Advancing Road Safety Through Intelligent Vehicle Accident Detection and Reporting Enabled by IoT Innovation

The escalating incidence of road accidents poses a significant challenge to public safety, often exacerbated by delays in emergency response. This research introduces an IoT-based intelligent system designed to revolutionize accident management by providing real-time data and critical information to emergency services. By seamlessly integrating Raspberry Pi and Arduino microcontrollers with advanced sensors such as force-sensitive resistors (FSRs), GPS, and potentially additional sensors like accelerometers and gyroscopes, the system accurately detects accidents, assesses their severity, and captures comprehensive data, including passenger images, vehicle details, and precise accident location. Upon impact, the system rapidly transmits this data, along with pre and post-accident images, to a designated emergency response center via a secure and reliable communication network. This swift dissemination of critical information empowers emergency responders to make informed decisions, optimize resource allocation, and prioritize response efforts, ultimately saving lives and minimizing the impact of accidents. The system's intelligence lies in its ability to not only detect accidents but also to assess their severity based on sensor data analysis, enabling a more targeted and efficient emergency response. Furthermore, the system's potential to collect valuable data on accident patterns and causes can contribute to the development of preventive measures and road safety strategies. By leveraging the power of IoT technology, this research aims to significantly enhance road safety and transform accident management practices.

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Publication Details

Journal
NUML International Journal of Engineering and Computing
Published
2024-10-14
DOI
https://doi.org/10.52015/nijec.v3i1.66
Primary Topic
Traffic Prediction and Management Techniques
Type
article
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article

Advancing Road Safety Through Intelligent Vehicle Accident Detection and Reporting Enabled by IoT Innovation

Abdullah Abdullah, Rahat Ullah Khattak
NUML International Journal of Engineering and Computing
Traffic Prediction and Management Techniques
article

Advancing Road Safety Through Intelligent Vehicle Accident Detection and Reporting Enabled by IoT Innovation

Abdullah Abdullah, Rahat Ullah Khattak
article en

Abstract

The escalating incidence of road accidents poses a significant challenge to public safety, often exacerbated by delays in emergency response. This research introduces an IoT-based intelligent system designed to revolutionize accident management by providing real-time data and critical information to emergency services. By seamlessly integrating Raspberry Pi and Arduino microcontrollers with advanced sensors such as force-sensitive resistors (FSRs), GPS, and potentially additional sensors like accelerometers and gyroscopes, the system accurately detects accidents, assesses their severity, and captures comprehensive data, including passenger images, vehicle details, and precise accident location. Upon impact, the system rapidly transmits this data, along with pre and post-accident images, to a designated emergency response center via a secure and reliable communication network. This swift dissemination of critical information empowers emergency responders to make informed decisions, optimize resource allocation, and prioritize response efforts, ultimately saving lives and minimizing the impact of accidents. The system's intelligence lies in its ability to not only detect accidents but also to assess their severity based on sensor data analysis, enabling a more targeted and efficient emergency response. Furthermore, the system's potential to collect valuable data on accident patterns and causes can contribute to the development of preventive measures and road safety strategies. By leveraging the power of IoT technology, this research aims to significantly enhance road safety and transform accident management practices.

NUML International Journal of Engineering and ComputingVol. 3(1)
Federal Urdu University (PK)
Good health and well-being
Openalex Percentile: Top 82%
Traffic Prediction and Management Techniques
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Advancing Road Safety Through Intelligent Vehicle Accident Detection and Reporting Enabled by IoT Innovation — Abdullah Abdullah, Rahat Ullah Khattak · NUML International Journal of Engineering and Computing (2024) | TGRS Research Map | TGRS