  Recently, road scene-graph representations used in conjunction with graph
learning techniques have been shown to outperform state-of-the-art deep
learning techniques in tasks including action classification, risk assessment,
and collision prediction. To enable the exploration of applications of road
scene-graph representations, we introduce roadscene2vec: an open-source tool
for extracting and embedding road scene-graphs. The goal of roadscene2vec is to
enable research into the applications and capabilities of road scene-graphs by
providing tools for generating scene-graphs, graph learning models to generate
spatio-temporal scene-graph embeddings, and tools for visualizing and analyzing
scene-graph-based methodologies. The capabilities of roadscene2vec include (i)
customized scene-graph generation from either video clips or data from the
CARLA simulator, (ii) multiple configurable spatio-temporal graph embedding
models and baseline CNN-based models, (iii) built-in functionality for using
graph and sequence embeddings for risk assessment and collision prediction
applications, (iv) tools for evaluating transfer learning, and (v) utilities
for visualizing scene-graphs and analyzing the explainability of graph learning
models. We demonstrate the utility of roadscene2vec for these use cases with
experimental results and qualitative evaluations for both graph learning models
and CNN-based models. roadscene2vec is available at
https://github.com/AICPS/roadscene2vec.
