Summary of Is It Safe to Cross? Interpretable Risk Assessment with Gpt-4v For Safety-aware Street Crossing, by Hochul Hwang et al.
Is it safe to cross? Interpretable Risk Assessment with GPT-4V for Safety-Aware Street Crossing
by Hochul Hwang, Sunjae Kwon, Yekyung Kim, Donghyun Kim
First submitted to arxiv on: 9 Feb 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 The paper introduces an innovative approach to help blind and low-vision individuals navigate street intersections safely. Traditional methods rely heavily on visual cues, which this method addresses by leveraging large multimodal models (LMMs) to interpret complex scenes. The LMM generates a safety score and scene description in natural language, supporting safe decision-making. The authors collected data containing multiview egocentric images from a quadruped robot and annotated them with corresponding safety scores. They evaluated the LMM’s ability to predict safety scores and describe scenes based on visual knowledge extracted from images and text prompts. The findings highlight the potential of LMMs for developing trustworthy systems that provide reliable decision-making support. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps people who can’t see or have trouble seeing navigate street intersections safely. It uses special computer models to understand what’s happening around them, like a traffic signal saying “go” or “stop”. The authors took lots of pictures from different angles and said which ones were safe or not. They then used these pictures to test the computer model and found that it can predict if something is safe or not based on what it sees. |
