We encounter a wide range of obstacles when integrating computer vision algorithms into applications inside the vehicle cabin, eg variations in illumination, sensor-type and-placement. Thus, designing domain-invariant representations is crucial for employing such models in practice. Still, the vast majority of driver activity recognition algorithms are developed under the assumption of a static domain, ie an identical distribution of training-and test data. In this work, we aim to bring driver monitoring to a setting, where domain shifts can occur at any time and explore generative models which learn a shared representation space of the source and target domain. First, we formulate the problem of unsupervised domain adaptation for driver activity recognition, where a model trained on labeled examples from the source domain (ie color images) is intended to adjust to a different target domain (ie infrared images) where only unlabeled data is available during training. To address this problem, we leverage current progress in imageto-image translation and adopt multiple strategies for learning a joint latent space of the source and target distribution and a mapping function to the domain of interest. ... mehrAs our longterm goal is a robust cross-domain classification, we enhance a Variational Auto-Encoder (VAE) for image translation with a classification-driven optimization strategy. Our model for classification-driven domain transfer leads to the best crossdomain recognition results and outperforms a conventional classification approach in color-to-infrared recognition by 13.75%.