Two-Way Human-Like Navigation System Using Large Language Models

This paper presents a novel interactive navigation system using multimodal large language models (LLMs) to deliver intuitive landmark-based turn-by-turn directions through both-way human-agent interaction. Comparative user studies reveal that this cognitive navigation approach enhances driving behavior and user satisfaction over traditional numerical systems. Analysis of steering, acceleration, and braking patterns shows significant improvements (p < 0.01) with large effect sizes (d > 0.7). Subjective feedback indicates that 80% of participants favored the cognitive system, citing reduced stress, improved focus and better anticipation.