CIRS: A Multi-Agent Machine Learning Framework for Real-Time Accident Detection and Emergency Response

In today's world, the inventions of powerful automobile engines have increased the speed of transportation. As a result, the situation has become more vulnerable to fatal accidents. To reduce these fatalities, the Emergency Medical Service (EMS) has to be quick and efficient enough. To make emergency services faster, an Accident Detection and Notification System is required. The research work aims at the development of such robust Accident Detection and Notification System. The approach focuses on the development of two subsystems. One of the subsystems is Accident Detection System (ADS). Another subsystem is a Notification System. ADS is implemented on separate hardware and it is responsible for accident detection and estimation of severity and direction of an impact. The ADS hardware incorporates an accelerometer, a microcontroller and a Bluetooth device. The system also consists of a Raspberry Pi which acts as a central controller of the system and is connected to the ADS through Bluetooth. For the notification system, a GPS-GSM module and a web application connected with firebase cloud database are used. The web application developed fetches the data when received by the cloud database about the accident and displays it to the emergency authorities.

Internet of Things (IoT) and 5G are enabling intelligent transportation systems (ITSs). ITSs promise to improve road safety in smart cities. Therefore, ITSs are gaining earnest devotion in the industry as well as in academics. Due to the rapid increase in population, vehicle numbers are increasing, resulting in a large number of road accidents. The majority of the time, casualties are not appropriately discovered and reported to hospitals and relatives. This lack of rapid care and first aid might result in life loss in a matter of minutes. To address all of these challenges, an intelligent system is necessary. Although several information communication technologies (ICT)-based solutions for accident detection and rescue operations have been proposed, these solutions are not compatible with all vehicles and are also costly. Therefore, we proposed a reporting and accident detection system (RAD) for a smart city that is compatible with any vehicle and less expensive. Our strategy aims to improve the transportation system at a low cost. In this context, we developed an android application that collects data related to sound, gravitational force, pressure, speed, and location of the accident from the smartphone. The value of speed helps to improve the accident detection accuracy. The collected information is further processed for accident identification. Additionally, a navigation system is designed to inform the relatives, police station, and the nearest hospital. The hospital dispatches UAV (i.e., drone with first aid box) and ambulance to the accident spot. The actual dataset from the Road Safety Open Repository is used for results generation through simulation. The proposed scheme shows promising results in terms of accuracy and response time as compared to existing techniques.

Speed is one of the basic reasons for vehicle accident.Many lives could have been saved if emergency services could get accident information and reach in time.This project deals with accident detection system when an accident occurs it uses various components and alerts the Rescue team for help.An efficient automatic accident detection with an automatic notification to the emergency service with the accident location is a prime need to save the precious human life.The proposed system deals with accident alerting and detection.It reads the exact latitude and longitude of the vehicle involved in the accident and sends this information to nearest emergency service provider.The goal of the project is to detect accidents and alert the rescue team in time.

Abstract—An IoT-based accident detection and rescue system leverages real-time sensors, GPS, and communication networks to monitor vehicular activities and promptly detect accidents. Upon detecting a crash, the system automatically sends alerts with location data to emergency services and nearby respon- ders, reducing the response time and improving the chances of saving lives. The integration of sensors such as accelerometers, gyroscopes, and pressure detectors ensures accurate detection of accidents. Additionally, this system can notify the victim’s family and provide crucial information to hospitals for timely medical interventi Index Terms—real-time monitoring, emergency response, GPS, sensors, accelerometers

This advanced accident detection system significantly improves road safety by enabling timely identification of traffic incidents and rapid emergency response. Current road safety systems often fail to provide accurate and prompt detection of accidents, resulting in delayed emergency services and increased casualties. To address this issue, an advanced system utilizes Convolutional Neural Networks (CNNs) and the You Only Look Once (YOLO) model for real-time accident detection and classification. By analyzing RGB frames and incorporating optical flow data, this system effectively handles dynamic scenarios such as high-speed traffic, varying weather conditions, and complex vehicle movements. Furthermore, a Real-Time Notification feature integrates to automatically alert nearby hospitals and dispatch ambulances immediately upon detecting an accident. Training and testing use the dataset from Kaggle, which includes CCTV footage frames of accidents and non-accidents, ensuring robust performance. This system detects accidents in real- time and promptly sends notifications via email to alert nearby hospitals and dispatch ambulances.

Emergency response teams must coordinate their actions and goals in dynamic and demanding situations to ensure patient health and safety. It is a major challenge to measure and model such dynamic phenomenon under real world conditions. However, measuring coordination in real world conditions is necessary to better understand how teams function and to enhance their individual situation awareness, coordination, and learning. The use of sensor-based and video-based behavioral measurement of coordination within and between teams has been a suggested method for continuously recording dynamic, physical behavioral data streams related to proximity, movement or positioning to inform the team. However, very few studies have attempted to implement unobtrusive sensor-based data collection, especially in messy and challenging conditions. If we are to truly embrace the dynamic and complex nature of team-based behaviors and learning with new forms of data collection such as unobtrusive sensors, digital video and audio, we need to better understand the reality of the difficulties of collecting, processing and integrating these multiple digital data streams in real time to generate meaning in demanding, continually evolving, real world contexts such as emergency response. In the current paper, we strive to advance our understanding of the ground truth of leveraging sensor-based and other digital data streams to identify valid constructs, uncover meaningful indicators, and target relevant combinations of digital data streams to visualize and improve understanding of how teams coordinate and learn in team environments.

