Vehicle Accident Control

Abstract

The project centers on creating an advanced accident detection system using an ESP32 microcontroller integrated with multiple sensors to enable real-time vehicle monitoring and emergency management. The system includes an array of sensors designed to detect various risk factors that can contribute to accidents. The Alcohol Sensor monitors the driver’s alcohol consumption, while the Eye Blink Sensor detects driver fatigue or sleepiness. Additionally, the Smoke Sensor identifies any smoke or fire-related incidents inside the vehicle, and the Temperature Sensor monitors engine or cabin temperature for potential overheating.For vehicle performance, the Ultrasonic Sensor measures fuel levels, ensuring that the vehicle does not run out of fuel unexpectedly. The IR Speed Sensor monitors the vehicle’s speed, while the IR Obstacle Sensor detects nearby objects to prevent collisions. If any abnormal conditions are detected, the system activates a Buzzer for alerting, while a Motor Driver controls the vehicle’s operations, such as initiating a shutdown if necessary to prevent accidents.Data collected by these sensors are transmitted to a remote server using the MIT App Inventor platform and monitored via ThingSpeak, an IoT analytics platform. This remote interface allows users to track the vehicle’s condition and receive alerts in real time. It also includes a feature for remote vehicle lock/unlock control, allowing further management of vehicle safety. This system is a promising innovation aimed at enhancing road safety, providing timely alerts, and reducing the likelihood of accidents by addressing both human and environmental factors. Moreover, the system’s ability to immediately notify authorities or family members in the event of an emergency ensures quicker response times and potentially saves lives.

Introduction

The rising number of road accidents worldwide has made vehicle safety a crucial issue, often caused by driver negligence, fatigue, intoxication, and mechanical failures. To reduce accidents, early detection of hazards is essential. Leveraging IoT technology, an accident detection and prevention system using the ESP32 microcontroller is proposed. The ESP32’s integrated Wi-Fi and Bluetooth enable real-time monitoring and communication with external devices.

The system uses multiple sensors:

• Alcohol Sensor to detect driver intoxication,

• Eye Blink Sensor to monitor driver fatigue,

• Smoke and Temperature Sensors to detect fire risks,

• Ultrasonic Sensor for fuel level measurement,

• IR Speed and Obstacle Sensors to monitor vehicle speed and detect obstacles.

Sensor data is transmitted to the ThingSpeak cloud platform for real-time analysis and can be accessed remotely via a mobile app developed with MIT App Inventor, which also allows remote vehicle control (e.g., locking/unlocking).

The system automatically alerts the driver with a buzzer and can slow or stop the vehicle if dangerous conditions are detected, such as drunk driving or obstacles. This integrated approach enhances driver safety by enabling quick preventive actions, reducing the risk of accidents.

The literature review discusses related technologies, including GPS and GSM-based accident alert systems and Bluetooth-enabled vehicle communication for intelligent transport systems.

Conclusion

The ESP32-based accident detection system enhances vehicular safety by integrating various sensors that monitor driver behavior, environmental conditions, and vehicle performance. This system provides real-time data and alerts, helping to prevent accidents effectively. Using ThingSpeak for cloud storage and a MIT App Inventor-built app for remote control, the system remains accessible and functional from a distance.

References

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Copyright

Copyright © 2025 Dr. P. K. Magadum, Arbaj Shaikh, Sajid Solapure, Umesh Yegade, Sujal Tambade. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.