  Deep learning models obtain impressive accuracy in road scenes understanding,
however they need a large quantity of labeled samples for their training.
Additionally, such models do not generalise well to environments where the
statistical properties of data do not perfectly match those of training scenes,
and this can be a significant problem for intelligent vehicles. Hence, domain
adaptation approaches have been introduced to transfer knowledge acquired on a
label-abundant source domain to a related label-scarce target domain. In this
work, we design and carefully analyse multiple latent space-shaping
regularisation strategies that work together to reduce the domain shift. More
in detail, we devise a feature clustering strategy to increase domain
alignment, a feature perpendicularity constraint to space apart features
belonging to different semantic classes, including those not present in the
current batch, and a feature norm alignment strategy to separate active and
inactive channels. In addition, we propose a novel evaluation metric to capture
the relative performance of an adapted model with respect to supervised
training. We validate our framework in driving scenarios, considering both
synthetic-to-real and real-to-real adaptation, outperforming previous
feature-level state-of-the-art methods on multiple road scenes benchmarks.
