Pith. sign in

REVIEW

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2411.03225 v2 pith:VG6TE6AI submitted 2024-11-05 cs.AI cs.CV

classification cs.AIcs.CV
keywords dscenekgneurosymbolicdrivingscenestaskscapabilitiesdatasetsemerging
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original 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

Discussion (0). Continue with ORCID to comment.

Pith tools