Connected and Cooperative Automotive Mobility (CCAM) solutions harness the power of Artificial Intelligence (AI) to develop driving functions that outperform humans under specific conditions. However, AI faces challenges in terms of explainability, privacy preservation, ethics, and accountability, which are essential for establishing trustworthy AI. Explainable AI (XAI) has gained prominence as users seek to understand how AI systems function and behave. It encompasses interpretability (comprehensibility by humans) and completeness (exhaustive explanations). AITHENA proposes a human-centric methodology for the development, deployment, and testing of AI models within CCAM functions. This methodology focuses on trustworthiness and human acceptance, with a strong emphasis on XAI and Data Management. This paper presents an ongoing development effort focused on the methodology and guidelines for a specific research area. The work primarily centers on methodological approaches and the forthcoming presentation of guidelines.