Summary of Towards Trustworthy Automated Driving Through Qualitative Scene Understanding and Explanations, by Nassim Belmecheri et al.
Towards Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations
by Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar, Helge Spieker
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 paper introduces the Qualitative Explainable Graph (QXG), a unified representation for scene understanding in urban mobility. The QXG uses spatio-temporal graphs and qualitative constraints to extract scene semantics from raw sensor inputs, such as LiDAR and camera data, offering an interpretable scene model. This approach enables interpreting an automated vehicle’s environment using sensor data and machine learning models, a crucial requirement for trustworthy automated driving. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how the Qualitative Explainable Graph (QXG) can help understand driving scenes and make automated vehicles more trustworthy. It uses special graphs and rules to look at what sensors like cameras and lasers see, and then makes sense of it all. This helps explain why a self-driving car made a certain decision, which is important for people who ride in them or walk near them. |
Keywords
» Artificial intelligence » Machine learning » Scene understanding » Semantics
