Human drivers naturally reason about interactions between road users to
understand and safely navigate through traffic. Thus, developing autonomous
vehicles necessitates the ability to mimic such knowledge and model
interactions between road users to understand and navigate unpredictable,
dynamic environments. However, since real-world scenarios often differ from
training datasets, effectively modeling the behavior of various road users in
an environment remains a significant research challenge. This reality
necessitates models that generalize to a broad range of domains and explicitly
model interactions between road users and the environment to improve scenario
understanding. Graph learning methods address this problem by modeling
interactions using graph representations of scenarios. However, existing
methods cannot effectively transfer knowledge gained from the training domain
to real-world scenarios. This constraint is caused by the domain-specific rules
used for graph extraction that can vary in effectiveness across domains,
limiting generalization ability. To address these limitations, we propose
RoadScene2Graph (RS2G): a data-driven graph extraction and modeling approach
that learns to extract the best graph representation of a road scene for
solving autonomous scene understanding tasks. We show that RS2G enables better
performance at subjective risk assessment than rule-based graph extraction
methods and deep-learning-based models. RS2G also improves generalization and
Sim2Real transfer learning, which denotes the ability to transfer knowledge
gained from simulation datasets to unseen real-world scenarios. We also present
ablation studies showing how RS2G produces a more useful graph representation
for downstream classifiers. Finally, we show how RS2G can identify the relative
importance of rule-based graph edges and enables intelligent graph sparsity
tuning.

提出了一种数据驱动的图提取和建模方法，称为 RoadScene2Graph (RS2G)，它能够学习提取用于解决自主场景理解任务的道路场景的最佳图表示，并提高图学习方法的泛化性和仿真数据到实际场景的转移能力。