ACCIDENT DETECTION AND REPORTING SYSTEMS IN IOT-ENABLED VEHICLES: TRENDS, TECHNOLOGIES, AND CHALLENGES

Abstract

There has been an increase in the usage of accident detection and response systems to improve road safety and reduce the number of fatalities brought on by traffic accidents. This article examines the use of Internet of Things (IoT) technology for real-time accident detection and categorization, as well as emergency communications in a vehicle environment. Continuous monitoring of vehicle dynamics is facilitated by a multi-sensor system, which includes accelerometers, a Global Positioning System (GPS), pressure sensors, and microphones. Data obtained is processed by microcontrollers and smartphones, and machine learning methods are employed to categorize the severity of an accident. When the detection has been made, alerts are sent to the emergency services through Global System for Mobile Communications (GSM) or 5G networks. The system also uses the Open-Source Computer Vision Library (OpenCV) in detecting visual accidents in surveillance videos and predictive analytics to predict the possibility of an accident using past and real-time data. Edge computing and cloud services are covered along with communication technologies to ensure effective data processing and a response with minimal delays. The paper also identifies existing barriers, such as sensor accuracy, false alarms, connectivity, and data privacy. Additionally, new trends that include the hybrid reasoning models, federated learning, and blockchain embedding are also suggested to improve the system's robustness. This study give a detailed picture of the realization, opportunities, and future of IoT-enabled accident detection systems in intelligent transportation and the safety of people.