The main factor causing car accidents is speed. Many lives could have been saved if emergency services had been able to gather information about the tragedy and reach in time. The main project's objective is to develop an accident detection system that makes use of several different parts to alert the rescue crew about an accident. An efficient automated collision detection system that instantly alerts emergency services to the accident site is crucial for protecting valuable human life. The recommended approach deals with accident detection and notification. After reading it, it sends the affected vehicle's exact latitude and longitude to the nearest emergency response provider. The project's goal is to locate incidents and alert the rescue team as soon as possible.

The fast growing and very variable nature of road traffic presents professional challenges to public safety and effective traffic management in smart city surroundings. Frequent traffic accidents not only result in loss of life but also break urban motility systems, featuring the need for intelligent and automated accident detection explanations. This work presents a complete experimental study of existing traffic accident detection techniques, highlighting the current limitations of the state of the art paths while offering an in-depth analysis of common accident types such as crashes of rear-end, crashes at T-bone, & front accident simpact. To address these objections, the proposed work introduces a novel and lightweight deep learning architecture named AMSE-ADM, specifically designed for traffic accident detection of vision-based in smart city surveillance systems. The model effectively involves RGB video frames with flow of optical information to capture both spatial and temporal motion features of traffic scenes. The architecture influences advanced spatio temporal feature extraction mechanisms along with enhanced temporal dependency modeling across video sequences, enabling accurate detection of accident events. Extensive experimental evaluation demonstrates the effectiveness of the proposed approach, showing that the AMSE-ADM model outperforms other configurations. Furthermore, this study discusses critical challenges such as data imbalance, limited datasets, and constrained road and traffic scenarios, which significantly impact model generalization. Overall, the proposed framework paves the way for an efficient accident detection model in real-time and which is suitable for deployment on edge devices within smart city traffic infrastructures.

ABSTARCT - Road accidents cause over 1.3 million deaths each year around the world. Delays in getting help often make these situations worse, especially in far-off areas or fast-moving crashes where people can't call for aid. This paper describes an IoT system that uses AI to spot accidents. It combines data from an accelerometer to measure sudden jolts, GPS to find the exact spot, and GSM to send quick messages. The setup runs on a microcontroller connected to an MPU6050 accelerometer and NEO-6M GPS. It checks sensors every 100 milliseconds and starts working if the G-force goes above 3g. It figures out the jolt strength with the formula e goes above 3g. It figures out the jolt strength with the formula (x2+y2+z2)/9.8\sqrt{(x^2 + y^2 + z^2)/9.8}(x2+y2+z2)/9.8 and sends text alerts with Google Maps links using a SIM800L module. Tests on 50 fake crash setups show 85% success in spotting problems, alerts sent in less than 10 seconds, and GPS spots within ±5 meters. Adding checks for speed changes cuts wrong alerts below 18%, better than some older systems. This base setup opens the door for adding YOLO to check for damage or fire with pictures. It's a small tool that works on bikes or cars to make roads safer with fast, proof-based warnings Key Words: Accident detection, Accelerometer, GPS, IoT, Microcontroller, Image Processing, Real-time Detection.

Avian influenza A H5N1 continues to be a major threat to global public health as it is a likely candidate for the next influenza pandemic. To protect public health and avert potential disruption to the economy, the Hong Kong Special Administrative Region Government has committed substantial effort in preparedness for avian and pandemic influenza. Public health infrastructures for emerging infectious diseases have been developed to enhance command, control and coordination of emergency response. Strategies against avian and pandemic influenza are formulated to reduce opportunities for human infection, detect pandemic influenza timely, and enhance emergency preparedness and response capacity. Key components of the pandemic response include strengthening disease surveillance systems, updating legislation on infectious disease prevention and control, enhancing traveller health measures, building surge capacity, maintaining adequate pharmaceutical stockpiles, and ensuring business continuity during crisis. Challenges from avian and pandemic influenza are not to be underestimated. Implementing quarantine and social distancing measures to contain or mitigate the spread of pandemic influenza is problematic in a highly urbanised city like Hong Kong as they involved complex operational and ethical issues. Sustaining effective risk communication campaigns during interpandemic times is another challenge. Being a member of the global village, Hong Kong is committed to contributing its share of efforts and collaborating with health authorities internationally in combating our common public health enemy.

