Building trustworthy, explainable,
and accountable CCAM technologies
Connected and Cooperative Automated Mobility (CCAM) solutions are increasingly present in vehicle technologies, which benefit from artificial intelligence (AI) through AI-based perception, situational awareness, and decision-making components.
But AI can be unfair, biased, and can be extremely sensitive to unexpected inputs. Building explainable and trustworthy AI is the next mandatory step of technology development, incorporating among other equally important properties: robustness, privacy, explainability,
News
Strengthening impact and alignment: Insights from the AIthena report on dissemination and standardisation activities
The latest public deliverable D6.5 from the AIthena project, 'Report on Dissemination and Standardisation Activities', provides a comprehensive overview of how the project has shared its results and engaged with key European and international stakeholders throughout...
AIthena project report on data sharing and integration with European data tools
The latest AIthena publication: 'D6.2 Report on Data Sharing and Integration with European Data Lakes, OpenData and OpenTool' outlines the project’s strategy for enabling transparent, interoperable and FAIR-aligned data sharing to support the development of...
AIthena publishes final evaluation of AI-driven CCAM use cases in new report
The AIthena project has released its deliverable D5.3 'Report on final use case evaluation'. This comprehensive report presents the final assessment of AIthena four use cases, namely: UC-1: Trustworthy Perception Systems for CCAM, UC-2: AI extended Situational...