Implementation of Vehicle Safety and Accident Detection System Using ESP32

Abstract— NavMorpho – Smart Vehicle Safety and Monitoring System is a cutting-edge, real-time intelligent safety solution designed to enhance road safety and reduce accident-related fatalities. The system aims to provide automatic crash detection, live vehicle tracking, obstacle monitoring, and instant emergency alert transmission.This innovative solution integrates IoT technology with advanced sensors to monitor vehicle movement and detect accidents in real time. The system provides immediate emergency communication by automatically sending live GPS location data to registered emergency contacts when a crash is detected.The ESP32-based system consists of multiple hardware components including an MPU6050 accelerometer and gyroscope for crash detection, ultrasonic and IR sensors for obstacle monitoring, and a GPS module for real-time location tracking. The system connects to the internet using WiFi, enabling real-time data transmission to a cloud-based backend developed using Node.js and MongoDB.The cloud platform processes vehicle data, stores crash history, and updates a live monitoring dashboard. Upon detecting a crash, the system automatically triggers WhatsApp alerts containing the vehicle’s live location. A manual emergency button is also integrated to allow users to send instant alerts when needed.NavMorpho provides a smart, scalable, and life-saving solution for modern vehicles by combining IoT hardware, cloud computing, and real-time communication technologies. The system has strong potential to transform vehicle safety systems into intelligent, automated emergency response platforms. Keywords: ESP32, MPU6050, NEO-6M GPS Module, Ultrasonic Sensor

The number of automobiles has also grown quickly to meet the enormous population. Additionally, this resulted in an increase in accidents. The accident prevention strategies now in use are all static and dated. Additionally, there is no reliable accident detection system. Timely medical aid can help in saving lives. Drive Sentinel is an advanced vehicle safety and monitoring system aimed at reducing road accidents and improving driver accountability. The system integrates driver drowsiness detection, real-time GPS tracking, and GSM-based communication to create a comprehensive in-vehicle safety solution. Using computer vision and embedded sensors, Drive Sentinel monitors the driver’s eye movements and facial cues to detect signs of fatigue or inattention. Upon detection of drowsiness or a potential accident, the system triggers immediate visual and audio alerts to wake the driver and simultaneously sends an SMS with the vehicle’s GPS location to registered emergency contacts using a GSM module. The hardware implementation involves a Raspberry Pi or microcontroller, camera module, GPS receiver, GSM modem, and buzzer, all working in coordination to ensure real-time monitoring and response. The use of machine learning for face and eye detection enhances accuracy in identifying driver fatigue. This system can be particularly beneficial for long-haul drivers, public transport operators, and fleet management companies. By combining embedded systems, IoT, and artificial intelligence, Drive Sentinel contributes to building safer roads and reducing the number of accidents caused by human error. Keywords- Drive Drowsiness Detection, Accident Prevention, Vehicle Safety System, GPS Tracking, GSM Alert System, Real-Time Monitoring, Image Processing, IoT in Transportation

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%.

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.

This paper presents the implementation of biometric access control using a fingerprint for the safety and security system of Electric Vehicles (EV). The EVs play a vital role in the minimization of the emission of carbon footprint into the atmosphere. Right now, researchers are giving more attention to the design and development of advanced EVs with more safety and security systems. As the population is gradually increasing, there is a positive correlation between the number of road traffic accidents deaths. These road accidents are unpredictable. The main reasons for road traffic accidents are negligence in driving, drunk and drive also a minor cause is being on the stand and driving. At the same time, the vehicle owner has afraid more because day-by-day vehicle robbery is keep on increasing, which intends to develop anti-theft devices for EVs. In the next few years, with tremendous growth in the electric vehicle market, it is important to design EVs with high security and safety systems. In this paper, an advanced safety and security system is proposed for EVs which ensures the stand removing and vehicle owner through fingerprint recognition. The proposed safety and security systems are implemented and tested in E-Bike the efficacy of the system was found satisfactory.

