Summary of Knowledge Graphs Of Driving Scenes to Empower the Emerging Capabilities Of Neurosymbolic Ai, by Ruwan Wickramarachchi et al.
Knowledge Graphs of Driving Scenes to Empower the Emerging Capabilities of Neurosymbolic AI
by Ruwan Wickramarachchi, Cory Henson, Amit Sheth
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Neurosymbolic AI approach has demonstrated enhanced capabilities in various tasks, including perception and cognition. To support the evaluation of this approach, a real-world benchmark dataset is introduced. The DSceneKG knowledge graph suite is constructed from high-quality scenes from multiple open autonomous driving datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Neurosymbolic AI is a new way to make computers smarter. It helps them understand what’s going on around them and make better decisions. But we need special test data to see how well it works. That’s where DSceneKG comes in – a collection of pictures and information about real driving scenes. We built this dataset from many open sources and used it for testing different tasks. |
Keywords
» Artificial intelligence » Knowledge graph
