自动化分析驾驶场景安全性的开源工具包，支持多数据集无缝接入。
ScenarioCharacterization: A Modular Toolkit for Characterizing Safety across Trajectory Datasets
- 模块化设计：通过配置驱动三层次流水线，实现数据集适配与场景分析解耦。
- 支持Waymo、Argoverse2、nuPlan等主流数据集，可生成场景特征与风险评分。
- 适合自动驾驶安全评估、场景库构建与算法对比研究的开发者使用。
我们提出ScenarioCharacterization，一个开源框架，用于对轨迹数据集中的驾驶场景进行自动化、数据集无关的特征刻画。该框架采用三层模块化设计：数据集适配器将自定义数据集映射到开放的场景表示；特征提取器在场景和代理层面执行特征提取、行为探测与关键性评分；分析层提供场景可视化及特征、评分和探测结果分析。各层仅通过Pydantic验证的配置化模式通信，新数据集可无需重写分析栈即可快速接入。本技术报告介绍其设计与API，展示在Waymo Open Motion、Argoverse2和nuPlan上的示例输出，并讨论下游应用。框架开源地址：https://github.com/navarrs/ScenarioCharacterization。
原文摘要 · Abstract (English)
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.
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