The automotive industry is not immune to the AI revolution, and as AI becomes central to perception and decision-making, validating its performance at scale remains one of the most complex challenges facing OEMs. Traditional validation methods struggle to keep pace with the non-deterministic nature of AI systems, the long tail of edge cases, and the sheer volume of scenarios required to build confidence in real-world deployment.
This panel brings together OEM experts for a deep, technical discussion on how AI validation is evolving in practice. When failure occurs, how do engineers decode the origin of the issue? What tools, logging strategies, and observability frameworks are needed to make AI decision-making more interpretable and diagnosable? And what might be the impacts of compressed development cycles?
