NavAble: An AI-Driven Accessibility Mapping Platform for Disabled Citizens
Authors/Creators
Description
Urban accessibility remains a major challenge for people with disabilities due to inconsistent infrastructure, lack of reliable accessibility information, and limited awareness. This paper proposes NavAble, an AI-driven accessibility mapping platform designed to assess, rate, and visualize accessibility features within urban environments. The system integrates community- sourced data, machine-learning- based accessibility inference, and personalized route planning to support independent mobility for disabled citizens. NavAble utilizes a modern full-stack architecture incorporating React.js, Node.js, and Firebase, TensorFlow.js, and Leaflet maps to deliver scalable, real-time accessibility insights. The platform enhances civic inclusivity, supports governmental compliance monitoring, and empowers users through data-driven navigation.
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NavAble An AI-Driven Accessibility.pdf
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References
- 1. World Health Organization, World Report on Disability, 2011.
- 2. Government of India, Rights of Persons with Disabilities (RPWD) Act, 2016.
- 3. Accessible India Campaign (Sugamya Bharat Abhiyan), Govt. of India.
- 4. S. R. Subramanian, P. B. Venkat, and A. Rao, "AI-based assistive navigation systems for persons with disabilities: A survey," IEEE Access, vol. 9, pp. 112458–112473, 2021.
- 5. R. Tapu, B. Mocanu, A. Bursuc, and T. Zaharia, "A survey on assistive technologies for blind and visually impaired users," IEEE Access, vol. 6, pp. 507–531, 2018.
- 6. M. A. Husni, T. Ahmad, and A. Mahmud, "Smart wheelchairs and navigation systems: A comprehensive review," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 754–769, Apr. 2021.
- 7. A. Kacorri, J. Bigham, and S. Ashtari, "Crowdsourcing ground truth data for accessibility: Opportunities and challenges," in Proc. ACM CHI Conf. Human Factors in Computing Systems, 2017, pp. 1–13.
- 8. S. M. Santos, M. F. Silva, and F. Ribeiro, "Urban mobility challenges for wheelchair users: Accessibility evaluation using GIS," IEEE International Smart Cities Conference (ISC2), 2020, pp. 1–7.
- 9. H. Alghamdi and M. Van Der Walt, "A machine learning approach to detecting accessibility features in urban images," in IEEE International Conference on Machine Learning and Applications (ICMLA), 2022, pp. 482–489.
- 10. S. Zhang, L. Wang, and K. Ren, "Vision-based sidewalk accessibility assessment using deep learning," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 9220–9233, 2022