Edge Computing And NLP In AI -Enabled Vehicle Emergency Systems: Reducing Response Time And Saving Lives - Prasad, Donthabhaktuni Rama Krishna Upendra; Subramanyam, Kodukula; Malleswari, D.Naga (Metallurgical and Materials Engineering, 2025)
This paper presents a novel AI-driven emergency response system for vehicles that significantly improves post-crash emergency assistance by enhancing accident detection accuracy and response time optimization. The proposed system integrates multi-modal sensor data with deep learning techniques to accurately detect accidents, assess injury severity, and optimize emergency resource allocation. Experimental results demonstrate a 94.7% accuracy in crash detection, 89.3% accuracy in injury severity prediction, and an average 8.2-minute reduction in emergency response time compared to traditional systems. The framework incorporates vehicle sensor networks, edge computing, and natural language processing to create a comprehensive emergency response ecosystem. Field tests conducted across diverse environmental conditions validate the system's robustness and reliability, suggesting significant potential for reducing road traffic fatalities through AI-enhanced emergency response mechanisms.