Enhancing the Travel Experience for People with Visual Impairments through Multimodal Interaction: NaviGPT, A Real-Time AI-Driven Mobile Navigation System.

He Zhang, Nicholas J Falletta, Jingyi Xie, Rui Yu, Sooyeon Lee, Syed Masum Billah, John M Carroll
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Abstract

Assistive technologies for people with visual impairments (PVI) have made significant advancements, particularly with the integration of artificial intelligence (AI) and real-time sensor technologies. However, current solutions often require PVI to switch between multiple apps and tools for tasks like image recognition, navigation, and obstacle detection, which can hinder a seamless and efficient user experience. In this paper, we present NaviGPT, a high-fidelity prototype that integrates LiDAR-based obstacle detection, vibration feedback, and large language model (LLM) responses to provide a comprehensive and real-time navigation aid for PVI. Unlike existing applications such as Be My AI and Seeing AI, NaviGPT combines image recognition and contextual navigation guidance into a single system, offering continuous feedback on the user's surroundings without the need for app-switching. Meanwhile, NaviGPT compensates for the response delays of LLM by using location and sensor data, aiming to provide practical and efficient navigation support for PVI in dynamic environments.

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通过多模态交互增强视障人士的旅行体验:NaviGPT,人工智能驱动的实时移动导航系统。
针对视障人士的辅助技术取得了重大进展,特别是人工智能(AI)和实时传感器技术的集成。然而,目前的解决方案通常需要PVI在多个应用程序和工具之间切换,以完成图像识别、导航和障碍物检测等任务,这可能会阻碍无缝和高效的用户体验。在本文中,我们提出了NaviGPT,一个高保真原型,集成了基于激光雷达的障碍物检测,振动反馈和大语言模型(LLM)响应,为PVI提供全面和实时的导航辅助。与Be My AI和Seeing AI等现有应用程序不同,NaviGPT将图像识别和上下文导航引导结合到一个系统中,无需切换应用程序就可以对用户周围环境提供持续的反馈。同时,NaviGPT利用位置和传感器数据补偿LLM的响应延迟,旨在为动态环境下的PVI提供实用高效的导航支持。
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