Trustworthy, explainable, and accountable AI-based CCAM

CCAM solutions are increasingly present in vehicle technologies, which benefit from artificial intelligence (AI) through AI-based perception, situational awareness, and decision-making components. The EC-funded project AIthena aims to build trustworthy AI, incorporating among other equally important properties: robustness, privacy, explainability, accountability, and ethics.

Four use cases were selected to demonstrate the AIthena methodology: three of them on the vehicle level, investigate the entire processing pipeline of an automated driving software stack and how a single vehicle (eventually) behaves in traffic; while the fourth will apply the AIthena methodology from a Traffic Management perspective and focuses on the impact of several vehicles on the traffic dynamics at the network level.

More details on each use case is available here and you can also watch short videos presenting the four Use Cases:

• Trustworthy Perception Systems for CCAM (UC1)
• AI extended Situational Awareness/Understanding (UC2)
• Trustworthy and Human understandable Decision-making (UC3)
• AI based Traffic management (UC4)

Read more about the project latest updates in their recently published newsletter.