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<p>In response to the escalating road accident rates in India and globally, this study presents an AI and IoT-enhanced emergency response system designed to advance vehicle and human safety, particularly for nighttime travel. The primary objectives are to address hazards associated with impaired windshield visibility and roadside collisions. The proposed system, "ClearView," integrates a chemical solution for instantly dissolving obstructive materials on windshields, coupled with an AI-driven, 360-degree monitoring framework. Utilizing deep learning models, this monitoring system leverages IoT devices, high-resolution cameras, and external airbags to predict collisions and deploy preventive measures. The methodology encompasses real-time data analysis, which enables proactive measures such as the rapid deployment of external airbags positioned around the vehicle. Compared to traditional safety mechanisms, the proposed model demonstrates a 25% improvement in collision prediction accuracy and a response time reduction of approximately 2.5&#xa0;s, thus enhancing overall safety. Results indicate that ClearView effectively reduces accident risks, offering a 95% success rate in maintaining visibility and an 85% reduction in collision impact. This integrated system represents a significant advancement in automotive safety by ensuring swift and precise responses to potential threats and fostering safer driving conditions. The implications of this research extend to various applications in vehicular safety, emphasizing the transformative potential of AI-driven, real-time monitoring solutions in reducing road accidents and enhancing emergency response capabilities.</p>

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Advanced AI-driven emergency response systems for enhanced vehicle and human safety

  • 期刊论文

  • 发表日期：

    2025年1月29日

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  • N. Satheesh,
  • N. Gopisankar,
  • S. Kumarganesh,
  • S. Anthoniraj,
  • S. Saravanakumar,
  • K. Martin Sagayam,
  • Binay Kumar Pandey,
  • Digvijay Pandey

摘要

In response to the escalating road accident rates in India and globally, this study presents an AI and IoT-enhanced emergency response system designed to advance vehicle and human safety, particularly for nighttime travel. The primary objectives are to address hazards associated with impaired windshield visibility and roadside collisions. The proposed system, "ClearView," integrates a chemical solution for instantly dissolving obstructive materials on windshields, coupled with an AI-driven, 360-degree monitoring framework. Utilizing deep learning models, this monitoring system leverages IoT devices, high-resolution cameras, and external airbags to predict collisions and deploy preventive measures. The methodology encompasses real-time data analysis, which enables proactive measures such as the rapid deployment of external airbags positioned around the vehicle. Compared to traditional safety mechanisms, the proposed model demonstrates a 25% improvement in collision prediction accuracy and a response time reduction of approximately 2.5 s, thus enhancing overall safety. Results indicate that ClearView effectively reduces accident risks, offering a 95% success rate in maintaining visibility and an 85% reduction in collision impact. This integrated system represents a significant advancement in automotive safety by ensuring swift and precise responses to potential threats and fostering safer driving conditions. The implications of this research extend to various applications in vehicular safety, emphasizing the transformative potential of AI-driven, real-time monitoring solutions in reducing road accidents and enhancing emergency response capabilities.