Knowledge Graphs of Driving Scenes to Empower the Emerging Capabilities of Neurosymbolic AI

TL;DR

This paper introduces DSceneKG, a comprehensive knowledge graph dataset of driving scenes designed to evaluate and advance Neurosymbolic AI capabilities in autonomous driving contexts.

Contribution

It presents the creation of DSceneKG, a new benchmark dataset for Neurosymbolic AI, addressing the lack of real-world datasets for driving scene understanding.

Findings

DSceneKG enables evaluation across seven different tasks.

The dataset improves benchmarking for Neurosymbolic AI in autonomous driving.

It enhances the development of explainable and reliable AI systems.

Abstract

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