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：本文阐述了我们为推进智能车辆与基础设施互操作性研究而设计的新型数据集的愿景与当前进展，该数据集特别致力于提升公共交通领域的协同感知与交互能力。与聚焦自车数据的传统数据集不同，本方案同时包含固定传感器塔与移动车辆平台，二者均配备摄像头、激光雷达与全球导航卫星系统，其中车辆平台还额外搭载惯性导航系统。我们的配置具备完整的标定与时间同步机制，确保实现无缝且精确的传感器数据融合，这对研究复杂动态场景至关重要。数据集以公共交通为核心，计划涵盖公交站场机动操作、公交专用道行驶等典型场景，并特别体现小型公共巴士的运行特性。我们为此新型数据集推出了开源的".4mse"文件格式，并配套提供研究工具包。该工具包包含自运动补偿、激光雷达-摄像头投影等工具，以支持智能车路协同集成的前沿研究。当前方案暂不包含数据标注，但我们计划采用基于前沿公开资源库的自动标注方案。若干技术细节仍在讨论中，我们诚挚期待学术界的及时反馈。我们将通过Google Colab Notebook提供单帧数据的预览，并依托相关GitHub代码库收集意见与建议。