The scene understanding component is a fundamental part of autonomous vehicle systems: it provides the necessary inputs to the downstream planning and control algorithms required to navigate the real world. In such context, to provide the necessary robustness guarantees of the deep learning models employed in the perception system, simulation is often resorted to as a means to perfect such algorithms in complex or edge case driving scenarios. However, perception models trained on simulated data and tested on real environments are affected by a domain shift, which often hinders the improvements obtained by the use of simulation. Hence, this work focuses on addressing the domain adaptation problem for the novel panoptic segmentation task, in the context of autonomous driving perception.