  Despite impressive advancements in Autonomous Driving Systems (ADS),
navigation in complex road conditions remains a challenging problem. There is
considerable evidence that evaluating the subjective risk level of various
decisions can improve ADS' safety in both normal and complex driving scenarios.
However, existing deep learning-based methods often fail to model the
relationships between traffic participants and can suffer when faced with
complex real-world scenarios. Besides, these methods lack transferability and
explainability. To address these limitations, we propose a novel data-driven
approach that uses scene-graphs as intermediate representations. Our approach
includes a Multi-Relation Graph Convolution Network, a Long-Short Term Memory
Network, and attention layers for modeling the subjective risk of driving
maneuvers. To train our model, we formulate this task as a supervised scene
classification problem. We consider a typical use case to demonstrate our
model's capabilities: lane changes. We show that our approach achieves a higher
classification accuracy than the state-of-the-art approach on both large (96.4%
vs. 91.2%) and small (91.8% vs. 71.2%) synthesized datasets, also illustrating
that our approach can learn effectively even from smaller datasets. We also
show that our model trained on a synthesized dataset achieves an average
accuracy of 87.8% when tested on a real-world dataset compared to the 70.3%
accuracy achieved by the state-of-the-art model trained on the same synthesized
dataset, showing that our approach can more effectively transfer knowledge.
Finally, we demonstrate that the use of spatial and temporal attention layers
improves our model's performance by 2.7% and 0.7% respectively, and increases
its explainability.
