GAIA-X 4 AGEDA: A Federated and Secure Cloud Framework with AI-Driven Traffic Compliance in Connected Vehicles - Rettberg, Achim; Ambigapathy, Narmada; Idrees, Fatima; Alhalabi, Ali; Glöwing, Katrin; Azarafza, Mehdi (Anais do Workshop Latinoamericano de Dependabilidade e Segurança em Sistemas Veiculares (SSV), 2025)
Connected and autonomous vehicles increasingly rely on secure cloud infrastructures to exchange perception, safety, and compliance information. Yet, the lack of standardized mechanisms for trustworthy data federation limits interoperability and regulatory validation across heterogeneous vehicle fleets. This paper presents GAIA-X 4 AGEDA, a federated and secure cloud framework that combines dynamic edge orchestration with AI-driven traffic compliance monitoring for connected vehicles. The architecture leverages the GAIA-X Trust Framework for credential-based authentication and sovereign data exchange, while integrating Orchestration for mixed-criticality workload management across a four-zone vehicle architecture. An edge-AI compliance module validates real-time adherence to legal road traffic regulations, with results securely transmitted through GAIA-X Data Spaces to enable cooperative awareness among connected vehicles. Using a CARLA-based proof-of-concept [1] deployed on Raspberry Pi 5 and Jetson Nano P3541 platforms, we demonstrate how compliance violations detected at the edge are authenticated, federated, and shared with Traffic Awareness Services (TAS) while preserving data sovereignty. The framework achieves V2X compliance alerts and this work establishes a practical pathway toward dependable, regulation-aware, and federated vehicle ecosystems aligned with European data sovereignty principles, enabling secure service deployment throughout the vehicle lifecycle.