The increasing use of AI in vehicles and transport requires humans to trust AI decisions blindly. To gain social acceptance, developing trustworthy AI is essential. This involves balancing robustness, privacy, explainability, accountability, and ethics.
AITHENA proposes a harmonized, human-centric methodology for AI-based CCAM solutions, focusing on perception, situational awareness, decision-making, and traffic management. This methodology emphasizes trustworthy AI pillars—accuracy, explainability, accountability, privacy, and ethics—to serve diverse end users, including drivers, function developers, and certification/legal bodies.
AITHENA advances three AI aspects: data management, AI model development, and testing and validation approaches. It focuses on developing explainable AI (XAI) concepts, such as physically informed neural networks, deep hybrid learning, and reinforcement learning with explainable layers. Additionally, it ensures scalable, transparent, and unbiased XAI training processes through data generation, processing, and traceability.
The RTD approach demonstrates use cases across CCAM layers: perception, situational awareness, decision-making, and mobility. These use cases propose extensions to existing standards, testing, and certification approaches.
Finally, AITHENA provides datasets and tools for future XAI system development on a cloud platform that complies with the European Dataspaces approach and GAIA-X4Future Mobility architecture. This adherence to European values of data protection, authenticity, and trust enhances European visibility and impact in the digital ecosystem.