Abstract: Road accidents are a leading cause of injuries and fatalities worldwide, often exacerbated by driver drowsiness, delayed medical assistance, and inadequate real-time monitoring. This research presents the design and implementation of a Vehicle Accident Detection, Prevention, and Reporting System using the ESP32 microcontroller as the central processing unit. The system integrates an ADXL335 accelerometer to detect sudden vehicle impacts, an eye blink sensor to monitor driver alertness, and a GPSmodule for real-time location tracking and emergency alert transmission. The methodology involves continuous monitoring of vehicle motion and driver behaviour, automated detection of abnormal conditions, and immediate reporting to designated emergency contacts. Experimental testing demonstrates that the system can reliably identify accidents and drowsiness events, trigger preventive alerts, and send timely notifications with location data, thereby reducing response time in critical situations. The results indicate that the proposed system enhances road safety, minimizes potential accident-related injuries, and provides a practical framework for integrating IoT-based vehicle safety solutions. In conclusion, the ESP32-based approach offers a cost-effective, efficient, and scalable method for real-time accident detection and driver safety monitoring.

In November 2023, a 6.4-magnitude earthquake struck western Nepal, its most powerful since 2015, causing severe destruction in the Jajarkot, Rukum, and Salyan districts. The disaster left 154 dead, thousands homeless, and over 75 health facilities non-functional. In response to government's request for support, World Health Organization (WHO) Nepal applied the Emergency Response Framework (ERF) 2.0 to structure its operations, demonstrating how a WHO country office could operationalize ERF protocols during an acute-onset emergency. Through the activation of a whole-of-office incident management team (IMT), including an active field-level presence, WHO Nepal met key ERF performance standards such as staff safety, rapid situation analysis, and coordinated action. This experience addresses the limited documentation on the practical application of the ERF at the country level and offers insights into the preconditions necessary for its effective use in real-world emergencies. Following the Jajarkot response, the WHO country office institutionalized ERF-recommended practices across operational units, creating a model that was successfully replicated during the 2024 floods and landslides affecting three provinces. This experience underlines the idea that, while the ERF provides a strong structural foundation for emergency response, its effective implementation hinges on institutional readiness, specifically a capacitated and trained workforce, functional coordination mechanisms, and established operational infrastructures. WHO Nepal's response demonstrates how structured frameworks like the ERF can enhance preparedness and enable scalable, flexible responses in disaster-prone settings, offering a replicable approach for other WHO country offices and partners working in similar contexts.

The number of road accidents is increasing at a rapid pace which also affects the death rate that is proportional to the number of accidents occurring. This is a serious issue that has unfortunately become somewhat normalized due to how deeply embedded it is in the day-to-day lives of people, especially in developing countries. For any society to make significant progress, it is imperative to utilize our technical skills or otherwise to ensure personal safety before anything else. This includes reducing the time of response to an accident to the minimum, among other things. The development of the Internet of Things (IoT) based accident detection and response system is an attempt towards minimizing the rates of casualty and injuries occurring to both passengers and vehicles due to road accidents. The system is fully automated to be able to send emergency notifications to nearby hospital or fire station and an emergency contact in case of an accident. This is done via a combination of a vehicle device that has to be installed in the vehicle of the consumer and a remote server system that responds to the device accordingly. The motive is to provide the location of the vehicle and measure the severity of the accident so that help can be provided as fast as possible. From the data values collected from the sensors in the vehicle device prototype, we have tested the notification process successfully and also been able to assess the level of accident severity. We believe this system will reduce the response time of emergency responders and in the process improve both the traffic and health system of a society.

Speed is the one of the basic reasons for vehicle accident in India. To reducing the accidents the system is used that is `Accident Detection and Alert System'. This system alert when accident occurred and immediately contact to the emergency unit. Arduino UNO is processor of this system. It control the whole system. The input are Acceleration meter , Gyroscope, Vibration Sensor and output are LCD , Buzzer, GSM module, GPS module .When accident occurred their vibrations are detected by vibration sensor and accelerometer detects sudden changes in speed that may indicate an accident or crash. Gyroscope monitors changes in orientation or rotation of the vehicle, which can be useful for detecting rollovers or sudden swerving. GPS module provides real-time location data, which is useful for sending SMS of accident locations to emergency unit. GSM module get accident location and data send it to emergency unit. And Buzzer get ON and LCD display danger message

Vehicle accidents are major concern today in traffic management systems which result in an increasing number of deaths. India is currently in top position in road accidents around the globe. This leads to a huge number in future with growing population and other causes e.g. bad road conditions, reckless driving, delay in medical help, etc. To overcome these, a proper accident detection and notification system is required. Here comes our android app based on IoT sensors (Accelerometer, Gyroscope) of smartphones used in our daily lives. The app automatically detects an accident from sensory data and sends notification to emergency numbers so that victims receive immediate medical assistance. The app covers most of the solutions in this regard in post-accident scenarios, such as accident detection, sending notifications, and a few which are applicable for alerting people about accidents and handling traffic congestion scenarios.Later includes warning drivers while entering an accident-prone zone, suggesting an alternate route to avoid congestion in the accident occurrence area, etc. The performance is analyzed by delay time, false alarm rates and it outperforms the existing systems in terms of different features. Thus, it is user-friendly, reliable, and cost effective.