<b>InterHub: A Naturalistic Trajectory Dataset with Dense Interaction for Autonomous Driving</b>
收藏资源简介：
We provide a dense interaction dataset, InterHub, derived from extensive naturalistic driving records to address the scarcity of real-world datasets capturing rich interaction events.The dataset provided on this page include:<b>A</b><b> CSV file </b>(Interactive_Segments_Index.csv) containing the <b>indexed list</b> of the extracted interaction events<b>. </b>In addition to indexing and tracing information about interaction scenarios, we also provide <b>some interesting labels</b> to facilitate more targeted retrieval and utilization of interaction scenarios.(For detailed information, please refer to https://github.com/zxc-tju/InterHub.)<b>Relevant unified data cache files </b>(InterHub_cache_files.zip that includes cache files of lyft_train_full, nuplan_train)<b>.</b>The Python codes used to process and analyze the dataset can be found at https://github.com/zxc-tju/InterHub. The tools for navigating InterHub involve the following three parts:0_data_unify.py converts various data resources into a unified format for seamless interaction event extraction.1_interaction_extract.py extracts interactive segments from unified driving records.2_case_visualize.py showcases interaction scenarios in InterHub.You can refer to the data structure of cache files presented in dataset.md, and after extracting the InterHub_cache_files.zip file, put it in the corresponding folder.<br><b>Statement: </b> <b>All third-party data redistributions included in the interhub_cache_files.zip repository are carried out in full compliance with the original licensing terms of the respective source datasets, as required by their mandatory licensing conditions. This portion of the data remains subject to its original licenses, and users of the data are required to comply with these original licensing terms in any subsequent use or redistribution.</b>
我们发布了一款密集交互数据集（InterHub），其源自海量自然驾驶记录，旨在解决现实世界中富含交互事件的真实数据集稀缺的问题。本页面提供的数据集包含以下内容：<b>一、</b><b>CSV文件</b>（Interactive_Segments_Index.csv），内含已完成索引的交互事件提取列表。除交互场景的索引与追踪信息外，我们还提供了若干实用标签，以助力对交互场景开展更具针对性的检索与利用。（详细信息请参阅https://github.com/zxc-tju/InterHub。）<b>二、</b><b>相关统一数据缓存文件</b>（InterHub_cache_files.zip，内含lyft_train_full、nuplan_train的缓存文件）。用于处理与分析该数据集的Python代码可在https://github.com/zxc-tju/InterHub获取。InterHub的配套工具包含以下三个模块：0_data_unify.py可将各类数据源转换为统一格式，以实现无缝的交互事件提取；1_interaction_extract.py可从统一后的驾驶记录中提取交互片段；2_case_visualize.py可对InterHub中的交互场景进行可视化展示。您可参考dataset.md中给出的缓存文件数据结构，解压InterHub_cache_files.zip后，将其放置至对应文件夹中。<b>声明：</b>InterHub_cache_files.zip中包含的所有第三方再分发数据，均严格遵循各源数据集的原始许可条款，符合其强制许可要求。该部分数据仍受其原始许可协议约束，数据集使用者在后续使用或再分发过程中，必须遵守这些原始许可条款。
