Driver Behavior–Based Intelligent System for Traffic Accident Detection and Early Warning

Abstract

This study presents an intelligent system for traffic accident detection and early warning based on driver behavior analysis. A driver behavior–based intelligent system for traffic accident detection and early warning operates by continuously monitoring the driver’s actions using cameras and vehicle sensors. It collects real-time data such as eye movements, head pose, steering patterns, and acceleration signals. Advanced deep learning models such as YOLO and recurrent neural networks analyze these features to detect fatigue, distraction, or abnormal driving behavior. The system then evaluates the risk level based on predefined thresholds and contextual traffic conditions. When the risk exceeds a safety limit, it generates early warnings to prevent potential accidents. It is widely applied in smart vehicles, fleet management systems, and advanced driver assistance systems (ADAS). It helps monitor driver fatigue, distraction, and risky behaviors in real time to improve road safety. In commercial transportation, it supports logistics companies by reducing accident rates and operational costs. Experimental results show improved detection accuracy, high precision–recall performance, and reduced false alarms under diverse driving conditions. Overall, the system contributes to fewer traffic accidents, enhanced driver awareness, and more reliable intelligent transportation systems.