This paper we present our vision and ongoing work for a novel dataset designed to advance research into the interoperability of intelligent vehicles and infrastructure, specifically aimed at enhancing cooperative perception and interaction in the realm of public transportation. Unlike conventional datasets centered on ego-vehicle data, this approach encompasses both a stationary sensor tower and a moving vehicle, each equipped with cameras, LiDARs, and GNSS, while the vehicle additionally includes an inertial navigation system. Our setup features comprehensive calibration and time synchronization, ensuring seamless and accurate sensor data fusion crucial for studying complex, dynamic scenes. Emphasizing public transportation, the dataset targets to include scenes like bus station maneuvers and driving on dedicated bus lanes, reflecting the specifics of small public buses. We introduce the open-source ".4mse" file format for the new dataset, accompanied by a research kit. This kit provides tools such as ego-motion compensation or LiDAR-to-camera projection enabling advanced research on intelligent vehicle-infrastructure integration. Our approach does not include annotations; however, we plan to implement automatically generated labels sourced from state-of-the-art public repositories. Several aspects are still up for discussion, and timely feedback from the community would be greatly appreciated. A sneak preview on one data frame will be available at a Google Colab Notebook. Moreover, we will use the related GitHub Repository to collect remarks and suggestions.

翻译：本文阐述了我们针对一种新型数据集的愿景与正在进行的工作，该数据集旨在推进智能车辆与基础设施互操作性研究，特别侧重于增强公共交通领域的协同感知与交互。与集中于自车数据的传统数据集不同，本方法同时包含一个固定传感器塔和一个移动车辆，两者均配备摄像头、激光雷达和全球导航卫星系统，而车辆还额外包含一个惯性导航系统。我们的设置具备全面的校准与时间同步功能，确保实现无缝且精确的传感器数据融合，这对于研究复杂动态场景至关重要。数据集重点关注公共交通，旨在涵盖公交车站内机动操作和专用公交车道行驶等场景，以反映小型公共巴士的具体特性。我们为这一新数据集引入了开源的“.4mse”文件格式，并配套提供研究工具包。该工具包提供诸如自运动补偿、激光雷达到相机投影等工具，以支持智能车路协同集成的前沿研究。我们的方法目前不包含标注数据；然而，我们计划采用基于先进公共资源库的自动生成标签方案。若干方面仍在讨论中，我们诚挚期待来自学术界的及时反馈。单个数据帧的预览版本将通过Google Colab Notebook提供。此外，我们将利用相关的GitHub仓库收集意见与建议。