Summary of Trustworthy Automated Driving Through Qualitative Scene Understanding and Explanations, by Nassim Belmecheri et al.
Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations
by Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar, Helge Spieker
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A unified symbolic and qualitative representation for scene understanding in urban mobility is presented, called Qualitative Explainable Graph (QXG). QXG enables the interpretation of an automated vehicle’s environment using sensor data and machine learning models. It leverages spatio-temporal graphs and qualitative constraints to extract scene semantics from raw sensor inputs, such as LiDAR and camera data, offering an intelligible scene model that can be incrementally constructed in real-time. This makes it a versatile tool for in-vehicle explanations and real-time decision-making across various sensor types. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers created a new way to understand the environment around self-driving cars using computer sensors and machine learning models. They made a special graph that can be built piece by piece over time, which helps explain why the car is doing certain things, like stopping for pedestrians or turning at an intersection. This tool can be used in many different ways, such as telling passengers what’s happening, alerting people on foot or bike, and helping figure out what happened after something happens. |
Keywords
» Artificial intelligence » Machine learning » Scene understanding » Semantics
