ScenarioCharacterization：用于刻画轨迹数据集安全性的模块化工具包
ScenarioCharacterization: A Modular Toolkit for Characterizing Safety across Trajectory Datasets
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中文总结 AI 辅助
本文提出开源模块化框架ScenarioCharacterization，可自动刻画轨迹数据集的驾驶场景安全性，适配多数据集，在Waymo等数据集上验证，支持下游应用。
中文摘要 AI 辅助
我们推出ScenarioCharacterization，这是一个开源框架，用于对轨迹数据集中的驾驶场景进行与数据集无关的自动刻画。该框架被打包为模块化、配置驱动的流水线，包含三层：数据集适配器，可将自定义数据集映射为开放的场景表示；刻画器，在场景和智能体层面执行特征提取、行为探测和临界性评分；分析层，用于场景可视化及特征、评分和探测分析。由于各层仅通过经Pydantic验证的、由配置组成的模式通信，新数据集可轻松接入，无需重写刻画与分析栈。本技术报告描述了其设计与API，展示了在Waymo Open Motion、Argoverse2和nuPlan上的示例输出，并讨论了该方法的下游用途，框架可通过指定URL获取。
英文摘要
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.
发表机构
- Robotics Institute, School of Computer Science, Carnegie Mellon University（卡内基梅隆大学计算机学院机器人研究所）
- Stack AV
- Bosch Center for Artificial Intelligence（博世人工智能中心）
机构由 AI 辅助整理，请以论文原文为准。
