ForeSightGuide: An Anticipatory Framework toward Accurate and Low-Redundancy Guidance for the Visually Impaired

2026-08-19

AI Summary

为解决视障者使用电子旅行辅助工具时遇到的认知过载问题，ForeSightGuide通过结合语义场景理解和预测性风险评估提供准确且低冗余的指导。

Abstract

Electronic travel aids are pivotal for the independent mobility of the visually impaired. While Vision-Language Models (VLMs) offer rich environmental understanding, they often suffer from excessive false positives in dynamic scenarios, leading to cognitive overload. To address this, we present ForeSightGuide, an anticipatory assistive guidance framework that couples semantic scene understanding with predictive hazard assessment. Unlike reactive systems, ForeSightGuide leverages the reasoning capabilities of VLMs to anticipate obstacle motion, effectively filtering out non-threatening objects to provide concise, actionable guidance. To validate our approach, we introduce a novel dataset captured in complex, dynamic real-world traffic scenes, designed to benchmark predictive capabilities. Extensive experiments on both public benchmarks and our proposed dataset demonstrate that ForeSightGuide achieves state-of-the-art performance. Notably, it significantly mitigates information overload by reducing redundant alerts to 0.299 per guidance output while maintaining a low missed-hazard rate of 0.112, proving its efficacy for safe walking assistance.

Problem

• Visually Impaired
• Vision-Language Models
• Cognitive Overload
• False Positives
• Dynamic Scenarios

Innovation

• Anticipatory Guidance
• Semantic Scene Understanding
• Predictive Hazard Assessment
• Vision-Language Models
• Cognitive Overload Mitigation