Prediction of Collision and Contributing Factors Using Decipherable Machine Learning Model

Abstract

Accidents in mountainous regions and ghats are often caused by limited visibility and lack of real-time information about oncoming traffic. This paper presents an Intelligent Mountainous Region Traffic Monitoring and Alert System that leverages computer vision, Internet of Things (IoT), and real-time data processing to enhance road safety. The system simulates a hilly terrain using a prototype equipped with dual cameras capturing video feeds of the road. These feeds are processed and compressed before being analyzed using YOLO (You Only Look Once), a real-time object detection algorithm, to identify vehicles (represented by toy cars). Upon detection, relevant data is uploaded to a Google Firebase real-time database. A NodeMCU microcontroller continuously retrieves this data and activates an alert mechanism—specifically a red light on a signal pole—indicating vehicle presence on the opposite side of the terrain. The system operates within an IoT-based framework and ensures secure data communication through end-to-end encryption. The integration of YOLO for high-speed detection, Firebase for responsive data management, and IoT for seamless communication forms a reliable, real-time alert system aimed at reducing head-on collisions and improving safety in low-visibility, high-risk mountain areas.

Introduction

Vehicles are essential for transportation but navigating mountainous regions with hairpin bends poses serious safety risks due to limited visibility, adverse weather, and poor road conditions. These bends increase accidents mainly because drivers cannot see oncoming traffic, leading to collisions and congestion. Existing road systems lack adequate real-time safety measures.

The proposed solution uses computer vision and IoT technologies to detect vehicles approaching hairpin bends, classify them as Light Motor Vehicles (LMVs) or Heavy Motor Vehicles (HMVs), and communicate this information to drivers on the opposite side via a seven-segment display. This system enhances driver awareness by showing vehicle type, count, and estimated clearance time, helping prevent accidents and improve traffic flow.

The literature survey covers recent advances in traffic accident prediction, detection, and avoidance using machine learning and deep learning models (e.g., YOLO, CNNs, Random Forest), real-time data from sensors, and floating car data. These studies emphasize the importance of integrating AI and IoT for dynamic, real-time traffic safety systems.

The proposed system architecture integrates multiple sensors (radar, ultrasonic, infrared, cameras) with IoT microcontrollers (NodeMCU, ESP32) and cloud services (Firebase) to monitor and communicate vehicle presence and road conditions in real-time. The YOLOv8 algorithm enables fast and accurate vehicle and obstacle detection. Alerts are communicated through LED displays, buzzers, and SMS via Twilio.

Implementation includes hardware setup (Arduino, cameras, sensors), software modules (Python for communication and detection, Arduino for control), and machine learning integration. Results show improved accuracy (92%) in vehicle detection and accident prevention compared to previous models (78%), highlighting the system’s practical effectiveness for real-time safety in hazardous terrains.

Conclusion

The proposed system establishes a robust framework for enhancing road safety in hazardous hairpin bends and mountainous terrains, leveraging IoT sensors, camera-based detection, and YOLO-driven object recognition to prevent accidents and improve emergency response. By integrating real-time environmental monitoring with predictive analytics via LSTM networks, the system offers a scalable and adaptable solution, capable of transitioning to urban roads and addressing diverse traffic challenges. Its success in reducing collision risks through proactive alerts and dynamic infrastructure updates marks a significant advancement in intelligent transportation systems, paving the way for safer, smarter roads. 1) Sustainable Power Solutions: Implementing solar panels and optimizing battery usage for sensors and microcontrollers will ensure uninterrupted operation in remote, off-grid locations. 2) Hardware Upgrades: Transitioning from Arduino to Raspberry Pi will unlock advanced processing capabilities, enabling multitasking, complex computations (e.g., Python-based AI models), and more adaptive system behavior. 3) Enhanced Imaging: Upgrading to 4K/8K cameras with higher-resolution sensors (4MP, 8MP) will improve object detection accuracy, particularly in low-light or foggy conditions, ensuring clearer input data for ML models. These enhancements, combined with ongoing advancements in edge computing and AI, will strengthen the system’s reliability, scalability, and environmental resilience. By bridging cutting-edge technology with practical safety needs, this project not only addresses a critical public health challenge but also sets the stage for future innovations in smart infrastructure and accident prevention.

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Copyright

Copyright © 2025 Dr. Udayashree S , Dr. Suresh Kumar, Dhivya Prasanna V, Mohamad Salman H, Mohamed Ghouse M. S, Mathew Alexander. 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.