To evaluate the safety and robustness of such an AI-oriented system with its HW and SW parts, we think of a methodological approach based on preliminary safety assessment results, functional testing on a virtual platform, fault injection, and uncertainty measurement. Concretely, the evaluation framework will be based on: Risk assessment providing some kind of envelope of permissible behavior for the AI ​​system without compromising safety and list of faults and failures with their effects on the system An embedded system simulator capable of injecting fault at functional and hardware levels Online (at runtime from devices) and offline (from testbench dataset) fault injection (HW/SW) technique to stress the system with regard to faulty scenarios uncertainty measures with an estimation of their confidence level runtime monitoring for the tracking of fault propagation, and study of their impact at the algorithmic level The goal of the project is to implement and validate such an approach on a use case.