ACCIDENT DETECTION AND ALERT SYSTEM

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 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

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.

Nowadays, traffic accidents are one of the main reasons of fatalities in the world, many lives can be saved by reducing the time between the occurrence of an accident and the arrival of first help at its location. In research literature, numerous accident detection and notification systems were proposed to solve this problem. Including built-in unit, black-box, and mobile phone-based solutions. Our research has been devoted to design a black-box system that integrates Global System for Mobile Communication (GSM) and Global Positioning System (GPS) technologies to locate and send a Minimum Set of Data (MSD) containing important information about the accident collected using different sensors such as the accident type, longitude and latitude, time and speed of the vehicle to the emergency services (Hospitals, civil protection, and police). This system is developed based on the Raspberry Pi 3 in order to provide both low cost and high system performances.

Traffic accidents are a thing that cannot be avoided by vehicle users. Many bus-related managers ignore vehicle engine maintenance from minor matters such as leaky fuel drains, supposedly replaceable vehicle brakes and short-circuit electrical connections. So in this study the author will make a fire detector and collision. The result of this tool will work when the MQ-135 sensor detects smoke, the fire sensor detects a fire and push button detects a collision. In addition to this tool also features GSM SIM800L module to provide information to the security agency. To facilitate the operation of the whole system is controlled by arduino uno microcontroller. After doing research MQ-135 sensor detects smoke when smoke comes into contact with the sensor, in addition to MQ-135 sensor in fire detection system used fire sensor module DFR0076, this module can detect fire up to distance 16 cm. While in the accident detection system (collision) is used push button. This tool system can be concluded that the overall design of the accident detection and fire detection equipment works well and the program used successfully applied to the system tool in accordance with the idea of design.

The goal of the manuscript is to propose a Vehicle Accident recognition and Smart Reporting System. The system is comprised of two major parts; Accident Detection (AD) and Smart Reporting System (SRS). The system contains three Switches / Buttons for ambulance, police, and Victim's home respectively. The Victim cause these Switches to choose non-required services based on his/her needs, by pressing particular Switches. When an accident commits, Accident Detection (AD) takes place by enabling the Piezoelectric Force Sensor, which alerts the Microcontroller that is connected to GPS and GSM module. GPS component finds the vehicle's exact spot where the accident occurred and the Microcontroller waits for a certain time to get a response from the Victim, based on the response of the Victim Smart Reporting System (SRS) comes into the picture and sends a message to desired services using the GSM module. Plenty of accidents occurs on highways or outskirts, sometimes there will be no helping hands to take the Victim to the hospital, or accident information will not reach the nearest ambulance, police authority, or Victim's home at the correct time, due to this many people are dying. As a solution, many proposals are available to identify the spot of the accident and to report it to the required people but if the Victim is perfectly alright or having some minor issues then, the Victim may not be in need of all these services then, providing these services will leads to waste of time, fuel and simply it creates tension. This project is designed to tackle both the cases. It helps the Victim to save his life without any help from the surrounding people. The entire procedure is done with Internet of Things (IoT) technology.

A mobile tracking application for emergency ambulance services is proposed in this paper with various challenges in vehicle tracking and monitoring system. The system issues alert messages in case of emergency. The alert system is essential to provide information about the location of the accident or driver's location. In the conventional method, the ambulance driver could not reach the accident location on time due to heavy traffic, leading to loss of many lives. With the current technological advancements in smartphones and mobile apps, a mobile app for tracking ambulance in real time is proposed in this paper. This application will let the ambulance driver record its availability and location. The user who executes the emergency will book the ambulance and log in to the user. The user location and nearby ambulances will be precisely pointed out on the Google map. Once the patient is boarded, the location of the ambulance will be recorded and sent to the traffic police present at the traffic signal. The driver then selects the nearby hospital on the map, which helps to treat the patients on time. The location of ambulance is tracked by the navigator based on Web Services. This technology will help the ambulance location to be updated in the database. In addition, the traffic signal can be controlled based on the upcoming ambulance vehicle thus providing a traffic less route.

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

Road traffic accidents remain a leading cause of fatalities worldwide, and the consequences are considerably worsened by delayed detection and emergency response. Although several machine learning-based approaches have been proposed, accident detection systems are not widely deployed, and most existing solutions fail to handle the growing complexity of modern traffic environments. This study introduces Collaborative Intelligence for Road Safety (CIRS), a novel, multi-agent, machine-learning-based framework designed for real-time accident detection, semantic scene understanding, and coordinated emergency response. Each agent in CIRS is designed for a distinct role perception, classification, description, localization, and decision-making, working collaboratively to enhance situational awareness and response efficiency. These agents integrate advanced models: YOLOv11 for high-accuracy accident detection and VideoLLaMA3 for contextual-rich scene description. CIRS bridges the gap between low-level visual analysis and high-level situational awareness. Extensive evaluation on a custom dataset comprising (5200 accident, 4800 nonaccident) frames demonstrates the effectiveness of the proposed approach. YOLOv11 achieves a top-1 accuracy of 86.5% and a perfect top-5 accuracy of 100%, ensuring reliable real-time detection. VideoLLaMA3 outperforms other vision-language models with superior factual accuracy and fewer hallucinations, generating strong results in the metrics of BLEU (0.0755), METEOR (0.2258), and ROUGE-L (0.3625). The decentralized multi-agent architecture of CIRS enables scalability, reduced latency, and the timely dispatch of emergency services while minimizing false positives.

