驾驶场景知识图谱赋能神经符号AI的新兴能力
Knowledge Graphs of Driving Scenes to Empower the Emerging Capabilities of Neurosymbolic AI
AI总结：
本文提出DSceneKG，一个基于真实自动驾驶场景构建的驾驶场景知识图谱套件，旨在填补神经符号AI基准数据集的空白，并支持多种任务的评估。
摘要：
在生成式AI时代，神经符号AI正成为一种强大的方法，适用于从感知到认知的各种任务。研究表明，使用神经符号AI可以实现增强的能力，包括改进的接地性、对齐性、可解释性和可靠性。然而，由于其处于初级阶段，缺乏广泛可用的、专为神经符号AI任务定制的真实世界基准数据集。为了解决这一差距并支持当前和未来方法的评估，我们引入了DSceneKG——一个基于多个开放自动驾驶数据集中的真实高质量场景构建的驾驶场景知识图谱套件。在本文中，我们详细介绍了DSceneKG的构建过程，并展示了其在七个不同任务中的应用。DSceneKG可在以下网址公开访问：https://github.com/ruwantw/DSceneKG
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
