Graph Convolutional Networks for Complex Traffic Scenario Classification
Tobias Hoek Holger Caesar Andreas Falkovén Tommy Johansson
A scenario-based testing approach can reduce the time required to obtain
statistically significant evidence of the safety of Automated Driving Systems
(ADS). Identifying these scenarios in an automated manner is a challenging
task. Most methods on scenario classification do not work for complex scenarios
with diverse environments (highways, urban) and interaction with other traffic
agents. This is mirrored in their approaches which model an individual vehicle
in relation to its environment, but neglect the interaction between multiple
vehicles (e.g. cut-ins, stationary lead vehicle). Furthermore, existing
datasets lack diversity and do not have per-frame annotations to accurately
learn the start and end time of a scenario. We propose a method for complex
traffic scenario classification that is able to model the interaction of a
vehicle with the environment, as well as other agents. We use Graph
Convolutional Networks to model spatial and temporal aspects of these
scenarios. Expanding the nuScenes and Argoverse 2 driving datasets, we
introduce a scenario-labeled dataset, which covers different driving
environments and is annotated per frame. Training our method on this dataset,
we present a promising baseline for future research on per-frame complex
scenario classification.