In the era of Generative AI, Neurosymbolic AI is emerging as a powerful approach for tasks spanning from perception to cognition. The use of Neurosymbolic AI has been shown to achieve enhanced capabilities, including improved grounding, alignment, explainability, and reliability. However, due to its nascent stage, there is a lack of widely available real-world benchmark datasets tailored to Neurosymbolic AI tasks. To address this gap and support the evaluation of current and future methods, we introduce DSceneKG -- a suite of knowledge graphs of driving scenes built from real-world, high-quality scenes from multiple open autonomous driving datasets. In this article, we detail the construction process of DSceneKG and highlight its application in seven different tasks. DSceneKG is publicly accessible at: https://github.com/ruwantw/DSceneKG

翻译：在生成式人工智能时代，神经符号人工智能正逐渐成为从感知到认知任务的一种强大方法。神经符号人工智能的应用已被证明能够实现增强的能力，包括改进的接地性、对齐性、可解释性和可靠性。然而，由于其处于起步阶段，目前缺乏广泛可用的、专为神经符号人工智能任务定制的真实世界基准数据集。为了弥补这一空白并支持当前及未来方法的评估，我们引入了DSceneKG——一套基于多个开放自动驾驶数据集中的真实世界高质量场景构建的驾驶场景知识图谱。本文详细阐述了DSceneKG的构建过程，并重点介绍了其在七个不同任务中的应用。DSceneKG可通过以下链接公开访问：https://github.com/ruwantw/DSceneKG