Autonomous Driving (AD), the area of robotics with the greatest potential impact on society, has gained a lot of momentum in the last decade. As a result of this, the number of datasets in AD has increased rapidly. Creators and users of datasets can benefit from a better understanding of developments in the field. While scientometric analysis has been conducted in other fields, it rarely revolves around datasets. Thus, the impact, attention, and influence of datasets on autonomous driving remains a rarely investigated field. In this work, we provide a scientometric analysis for over 200 datasets in AD. We perform a rigorous evaluation of relations between available metadata and citation counts based on linear regression. Subsequently, we propose an Influence Score to assess a dataset already early on without the need for a track-record of citations, which is only available with a certain delay.

翻译：自主驾驶（AD）作为机器人领域对社会具有最大潜在影响的领域，在过去十年中获得了显著发展势头。由此，自主驾驶数据集的数量迅速增长。数据集的创建者和使用者均可从对该领域发展趋势的深入理解中获益。尽管其他领域已开展了科学计量分析，但鲜有围绕数据集展开的研究。因此，数据集对自主驾驶的影响、关注度与作用力仍是一个鲜少被探索的领域。本研究对自主驾驶领域超过200个数据集进行了科学计量分析。我们基于线性回归方法，对可用元数据与引用次数之间的关系进行了严格评估。进而提出一种影响力评分（Influence Score），可在无需依赖仅能通过延时获取的引用记录的情况下，对数据集进行早期评估。