Smart Vehicle Accident Detection and Reporting 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.

As it is observed these days, the roads are crowded with different kinds of vehicles, and as a result, accidents have become more dangerous every day. In this paper, a vehicle accident detection and notification system is designed by using road surveillance camera data and machine learning techniques to use the recorded footage from the surveillance cameras to detect the accident status, activate an alerting sound, and send the notification message as a Google email (Gmail) to the specialist. The system model was made by combining two algorithms, convolutional neural networks (CNN) and support vector machines (SVM), to make a CNN-SVM hybrid model, which was then trained using two datasets. The first dataset contains 4814 images with sizes (28, 28) and extensions (jpg), and the second dataset contains 990 images with sizes (32, 32) and extensions (jpg). The outcomes scores of the evaluation matrices are: accuracy for the first dataset is 99.74% and loss is 1.14%, and for the second dataset, accuracy is 98.88% and loss is 3.39%. When testing it with real-world data, it achieved its objectives in 30 seconds, with accuracy reaching 100%.

Road accidents have become an issue of major concern to the people. This paper presents an accident prevention mechanism developed through alcohol detection using an MQ3 alcohol sensor followed by automatic engine locking. The detection part uses an SW-420 vibration sensor to detect any sort of abnormal vibrations that may occur from a collision. This is accompanied by supervised deep learning CNN algorithms. The accident scene image is captured using a front camera built in the vehicle to be used by the deep learning model for accident prediction. Accident detection is followed by communication to the nearest emergency center using GPS and GSM modules.

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.

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.

Road accidents are often caused by short abnormal events, including traffic violations, abrupt change in vehicular motion, driver fatigue, etc. Observing an accident event from the right camera perspective plays a crucial role while detecting accidents. However, it may not be possible to capture such abnormal events from a limited camera perspective. We present a deep learning framework to analyze the accident events recorded from multiple perspectives. First, we estimate feature similarity in videos recorded from multiple perspectives. We then divided the video samples into high and low feature similarity groups. Next, we extract spatio-temporal features from each group using two-branch DCNNs and fuse them using a rank-based weighted average pooling strategy followed by classification. We present a new road accident video dataset (MP-RAD), where each accident event is synthetically generated and captured from five independent camera perspectives using a computer gaming platform. Most of the existing road accident datasets use egocentric views or they are captured in fixed camera setups. However, our dataset is large and multi-perspective that can be used to validate ITS-related tasks such as accident detection, accident localization, traffic monitoring, etc. The dataset contains 400 accident events with a total of 2000 videos. We provide temporal annotations of all videos. The proposed framework and the dataset have been cross-validated with latest accident detection baselines trained on real-world road accident videos and vice-versa. The sub-optimal detection accuracy obtained using the baselines indicates that the proposed framework and the dataset can be useful for ITS related research. Code and dataset is available at: https://github.com/draxler1/MP-RAD-Dataset-ITS-

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.

Nowadays, the increase of using vehicles on the roads may impose several issues in delivering emergency help during an accident of which may threaten human lives involved greatly. During the accident, many people may lose their lives because of the delayed medical services, or the ambulance is stuck in traffic jam, and accident victims might die while waiting for the first aid kits and medical assistance. In this work, we propose a solution in providing the medical assistance and first aid kits before the arrival of the ambulance, coined as ECBA (Everyone Can be Ambulance). ECBA system is a software and a hardware system that works together for accident detection, report and medical assistance system. It is designed to provide automatic accident reporting system and to save people's lives.

People lose their lives every day in densely populated nations like India due to accidents and inadequate emergency services.Due to delayed alerts and incomplete knowledge of the precise accident site, some rescue personnel has trouble getting to the injured.A paradigm shift in public and private services was brought about by the emergence of the mobile phone and Internet of Things (IoT) businesses, which also reshaped how people communicate.The invention of transport has greatly impacted our daily lives and made many of our tasks considerably simpler.The use of GPS in an IoT-based vehicle accident detection system has gained popularity.This method sends a text message to the guardian's cell phone number when an accident occurs.Guardians can obtain latitude and longitude information.These values can be used to accurately coordinate the accident's location.Drunk or reckless driving, among other factors, is to blame for the majority of traffic accidents.The goal of this project is an effort made to define a monitoring system for vehicles that will alert the owner or driver of the vehicle in the case of an accident, drunk driving, reckless driving, or low fuel.The guardian inquires about the car's whereabouts in an emergency from any location.

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.

Driving automation leads drivers to be disengaged by dynamic driving tasks, but the need for the driver to monitor traffic environment is existed in a partial automation. Monotonous road situations can produce drowsiness, resulting in poor driving performance. Conversation in automated vehicles is expected to mitigate fatigued driving. However, few studies have examined effects of conversation. This paper reports a driving simulator study describing impacts of passenger conversation on driver fatigue and driving performance regarding the relationship between a driver and a passenger. Participants were categorized into two groups: a passenger's friends (ConvR) and people who had not met the passenger before (ConvNR) to examine whether the acquaintance with the passenger affects driver behaviour, driving performance, fatigue and workload. The results showed that drivers in ConvNR felt lesser sleepy and found it easier to drive than the drivers in ConvR, but no difference of driver behaviour and driving performance was observed between two groups. In addition, regardless of the relationship, making casual conversation led decreased fatigue and workload in comparison with solo driving without conversation. The practical implication for further conversation methods to regard driver fatigue and driving performance in the automated vehicle is discussed.

Driver fatigue causes serious damages amongst all other road accidents. Around 20% of fatal road accidents involve driver fatigue. This paper describes a modern approach which will detect driver fatigue considering most of the fatigue symptoms. Eye closure, yawning, head tilting is the major symptoms of fatigue behavior. Inattentive vehicle movement in the road under fatigue condition is also accountable for driver fatigue. The goal of this paper is to detect these symptoms for better driving condition in the road. These symptoms are monitored by two cameras. This paper proposes a robust system where facial expressions, head tilting and lane departure for fatigue will be detected collectively. Experimental results of the proposed method are compared with the previous method. The results show good accuracy and reliable performance to avoid road accidents compared to the previous method. The proposed system is very simple and avoids any complexity.

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.

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.