Omega-Prime: Data Model, Data Format and Python Library for Handling Ground Truth Traffic Data

Data Model, Format and Python Library for ground truth data containing information on dynamic objects, map and environmental factors optimized for representing urban traffic. The repository contains: Data Model and Specification see ./docs/omega_prime_specification.md 🌍 Data Model: What signals exist and how these are defined. 🧾 Data Format Specification: How to exchange and store those signals. Python Library 🔨 Create omega-prime files from many sources (see ./tutorial.ipynb): ASAM OSI GroundTruth trace (e.g., output of esmini) Table of moving object data (e.g., csv data) ASAM OpenDRIVE map LevelXData datasets through lxd-io extend yourself by subclassing DatasetConverter 🗺️ Map Association: Associate Object Location with Lanes from OpenDRIVE or OSI Maps (see tutorial_locator.ipynb) 📺 Plotting of data: interactive top view plots using altair ✅ Validation of data: check if your data conforms to the omega-prime specification (e.g., correct yaw) using pandera 📐 Interpolation of data: bring your data into a fixed frequency 📈 Metrics: compute interaction metrics like PET, TTC, THW (see tutorial_metrics.ipynb) 🚀 Fast Processing directly on DataFrames using polars, polars-st The data model and format utilize ASAM OpenDRIVE and ASAM Open-Simulation-Interface GroundTruth messages. omega-prime sets requirements on presence and quality of ASAM OSI GroundTruth messages and ASAM OpenDRIVE files and defines a file format for the exchange and storage of these. Omega-Prime is the successor of the OMEGAFormat. It has the benefit that its definition is directly based on the established standards ASAM OSI and ASAM OpenDRIVE and carries over the data quality requirements and the data tooling from OMEGAFormat. Therefore, it should be easier to incorporate omega-prime into existing workflows and tooling. To learn more about the example data read example_files/README.md. Example data was taken and created from esmini.本项目面向用于表征城市交通场景的真值数据（包含动态物体、地图与环境因子信息），提供专属的数据模型、格式规范与Python工具库。本代码仓库包含以下内容： ### 数据模型与规范说明 详见 ./docs/omega_prime_specification.md 🌍 数据模型：涵盖所有可用信号及其定义方式。 🧾 数据格式规范：说明如何交换与存储上述信号。 ### Python工具库 🔨 支持从多种数据源构建omega-prime格式文件（详见 ./tutorial.ipynb）： - ASAM OSI（开放仿真接口）真值轨迹（例如esmini的输出结果） - 动态物体数据表（例如CSV格式数据） - ASAM OpenDRIVE地图 - 基于lxd-io工具的LevelXData数据集 - 支持通过继承DatasetConverter类实现自定义扩展 🗺️ 地图关联模块：将物体位置与OpenDRIVE或OSI地图中的车道进行关联（详见 tutorial_locator.ipynb） 📺 数据可视化模块：基于altair实现交互式顶视图绘图 ✅ 数据校验模块：借助pandera工具验证数据是否符合omega-prime规范（例如航向角正确性校验） 📐 数据插值模块：将数据统一到固定采样频率 📈 指标计算模块：支持计算后侵时间（Post Encroachment Time, PET）、碰撞时间（Time To Collision, TTC）与车头时距（Time Headway, THW）等交互场景指标（详见 tutorial_metrics.ipynb） 🚀 基于polars与polars-st库实现DataFrame级别的极速数据处理 本数据模型与格式规范基于ASAM OpenDRIVE与ASAM开放仿真接口（Open-Simulation-Interface, OSI）真值消息构建。omega-prime对ASAM OSI真值消息与ASAM OpenDRIVE文件的存在性与质量提出要求，并定义了用于此类数据交换与存储的专属文件格式。 Omega-Prime是OMEGAFormat的升级继任版本。其优势在于直接基于ASAM OSI与ASAM OpenDRIVE这两项成熟标准进行定义，并继承了OMEGAFormat的数据质量要求与配套工具链，因此能够更轻松地集成至现有工作流与工具生态中。 如需了解示例数据的更多细节，请查阅 example_files/README.md。本项目的示例数据源自并基于esmini生成。