OmniAcc: Personalized Accessibility Assistant Using Generative AI

Siddhant Karki, Ethan Han, Nadim Mahmud, Suman Bhunia, John Femiani +1 more
9/8/2025
cs.AI

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Abstract

Individuals with ambulatory disabilities often encounter significant barriers when navigating urban environments due to the lack of accessible information and tools. This paper presents OmniAcc, an AI-powered interactive navigation system that utilizes GPT-4, satellite imagery, and OpenStreetMap data to identify, classify, and map wheelchair-accessible features such as ramps and crosswalks in the built environment. OmniAcc offers personalized route planning, real-time hands-free navigation, and instant query responses regarding physical accessibility. By using zero-shot learning and customized prompts, the system ensures precise detection of accessibility features, while supporting validation through structured workflows. This paper introduces OmniAcc and explores its potential to assist urban planners and mobility-aid users, demonstrated through a case study on crosswalk detection. With a crosswalk detection accuracy of 97.5%, OmniAcc highlights the transformative potential of AI in improving navigation and fostering more inclusive urban spaces.

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Cite this paper

@article{karki2025omniacc,
  title  = {OmniAcc: Personalized Accessibility Assistant Using Generative AI},
  author = {Siddhant Karki and Ethan Han and Nadim Mahmud and Suman Bhunia and John Femiani and Vaskar Raychoudhury},
  year   = {2025},
  eprint = {2509.07220},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.07220v1}
}

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