  The safety of Automated Vehicles (AV) as Cyber-Physical Systems (CPS) depends
on the safety of their consisting modules (software and hardware) and their
rigorous integration. Deep Learning is one of the dominant techniques used for
perception, prediction, and decision making in AVs. The accuracy of predictions
and decision-making is highly dependant on the tests used for training their
underlying deep-learning. In this work, we propose a method for screening and
classifying simulation-based driving test data to be used for training and
testing controllers. Our method is based on monitoring and falsification
techniques, which lead to a systematic automated procedure for generating and
selecting qualified test data. We used Responsibility Sensitive Safety (RSS)
rules as our qualifier specifications to filter out the random tests that do
not satisfy the RSS assumptions. Therefore, the remaining tests cover driving
scenarios that the controlled vehicle does not respond safely to its
environment. Our framework is distributed with the publicly available S-TALIRO
and Sim-ATAV tools.
