  We present DeepDECS, a new method for the synthesis of
correct-by-construction discrete-event controllers for autonomous systems that
use deep neural network (DNN) classifiers for the perception step of their
decision-making processes. Despite major advances in deep learning in recent
years, providing safety guarantees for these systems remains very challenging.
Our controller synthesis method addresses this challenge by integrating DNN
verification with the synthesis of verified Markov models. The synthesised
models correspond to discrete-event controllers guaranteed to satisfy the
safety, dependability and performance requirements of the autonomous system,
and to be Pareto optimal with respect to a set of optimisation objectives. We
use the method in simulation to synthesise controllers for mobile-robot
collision mitigation and for maintaining driver attentiveness in shared-control
autonomous driving.