The incidence of road accidents is quite high. This is due to driver negligence. Based on these problems, a driving accident detection system was created. The system uses vibration sensors, tilt sensors, and pressure sensors as inputs to detect driving accidents. The system is supported by an Arduino uno kit with an R3 SMD microcontroller as the brain to process data from vibration, tilt, and pressure sensors, and is equipped with GPS and ESP32. Then, the output to ESP32 will send notification information to Telegram by detecting GPS, so that it will automatically send notification of driving accident coordinates. From the making of this system, it can be concluded that, if a driving accident occurs, it will automatically send information directly to the victim's family quickly. The system will work if the sensor is detected and will send data to the phone number stored in the database. So that victims can be handled more quickly to avoid casualties.

Road safety is an all-time global concern. Every day a large number of human lives are lost with many sustaining a disability as a result of their injury due to car accidents and delay in calling the rescue services. Recently, Intelligent Transportation Systems (ITS) have emerged as an efficient way of improving interpretation of transportation systems and enhancing travel safety. Accident detection systems are one of the most effective (ITS) tools. The accident detection system which based on Global Positioning System (GPS) and Global System for Mobile communication (GSM) can be accomplished though one or several sensors, the system can gather the information and coordinates of accident spot, then send this data to the rescue services center over a network link in the shortest time, In this paper, we propose an enhanced system that composed of a GPS receiver, Vibration sensor, GSM Modem and integrated with Vehicular AD-Hoc Network (VANET). The employment of (VANET) assists to increase the data delivery by providing a second path to send an emergency message to The Rescue Services Center (RSC) whenever an accident located at out of coverage area of GSM network. Simulation tested two of distance ranges among nodes (Long & Short) and the result shown that VANET modified algorithm based on average signal strength has the advantage of choosing the optimum route to deliver data and own enhanced ratio according to the distances (17.5% & 24.1%) by comparing with a maximum signal strength route.

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

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.

With the development of network and related technology has become more mature, now we can see the network applications in everywhere of the community, such as ATMs, mobile phones, home appliances and so on. With the increase of network applications as well as technology becomes more mature, we can foresee the future of the Internet is limitless. Network can be divided into wired network and wireless network, wireless network compared to a wired network has many advantages, such as easy to install, cover a wide range, amplified easy and so on. Wireless network thus ideal for use in difficult line installation place, such as vehicle, transportation node, institution and so on. Car is the most common means of transport, along with the progress of the automotive industry as well as increase of number of vehicles, how to enhance the safety of the vehicle is a very serious issue. Regarding to the safety of vehicle, in addition to considering the structural safety of the car, provide reliable auxiliary mechanism to driver is also a very important part. Millions of people injured or killed in a car accident, it is obvious that the issue of vehicle safety can not to be sniffed at. Because of the issue, the world-side countries are actively looking for solution to enhance the safety of vehicle. For example, the U.S. Department of Transportation’s National Highway Traffic Safety Administration (NHTSA) issued a final rule requiring all vehicles under 10,000 pounds, including buses and trucks, manufactured on or after May 1, 2018, to come equipped with rear visibility technology. Therefore, the rear visibility technology is a topic worthy of attention. In this paper, in order to decrease the technical threshold of establish the rear visibility system and avoid the hard installation to cause that can’t obtain the safety of vehicle. Therefore, we established a rear visibility system based on wireless transmission. By means of wireless transmission to increase the convenient of wiring and proposed a related power save mechanism to enhance the life time of wireless device in limited power. In addition, in order to avoid the system real time reaction capability decrease with power save mechanism, we proposed another auxiliary mechanism to reduce the effect that cause by power save mechanism. In addition, in order to avoid immediate saving mechanism to reduce system reaction capability, and the other proposed mechanisms to reduce the impact of an auxiliary power mechanism of response capabilities. The results show through simulations, through the mechanism proposed in this paper, the use of different scenario can effectively increase the life time of power and maintain a considerable degree of responsiveness.

