Summary of Generating Out-of-distribution Scenarios Using Language Models, by Erfan Aasi et al.
Generating Out-Of-Distribution Scenarios Using Language Models
by Erfan Aasi, Phat Nguyen, Shiva Sreeram, Guy Rosman, Sertac Karaman, Daniela Rus
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper proposes a framework for generating diverse Out-Of-Distribution (OOD) driving scenarios using Large Language Models (LLMs). The authors leverage LLMs’ zero-shot generalization and common-sense reasoning capabilities to construct a branching tree representing unique OOD scenarios. These scenarios are then simulated in the CARLA simulator, with performance evaluated through simulations and a diversity metric. Additionally, the paper introduces an “OOD-ness” metric quantifying how much the generated scenarios deviate from typical urban driving conditions. The authors also explore the capacity of Vision-Language Models (VLMs) to interpret and safely navigate OOD scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make self-driving cars safer by creating fake situations that car AI might encounter on the road, like unexpected turns or pedestrians crossing unexpectedly. They use special language models to come up with these scenarios, which are then tested in a computer simulation. The goal is to see how well these scenarios work and if they can help car AI avoid accidents. |
Keywords
* Artificial intelligence * Generalization * Zero shot
