References are organized by type and listed alphabetically, including standards such as ISO 21448:2022, ISO 23894, ISO 26262, ISO 22989, ISO 39003:2023, ISO/IEC 42001:2023, ISO/IEC TR 5469, ISO/PAS 8800:2024, ISO/SAE 21434:2021, ISO/TS 5083:2025, SAE J3016, SAE J3298, SAE J3312, and UL4600. Policy papers and research articles address trustworthy AI, autonomous vehicles, and safety.

The Informal Working Group on Artificial Intelligence identified relevant literature for a literature review addressing existing standards, research, best practice, and regulations relating to artificial intelligence in automotive safety applications. Proposal to include IEEE standards on algorithmic bias, robustness testing, and organizational governance; risk assessment frameworks from IEEE and NIST; the EU AI Act; international instruments including the Council of Europe Framework Convention on AI and OECD AI Principles; and the G7 Hiroshima AI Process Code of Conduct. These sources address lifecycle phases and risk management relevant to the prospective catalogue of AI risks in automotive (AI-07-06) and consolidated draft documentation (AI-06-03).

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.

At the third IWG on AI session, experts agreed that a robust review supporting tasks specified in ECE/TRANS/WP.29/1186, para. 6 was needed as a precursor to drafting a reference document. The IWG on AI agreed that three guiding questions provide the basis for common understanding of artificial intelligence in the automotive sector, including emergent practices, risks, and how AI is currently being used to promote safety. The three questions address where and how AI is used and its benefits, what risks AI presents, and what emergent practices exist for AI use and risk mitigation.

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 contributes to consolidated text and a risk catalogue for artificial intelligence in the automotive sector. It defines AI as a field of computer science focused on creating systems capable of performing tasks requiring human intelligence, including learning, reasoning, problem-solving, natural language processing, and decision-making. High-priority AI use cases include perception, planning, and motion-control driving functions, as well as driver assessment systems. The document identifies 14 risks organized across five lifecycle stages: AI and use case specification, model architecture and training processes, data specification and management, verification and validation, and operation and in-use monitoring. It provides detailed risk descriptions and references emergent practices for managing these risks, including ISO/PAS 8800, NIST AI RMF, and EU AIA approaches. Lower-priority use cases referenced include AI-06-09 for periodic technical inspection and AI-06-12 for cybersecurity threat detection.

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.

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.