The application of reliability models in traffic accident frequency analysis

Road accidents claim over a million lives every year. Some of the causes of these accidents are poor visibility of roads, false estimation of nearby vehicles and delay of driver to hit the brake. The developed IoT-based system focuses on reduction of accidents by addressing these causes. It alerts the driver about the presence of humps and potholes on the road by detecting it in advance. The visual alerts are provided by various coloured LEDs and the audio alert is provided using voice communication. The system also measures distance between the host vehicle and the vehicle ahead to maintain a safe distance of 400 m and warns the driver if safe distance is not maintained. This feature also helps in avoiding collisions with other vehicles and unidentified objects. In the worst-case scenario, if an accident occurs, this system tracks the vehicle's geographical location and provides a message alert to the registered emergency contacts. The system is equipped with vehicle-to-vehicle communication for data transmission amongst vehicles using Li-Fi technology. The range of this V2V communication is up to 2–3 m. With this feature, the host vehicle can transmit information about emergency brake situations and presence of emergency service vehicles in the vicinity for clearing the driving path. Audio and visual mechanisms are employed for alerting the driver. The system is developed by incorporating ultrasonic sensors with Arduino for testing purposes. Every feature of the system is tested with real vehicles in simulated circumstances. The performance of the system is satisfactory in all test environments.

With the rapid growth of population vehicles have become an absolute necessity of our daily life. Increasing vehicles causes lots of accidents and loss of life due to late information reaching to the rescue team and arrival of an ambulance. This paperpresents an Automatic Accident Detection and Human Rescue System (AADHRS) that will detect accidents and save the victims' life by informing the rescue team. Vibration sensor, Global System for Mobile (GSM) and Global Positioning System (GPS) are used in this system. After taking places of an accident, the system delivers a short message to a nearby rescue team and police station via GSM module. The message includes the longitude and latitude values of the location. A rescue team can instantly trace the location of the vehicle tapping geographical coordinates in Google earth or any other GPS viewer application to help wounded people. Most of the previous system comprised of multiple sensors for detecting accident which increased the cost of the project. But, the proposed model includes only one vibration sensor; thus it reduces the cost of multiple sensor and the complexity of interfacing. So, it will be affordable for vehicle owners of Bangladesh.

Transportation systems are invariably burdened with dynamically changing environmental conditions and ill-defined human factor. To raise ground vehicle safety on a new supreme level and to boost autonomous vehicles development driver-vehicle-environment cooperation is inevitable. In this paper, an overview of several existing driver-vehicle-environment integration methods with purpose of vehicle safety enhancement are stressed. Five unique and fundamentally different solutions are proposed, which have common similarity: the solutions are accomplished with machine learning algorithms. The methods aim at modelling drivers' or vehicles' behaviour with reasonable prediction accuracy under various complex scenarios. All five solutions are developed in individual projects in a framework of a continuous interdisciplinary European network ITEAM. The aim of the paper is to underline significant benefit of man-machine-environment integration in vehicle safety systems by exploiting fairly received tremendous attention machine learning methods.

Automotive safety systems like “Adaptive Cruise Control” (ACC), “Lane Change Assist” and pre-crash systems are nowadays dealing with the detection of vehicles on the road. All major upper class vehicle manufacturers like Mercedes, BMW, Chrysler and Lexus as well as leading suppliers (Bosch, Continental, Delphi) are currently developing intelligent vehicle safety systems. For vehicle environment perception many different sensors are under investigation. Well known and cheap devices like grayscale cameras are already integrated in many cars today, e.g. for parking assist functions. To meet the requirements for extremely reliable and robust environment recognition additional sensors and multi sensor data fusion approaches have to be applied. New sensors like 3-dimensional measuring multilayer laser scanner systems will be introduced in the near future to deliver environment information for these systems. By fusing multiple sensors like laser and radar systems the reliability level of automotive safety applications will be improved significantly. The paper presents an approach for the processing of the data of a multilayer laser scanner (lidar) for the detection of vehicles in road environments. The lidar data is processed using a new 3-dimensional occupancy grid. An example is given for an automotive pre-crash safety application.