构建驾驶场景知识图谱，助力神经符号AI落地。
Knowledge Graphs of Driving Scenes to Empower the Emerging Capabilities of Neurosymbolic AI
- 从多个公开数据集构建真实驾驶场景知识图谱
- 支持7类任务，涵盖感知到认知的多阶段应用
- 为神经符号AI提供首个公开可用的驾驶场景基准
在生成式AI时代，神经符号AI正成为从感知到认知任务的重要方法，具备更强的语义对齐、可解释性与可靠性。然而该领域尚处初期，缺乏面向神经符号任务的通用真实世界基准数据集。为此，我们提出DSceneKG——基于多个公开自动驾驶数据集构建的高质量驾驶场景知识图谱套件。本文详述其构建流程，并展示其在七类不同任务中的应用。DSceneKG已开源，地址：https://github.com/ruwantw/DSceneKG。
原文摘要 · Abstract (English)
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
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