Comparison of Artificial Intelligent Systems for Real-Time Accident-Prone Applications

Fatigue drowsiness is a major cause of road accidents, particularly among long-distance drivers. To address this issue, numerous artificial intelligence (AI) solutions for detecting driver fatigue in real time have been developed. We performed an analysis of four distinct AI algorithms for detecting driver tiredness in this study: 2 s-STGCN, PERCLOS (percent of the time eyelids are CLOSed), support vector machines (SVM), non-negative matrix factorization (NMF), convolutional neural networks (CNN), and AlexNet. We used publicly available datasets of more than 3000 images collected from drivers under different conditions, including varying levels of drowsiness. The dataset was preprocessed and augmented to improve the robustness of the models. We then trained and evaluated each of the four AI techniques on the dataset using various performance metrics, including accuracy. Our results showed that all four AI techniques performed reasonably well, with AlexNet achieving the highest accuracy of 99.65%. SVM and CNN with PERCLOS also performed well, with accuracy scores of 94% and 93.4%, respectively. CNN and 2 s-STGCN had lower accuracy scores of 93.37%. Our findings suggest that AlexNet is the most effective AI technique for driver drowsiness detection, followed by SVM and CNN. These findings can be used to help build robust driver drowsiness monitoring tools, which may also help minimize the number of car accidents that result from driver fatigue.