🤖 AI Summary
This study addresses the lack of automated, universal representation tools for trajectory safety analysis by proposing an open-source, modular framework. Introducing a novel three-layer decoupled architecture with Pydantic validation, it establishes a configuration-driven, plug-and-play pipeline that enables dataset-agnostic feature extraction, behavior detection, and criticality scoring. Validated on Waymo, Argoverse2, and nuPlan datasets, the framework supports unified cross-dataset safety analysis and visualization. This work significantly enhances the efficiency and standardization of autonomous driving safety evaluation, effectively bridging the gap in general-purpose safety representation tooling.
📝 Abstract
We introduce ScenarioCharacterization, an open-source framework for automated, dataset-agnostic profiling of driving scenarios in trajectory datasets. Our framework is packaged as a modular, configuration-driven pipeline of three layers: a dataset adapter that maps custom datasets onto an open Scenario representation, a characterizer that performs feature extraction, behavior probing, and criticality scoring at scenario and agent levels, and an analysis layer for scenario visualization and feature, score, and probe analyses. Because the layers communicate only through Pydantic-validated schemas composed via configurations, a new dataset can easily plug in without rewriting the characterization and analysis stack.
This technical report describes the design and APIs, shows example outputs on Waymo Open Motion, Argoverse2, and nuPlan, and discusses downstream uses of the approach. The framework is available at https://github.com/navarrs/ScenarioCharacterization.