In recent years we have seen a rise in the complexity of the architecture of physical systems, as these architectures become more complex also the correct functioning of such systems becomes more complicated. Furthermore, it is possible that an accurate model of the system is not provided therefore, even with complex algorithms to verify it’s proper functioning becomes complicated. The goal of the thesis is to provide a data-driven model where the model of the cyber-physical system (in this case a self driving car) is developed through a black-box model, an LSTM neural network. This network will learn the behavior of the system by providing the states at the next time step and will use these outputs to guarantee safety protocols.