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

arXiv: https://arxiv.org/abs/2411.03225 · HTML: https://mlsanity.com/paper/2411.03225/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 5 Nov 2024.

## Builds on (most cited first, 4 of 4)

- [CausalKG: Causal Knowledge Graph Explainability using interventional and counterfactual reasoning](https://mlsanity.com/paper/2201.03647/) (2022)
- [Building Trustworthy NeuroSymbolic AI Systems: Consistency, Reliability, Explainability, and Safety](https://mlsanity.com/paper/2312.06798/) (2023)
- [An Evaluation of Knowledge Graph Embeddings for Autonomous Driving Data: Experience and Practice](https://mlsanity.com/paper/2003.00344/) (2020)
- [Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic Systems](https://mlsanity.com/paper/2311.07759/) (2023)

## Built on by (most cited first, 1 of 1)

- [CURVE: Learning Causality-Inspired Invariant Representations for Robust Scene Understanding via Uncertainty-Guided Regularization](https://mlsanity.com/paper/2601.20355/) (2026)

## Abstract (arXiv)

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