  Automated driving systems require monitoring mechanisms to ensure safe
operation, especially if system components degrade or fail. Their runtime
self-representation plays a key role as it provides a-priori knowledge about
the system's capabilities and limitations. In this paper, we propose a
data-driven approach for deriving such a self-representation model for the
motion controller of an automated vehicle. A conformalized prediction model is
learned and allows estimating how operational conditions as well as potential
degradations and failures of the vehicle's actuators impact motion control
performance. During runtime behavior generation, our predictor can provide a
heuristic for determining the admissible action space.
