Information on Cognitive AI Assistants for Visually Impaired Navigation Support

Navigational independence remains one of the most pressing challenges faced by visually impaired individuals. Traditional tools such as white canes and guide dogs, while valuable, offer limited assistance in dynamic urban environments. Recent advancements in Artificial Intelligence (AI), particularly cognitive AI systems, have led to the development of intelligent navigation assistants capable of perceiving, understanding, and responding to real-world scenarios. These systems leverage computer vision, natural language processing, spatial mapping, and edge computing to provide real-time situational awareness and guidance to users. This paper explores the core technologies behind cognitive AI assistants for visually impaired navigation, examining their architecture, sensory integration, and user interaction models. It discusses use cases in indoor and outdoor mobility, obstacle avoidance, and context-aware pathfinding. Case studies from pilot programs and commercial applications are presented to illustrate effectiveness and accessibility. Ethical and societal considerations such as privacy, autonomy, and inclusivity are critically analyzed. Technical challenges including environmental variability, localization accuracy, and real-time processing are addressed. Finally, the paper outlines future innovations in multimodal input integration, wearable AI devices, and community-informed design. Cognitive AI assistants hold the potential to enhance independence, safety, and quality of life for individuals with visual impairments in an increasingly digital world.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-03
DOI
https://doi.org/10.5281/zenodo.15334021
Primary Topic
Tactile and Sensory Interactions
Type
article
Field-Weighted Citation Impact
0.00
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article

Information on Cognitive AI Assistants for Visually Impaired Navigation Support

Zenodo (CERN European Organization for Nuclear Research)
Tactile and Sensory Interactions
article

Information on Cognitive AI Assistants for Visually Impaired Navigation Support

article en

Abstract

Navigational independence remains one of the most pressing challenges faced by visually impaired individuals. Traditional tools such as white canes and guide dogs, while valuable, offer limited assistance in dynamic urban environments. Recent advancements in Artificial Intelligence (AI), particularly cognitive AI systems, have led to the development of intelligent navigation assistants capable of perceiving, understanding, and responding to real-world scenarios. These systems leverage computer vision, natural language processing, spatial mapping, and edge computing to provide real-time situational awareness and guidance to users. This paper explores the core technologies behind cognitive AI assistants for visually impaired navigation, examining their architecture, sensory integration, and user interaction models. It discusses use cases in indoor and outdoor mobility, obstacle avoidance, and context-aware pathfinding. Case studies from pilot programs and commercial applications are presented to illustrate effectiveness and accessibility. Ethical and societal considerations such as privacy, autonomy, and inclusivity are critically analyzed. Technical challenges including environmental variability, localization accuracy, and real-time processing are addressed. Finally, the paper outlines future innovations in multimodal input integration, wearable AI devices, and community-informed design. Cognitive AI assistants hold the potential to enhance independence, safety, and quality of life for individuals with visual impairments in an increasingly digital world.

Zenodo (CERN European Organization for Nuclear Research)
Sustainable cities and communities
Openalex Percentile: Top 48%
Tactile and Sensory Interactions
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Information on Cognitive AI Assistants for Visually Impaired Navigation Support · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS