AI-driven systems introduce dynamic learning, adaptability and continuous updates, posing significant challenges to traditional homologation methods. The objective of this paper is to analyze the existing homologation methodologies, such as the New Assessment/Test Methodology (NATM), and examine how various institutions, including UNECE, JRC and SAE, address AI's incorporation into ADS certification. The discussion focuses on identifying gaps in current frameworks, evaluating the harmonization of principles like transparency, robustness and ethical accountability, and proposing a roadmap for future integration. Ultimately, the paper aims to highlight how harmonized approaches can ensure both innovation and safety in AI-enabled ADS.

• AI use cases and current trends
• A harmonized roadmap for AI in ADS homologation
• Key challenges in AI homologation
• Current Frameworks and their applications