AI vs. traditional navigation systems: enhancing efficiency and reducing cognitive burden for visually impaired users
LiYan Bu, YiZhuo Hou, WeiCheng Pan, Hong Chen, Bo Cui, Hesen Li
Abstract
People with visual impairments face significant challenges during travel, and traditional navigation systems often fall short of addressing their specific needs. Existing assistive technologies, including tactile paths and basic voice navigation, struggle with low efficiency, high error rates, and heavy cognitive and psychological burdens. With the advancement of artificial intelligence (AI), AI-based navigation systems have emerged as promising alternatives by offering real-time environmental perception, personalized feedback, and adaptive interaction. This study compared AI navigation systems, existing assistive systems, and traditional navigation tools using five performance metrics: navigation time, error rate, task completion rate, psychological burden, and cognitive load. A four-week experiment involving 36 visually impaired participants revealed that the AI navigation system significantly improved task efficiency and reduced both cognitive and psychological stress. Notably, participants with total blindness outperformed semi-blind participants in task performance, emphasizing the effectiveness of voice-based navigation. These findings highlight the superior adaptability and user-centered design of AI navigation systems and underscore their value in enhancing travel independence and quality of life for the visually impaired. The results provide empirical support for advancing AI-driven assistive technologies and contribute to future developments in inclusive, barrier-free navigation design.