A Deep Learning-Based Multi-Hazard Detection System for Intelligent Road Safety and Driver Assistance

Ramneet Singh Chadha; Sakshi Negi

doi:10.36948/ijfmr.2026.v08i03.80101

A Deep Learning-Based Multi-Hazard Detection System for Intelligent Road Safety and Driver Assistance

Author(s)	Mr. Ramneet Singh Chadha, Ms. Sakshi Negi
Country	India
Abstract	As time has progressed, road transportation has become essential in today’s world. This evolution has been driven by economic growth, increased mobility and intensified communication activities. The increase in number of vehicles on the roads has been tremendous. Also, ill-constructed roads, rash driving, negligence of traffic rules and sudden obstacles have caused many accidents. We have made great progress in artificial intelligence, deep learning and computer vision. These developments can be used for road hazard detection and driver assistance. This research presents an intelligent multi-hazard road detection and driver assistance framework is presented based on deep learning and computer vision technologies to boost the transportation safety and driver awareness. The framework is intended to analyze the dashcam video streams and identify major road hazards such as potholes, traffic signs, speed breakers etc. The framework utilizes deep learning models like YOLOv8 and Convolutional Neural Networks (CNNs) for hazard identification and classification. Furthermore, the framework includes an intelligent audio alert system to inform drivers when hazardous road conditions are detected, enhancing driving awareness and safety. The architecture also comprises GPS-based hazard localization for transportation monitoring and future intelligent transportation applications. The goal of this research is to improve road monitoring, situational awareness and intelligent driver assistance by combining different hazard detection capabilities into a common framework. Overall, the proposed work demonstrates the increasing importance of artificial intelligence and computer vision technologies in the development of safer, smarter, and more efficient transportation systems.
Keywords	Deep Learning, Computer Vision, Intelligent Transportation Systems, Multi-Hazard Detection, YOLOv8, Driver Assistance, Road Safety, Traffic Sign Recognition, Smart Transportation
Field	Engineering
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-06-02
DOI	https://doi.org/10.36948/ijfmr.2026.v08i03.80101

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A Deep Learning-Based Multi-Hazard Detection System for Intelligent Road Safety and Driver Assistance

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