A Hybrid Model Utilizing Principal Component Analysis and Artificial Neural Networks for Driving Drowsiness Detection

Abstract

The detection of drowsiness while driving plays a vital role in ensuring road safety. Existing detection methods need to reduce external interference and sensor intrusiveness, and their algorithms must be modified to improve accuracy, stability, and timeliness. In order to realize fast and accurate driving drowsiness detection using physiological data that can be collected non-intrusively, a hybrid model with principal component analysis and artificial neural networks was proposed in this study. Principal component analysis was used to remove the noise and redundant information from the original data, and artificial neural networks were used to classify the processed data. Three other models were designed for comparison, including a hybrid model with principal component analysis and classic machine learning algorithms, a single model with artificial neural networks, and a single model with classic machine learning algorithms. The results indicated that the average accuracy of the proposed model exceeded 97%, the average training time was lower than 0.3 s, and the average standard deviation of the proposed model’s accuracy was 0.7%, indicating that the model could detect driving drowsiness more accurately and quickly than the comparison models while ensuring stability. Thus, principal component analysis can help to improve the accuracy of driving drowsiness detection. This method can be applied to active warning systems (AWS) in intelligent vehicles in the future.