Machine Learning-Based Automatic Accident Identification, Segmentation, and Duration Forecasting

Abstract

The use of automobiles has grown dramatically in the modern world. Because of the increased traffic caused by the excessive use of vehicles, there are now more accidents on the roads. This damages the property and causes fatalities since there is no quick access to safety and preventative measures. While complete accident prevention may not be achievable, the consequences can be reduced to a minimum. This embedded technology has put the right safety precautions in place and can prevent mishaps from happening. To make it easy for the police station and ambulance service\'s smart devices, which can access mobile networks, to find the place, they were provided with the address and a link to a Google map. An accelerometer, temperature sensor, GSM module, GPS module, alcohol sensor, and eye blink sensor are all part of the system.

Introduction

Problem Overview:

The rise in traffic accidents globally—especially in densely populated countries like India—is a critical issue, driven by increased vehicle usage, drunk or drowsy driving, and insufficient emergency response. The current technologies mostly detect accidents after they occur, often with delays in notifying emergency services, leading to preventable fatalities.

Proposed Solution:

An IoT-based system is proposed to detect, prevent, and report accidents automatically and instantly. It uses real-time monitoring and automated alerts to emergency services, aiming to:

• Reduce response time

• Increase survival chances

• Prevent accidents (e.g., via alcohol detection)

• Provide exact accident location via GPS

Literature Review Highlights:

1. Majd Khaled Almohsen et al. (2021): Developed a smart seatbelt system with heart rate monitoring and GPS tracking. Uses Arduino for real-time accident detection and emergency alerts via GSM.

2. Unaiza Alvi et al. (2020): Reviewed various IoT-based methods (e.g., GSM/GPS systems, vehicle networks, ML algorithms) for accident prediction and detection. Emphasized need for timely medical response.

Hardware Components:

• Regulated Power Supply: Ensures stable voltage for sensitive electronics.

• GSM Module: Sends accident alerts and location data to emergency contacts.

• MEMS Sensor: Detects movement or vibration indicating a crash.

• LEDs: Serve as indicators in the system.

• Arduino-based Setup: Integrates all components to detect accidents and trigger alerts.

Working Mechanism:

• Sensors detect an accident based on sudden force or abnormal heart rate.

• System validates accident using input data.

• Location is tracked via GPS.

• Alert is sent via GSM to emergency contacts with location info.

• Alcohol detection system can disable vehicle engine preemptively.

Operational Benefits:

• Faster emergency response through real-time alerts

• Improved traffic flow via prediction of incident impact

• Accident severity analysis using segmentation and ML

• Data for policy-making and city planning

• Supports smart city infrastructure and road safety goals

Results:

The implemented system successfully identifies accident events and provides real-time alerts to emergency services. It enhances survivability, reduces response time, and provides valuable data for future improvements in road safety.

Conclusion

To sum up, the suggested An ML-based accident avoidance, detection, and evaluation system offers a viable way to improve Rescue interventions and road security. The system shows its potential to reduce accidents and lessen their effects on people and infrastructure by integrating sensor networks and ML technology. Sustained research and development endeavors are important in order to enhance the system’s functionalities, tackle novel obstacles, and adjust to changing road safety requirements. We can work to make roads safer and lessen the toll that traffic accidents have on society by utilizing innovation and teamwork.

References

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Copyright

Copyright © 2025 B. Sarvesh, Talari Sanghavi, T. Indu, Pallam Venkatapathi. 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.