  Testing is essential for verifying and validating control designs, especially
in safety-critical applications. In particular, the control system governing an
automated driving vehicle must be proven reliable enough for its acceptance on
the market. Recently, much research has focused on scenario-based methods.
However, the number of possible driving scenarios to test is in principle
infinite. In this paper, we formalize a learning-based optimization framework
to generate corner test-cases, where we take into account the operational
design domain. We examine the approach on the case of a feedback control system
for automated driving, for which we suggest the design of the objective
function expressing the criticality of scenarios. Numerical tests on two
logical scenarios of the case study demonstrate that the approach can identify
critical scenarios within a limited number of closed-loop experiments.

Systems and Control;Systems and Control