Collective perception has received considerable attention as a promising approach to overcome occlusions and limited sensing ranges of vehicle-local perception in autonomous driving. In order to develop and test novel collective perception technologies, appropriate datasets are required. These datasets must include not only different environmental conditions, as they strongly influence the perception capabilities, but also a wide range of scenarios with different road users as well as realistic sensor models. Therefore, we propose the Synthetic COllective PErception (SCOPE) dataset. SCOPE is the first synthetic multi-modal dataset that incorporates realistic camera and LiDAR models as well as parameterized and physically accurate weather simulations for both sensor types. The dataset contains 17,600 frames from over 40 diverse scenarios with up to 24 collaborative agents, infrastructure sensors, and passive traffic, including cyclists and pedestrians. In addition, recordings from two novel digital-twin maps from Karlsruhe and T\"ubingen are included. The dataset is available at https://ekut-es.github.io/scope

本研究解决了当前自主驾驶中集体感知技术所需的缺乏适当数据集的问题。提出的SCOPE数据集是首个包含真实相机和激光雷达模型以及物理准确天气模拟的合成多模态数据集，涵盖超过40种场景，并支持多达24个协作代理。该数据集的发布将推动集体感知技术的研究与发展。

综合感知：一种包含物理准确天气条件的合成多模态数据集