Is it safe to cross? Interpretable Risk Assessment with GPT-4V for Safety-Aware Street Crossing

• URL: http://arxiv.org/abs/2402.06794v2
• Date: Sat, 6 Jul 2024 15:36:23 GMT
• Title: Is it safe to cross? Interpretable Risk Assessment with GPT-4V for Safety-Aware Street Crossing
• Authors: Hochul Hwang, Sunjae Kwon, Yekyung Kim, Donghyun Kim,
• Abstract summary: This paper introduces an innovative approach that leverages large multimodal models (LMMs) to interpret complex street crossing scenes. By generating a safety score and scene description in natural language, our method supports safe decision-making for the blind and low-vision individuals.
• Score: 8.468153670795443
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Safely navigating street intersections is a complex challenge for blind and low-vision individuals, as it requires a nuanced understanding of the surrounding context - a task heavily reliant on visual cues. Traditional methods for assisting in this decision-making process often fall short, lacking the ability to provide a comprehensive scene analysis and safety level. This paper introduces an innovative approach that leverages large multimodal models (LMMs) to interpret complex street crossing scenes, offering a potential advancement over conventional traffic signal recognition techniques. By generating a safety score and scene description in natural language, our method supports safe decision-making for the blind and low-vision individuals. We collected crosswalk intersection data that contains multiview egocentric images captured by a quadruped robot and annotated the images with corresponding safety scores based on our predefined safety score categorization. Grounded on the visual knowledge, extracted from images, and text prompt, we evaluate a large multimodal model for safety score prediction and scene description. Our findings highlight the reasoning and safety score prediction capabilities of a LMM, activated by various prompts, as a pathway to developing a trustworthy system, crucial for applications requiring reliable decision-making support.

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