AURANAV: Safety-Centric Navigation through Real-Time Familiarity and Social Awareness

Traditional navigation systems often overlook dynamic social contexts, affecting users’ sense of safety and comfort in unfamiliar settings. This challenge requires integrating personalized real-time social data into route planning on resource-constrained mobile and wearable platforms. This paper introduces AURANAV, enhancing navigation by incorporating real-time recognition and individualized social patterns. AURANAV includes two main modules: (i) Social Topology Enhanced Navigation uses real-time familiar individual detection to create dynamic safety corridors by adjusting path costs with an influence field-based metric in A* search; and (ii) Personalized Path Memory mines long-term familiarity data to build heatmaps, offering route recommendations aligned with user habits and comfort zones. This system uses a modular edge-cloud architecture for low-latency sensor data processing and scalable analysis of social-spatial information. The evaluation shows that AURANAV improves routing safety by 17-20% and reduces travel time by 5%, maintaining immediate responsiveness with negligible latency. AURANAV offers a framework for socially aware navigation systems that evolve into personalized and context-aware systems.