Abstract

Conventional pedestrian navigation systems may not adequately account for the needs of users with mobility or sensory impairments, often suggesting routes with barriers such as stairs or poor pavement. This disclosure describes a system for accessibility-optimized navigation that can utilize a granular environmental data layer. This data layer can be populated from sources, for example, computer vision analysis, crowdsourcing, and municipal datasets, to annotate map data with accessibility attributes such as surface quality, ramp gradients, and tactile paving. A profile-based routing engine can use this data to compute paths by applying a dynamic cost function tailored to a user's specific needs, such as those of a wheelchair user or a visually impaired person. This approach allows for the generation of routes that consider detailed environmental factors, which may improve travel predictability and safety for individuals with specific accessibility requirements.

Publication Date

2026-01-05

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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