Bridging the Gaps: A Comparative Analysis of ISO 21434, ISO 26262 and Machine Learning in Autonomous Vehicles - Bastos, Suelen Daiene de Oliveira; Castelo Branco, Kalinka; Oliveira, André Luiz de (Anais do Simpósio Brasileiro de Robótica e Simpósio Latino Americano de Robótica (SBR/LARS), 2025)
The integration of Machine Learning (ML) in Au-tonomous Vehicles (AV s) raises challenges not fully addressed by current automotive standards, specifically ISO 26262 (functional safety) and ISO/SAE 21434 (cybersecurity). This paper reviews gaps in these standards concerning ML-enabled systems, focusing on explainability, data lifecycle, runtime assurance, and ethical compliance. To bridge these gaps, we propose the ML-Aware Compliance Bridge, a four-layer framework covering: (i) Data Lifecycle Management, (ii) Model Governance, (iii) Runtime Assurance, and (iv) Ethics and Explainability. The framework maps standard deficiencies to actionable recommendations, sup-porting the development of safer and more trustworthy AI -driven autonomous systems.