  This work explores scene graphs as a distilled representation of high-level
information for autonomous driving, applied to future driver-action prediction.
Given the scarcity and strong imbalance of data samples, we propose a
self-supervision pipeline to infer representative and well-separated
embeddings. Key aspects are interpretability and explainability; as such, we
embed in our architecture attention mechanisms that can create spatial and
temporal heatmaps on the scene graphs. We evaluate our system on the ROAD
dataset against a fully-supervised approach, showing the superiority of our
training regime.