GPS (Global Positioning System) has become an integral part of a vehicle system which provides speed, time, direction etc besides the navigation data. Speed is one of the primary attributes of vehicle accident. Many lives could have been saved if emergency service could receive accident information timely. This paper proposes to detect an accident from the map matched position of a vehicle by utilizing the GPS speed data and map matching algorithm and send accident location to an Alert Service Center. The GPS provides speed and position in every 0.1 second. The position data will be used in the map matching algorithm to locate the vehicle on the road. The present speed will be compared with the previous speed in every 0.1 second through a Microcontroller Unit. Whenever the speed will be falling below the safe calculated threshold speed, the system will generate an accident situation. It will check the vehicle location from map matching module and generate an accident situation if the vehicle is found outside the road network. This will reduce the false accident detection drastically. The map matched accident location is then sent by utilizing the GSM network. The proposed system will save many accident victims with timely rescue.

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.

The vehicle safety and accident detection system using ESP32 of an by using IOT technology is an a comprehensive solution designed to enchance road safety by detecting accidents in real time to emergency services,drivers,and authorities.This system integarates various sensors, including sp02,GPS,GSM,and vibration sensor,with a microcontroller to monitor vehicle condition and detect such as sudden deceleration,collisions, or fire hazards.upon detecting an accident,the system triggers automatic notification and sends critical data such as the vehicle location and status to emergency contacts via GSM or WI-FI.In addition to accident detection the system also offers continuous monitoring of vehicle health,preventing potential break downs.This IOT based solution ensures faster improved vehicle safety and provide valuable data for accident analysis,making it a vital tool for modern transportation systems. Keywords- ESP32, GPS, SP02, VIBRATION SENSOR, LCD.

Enormous advance has proven throughout the years in the area of traffic surveillance by the growth of intelligent traffic video surveillance system. In the current work, through the traffic videos, the traffic video surveillance automatically keyed out the vehicles like ambulance and trucks, which in turn assisted us in directing the vehicles at the time of emergency. Nevertheless, it doesn’t provide us a vital solution for the regulating the traffic. Moreover, this idea just identifies the vehicles, but it couldn’t notice the accidents expeditiously. Therefore in the proposed work, expeditious traffic video surveillance and monitoring system are presented along with dynamic traffic signal control and accident detection mechanism. Hybrid median filter has been utilized at the beginning for pre-processing of traffic videos, and to remove the noise. Hybrid support vector machine (SVM with extended Kalman filter) has been utilized to chase the vehicles. Next, the histogram of flow gradient features are drew-out to categories the vehicles. According to the traffic density and through video files, vehicles are computed, and then for emergency vehicles, the traffic signal gets switched dynamically. To realize the arrival of ambulances, the cameras have been set to catch traffic videos minimum at 500 m of the signal and deep learning neural networks has been employed. Hence dynamic signal control has been incorporated expeditiously. Likewise, multinomial logistic regression has been utilized in real-time live streaming videos, to identify the accidents correctly. The observational solution shows that the proposed intelligent traffic video surveillance system render expeditious dynamic control of traffic signals and it raises the identification of accidents correctly.

Physical activity is a crucial factor for maintaining not only physical health status, but vast amounts of research have shown its link with better mental health. Supporting the use of gyms for the safety of its practitioners is vital in the new norm and living with COVID-19. Therefore, in this study we show research supporting the development of a framework for a Total Safe-Care Fitness Solution based on a multimodal COVID-19 tracking system integrating computer vision and data from wearable sensors. We propose a framework with three areas that need to be integrated: a COVID-19 vaccine and health status recognition system (QR code scan prior to entry to the gym, and physiological signals monitored by a smart-band and a health questionnaire filled in prior to entry to the gym); an accident detection system (video and smart-band based); and a gym-user digital tracking system (CCTV and smart-band based). We show the proposed architecture for the integration of these systems and provide practical tips on how to implement it in testbeds for feasibility testing. To the best of our knowledge, this is the first proposed COVID-19 tracking system of use in gyms that includes a predictive model for accident detection for safer exercise participation through health monitoring.