AI-Driven Emergency Response System For Vehicles: Enhancing Safety And Assistance

Abstract

This research introduces an innovative AI-driven emergency response system for vehicles designed to detect, analyse, and respond to emergency situations in real-time. By integrating multiple sensors, machine learning algorithms, and communication technologies, the proposed system minimizes response time and enhances the effectiveness of emergency interventions. The system employs a novel hybrid deep learning architecture that combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to process multimodal data streams from vehicle sensors, wearable devices, and environmental monitoring systems. Experimental results demonstrate a 37% reduction in emergency detection time and a 42% improvement in the accuracy of severity assessment compared to conventional systems. The implementation achieves a real-time processing capability with a latency of less than 200 milliseconds on standard automotive hardware platforms. This research contributes to advancing vehicle safety systems and provides a scalable framework for future intelligent transportation infrastructure.