Fatigue and drowsiness detection using a support vector machine for traffic accident reduction

Abstract

Fatigue and drowsiness are major contributors to road safety issues, causing slower reactions, poor decision-making, and increased accidents. Support vector machine (SVM) can improve road safety by analyzing complex data sets and patterns related to driver behavior. When using features extracted from electrooculography signals to determine driver fatigue, SVM demonstrated high classification accuracy. This shows that it could be a useful tool in real-time fatigue detection systems. SVM's successful application in traffic accident reduction demonstrates its potential for improving road safety through predictive modeling and early warning systems. Integrating SVM algorithms into traffic accident prediction models enables the analysis of a wide range of factors, including road conditions, driver behavior, and vehicle characteristics, in order to identify potential risk factors and take proactive measures to avoid accidents. Studies have shown that SVM-based systems can predict accidents with high accuracy, resulting in timely interventions and, ultimately, fewer road fatalities and injuries. In conclusion, using SVM to detect driver fatigue and drowsiness is critical for increasing road safety. Future research should focus on improving the system's accuracy and real-time capabilities, incorporating advanced machine learning algorithms, and developing adaptive SVM models that constantly learn and update their parameters based on real-time data.