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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.13668 |
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| _version_ | 1866911514953252864 |
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| author | Bradshaw, Jacob Alam, Mohsen Riahi Ainary, Bhanuja Kim, Minseo Salehi, Mohsen Amini |
| author_facet | Bradshaw, Jacob Alam, Mohsen Riahi Ainary, Bhanuja Kim, Minseo Salehi, Mohsen Amini |
| contents | Despite advances in assistive technologies, Blind and Low-Vision (BLV) individuals continue to face challenges in understanding their surroundings. Delivering concise, useful, and timely scene descriptions for ambient perception remains a long-standing accessibility problem. To address this, we introduce Audo-Sight, an AI-driven assistive system across Edge-Cloud that enables BLV individuals to perceive their surroundings through voice-based conversational interaction. Audo-Sight employs a set of expert and generic AI agents, each supported by dedicated processing pipelines distributed across edge and cloud. It analyzes user queries by considering urgency and contextual information to infer the user intent and dynamically route each query, along with a scene frame, to the most suitable pipeline. In cases where users require fast responses, the system simultaneously leverages edge and cloud processing pipelines. The edge generates an initial response quickly, while the cloud provides more detailed and accurate information. To overcome the challenge of seamlessly combining these outputs, we introduce the Response Fusion Engine, which fuses the fast edge response with the more accurate cloud output, ensuring timely and high-accuracy response for the BLV users. Systematic evaluation shows that Audo-Sight delivers speech output around 80% faster for urgent tasks and generates complete responses approximately 50% faster across all tasks compared to a commercial cloud-based solution -- highlighting the effectiveness of our system across edge-cloud. Human evaluation of Audo-Sight shows that it is the preferred choice over GPT-5 for 62% of BLV participants with another 23% stating both perform comparably. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_13668 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Audo-Sight: AI-driven Ambient Perception Across Edge-Cloud for Blind and Low Vision Users Bradshaw, Jacob Alam, Mohsen Riahi Ainary, Bhanuja Kim, Minseo Salehi, Mohsen Amini Distributed, Parallel, and Cluster Computing Computers and Society Human-Computer Interaction Despite advances in assistive technologies, Blind and Low-Vision (BLV) individuals continue to face challenges in understanding their surroundings. Delivering concise, useful, and timely scene descriptions for ambient perception remains a long-standing accessibility problem. To address this, we introduce Audo-Sight, an AI-driven assistive system across Edge-Cloud that enables BLV individuals to perceive their surroundings through voice-based conversational interaction. Audo-Sight employs a set of expert and generic AI agents, each supported by dedicated processing pipelines distributed across edge and cloud. It analyzes user queries by considering urgency and contextual information to infer the user intent and dynamically route each query, along with a scene frame, to the most suitable pipeline. In cases where users require fast responses, the system simultaneously leverages edge and cloud processing pipelines. The edge generates an initial response quickly, while the cloud provides more detailed and accurate information. To overcome the challenge of seamlessly combining these outputs, we introduce the Response Fusion Engine, which fuses the fast edge response with the more accurate cloud output, ensuring timely and high-accuracy response for the BLV users. Systematic evaluation shows that Audo-Sight delivers speech output around 80% faster for urgent tasks and generates complete responses approximately 50% faster across all tasks compared to a commercial cloud-based solution -- highlighting the effectiveness of our system across edge-cloud. Human evaluation of Audo-Sight shows that it is the preferred choice over GPT-5 for 62% of BLV participants with another 23% stating both perform comparably. |
| title | Audo-Sight: AI-driven Ambient Perception Across Edge-Cloud for Blind and Low Vision Users |
| topic | Distributed, Parallel, and Cluster Computing Computers and Society Human-Computer Interaction |
| url | https://arxiv.org/abs/2603.13668 |