A CNN-LSTM-based Deep Learning Approach for Driver Drowsiness Prediction

Abstract

Abstract: The development of neural networks and machine learning techniques has recently been the cornerstone for many applications of artificial intelligence. These applications are now found in practically all aspects of our daily life. Predicting drowsiness is one of the most particularly valuable of artificial intelligence for reducing the rate of traffic accidents. According to earlier studies, drowsy driving is at responsible for 25 to 50% of all traffic accidents, which account for 1,200 deaths and 76,000 injuries annually. The goal of this research is to diminish car accidents caused by drowsy drivers. This research tests a number of popular deep learning-based models and presents a novel deep learning-based model for predicting driver drowsiness using a combination of convolutional neural networks (CNN) and Long-Short-Term Memory (LSTM) to achieve results that are superior to those of state-of-the-art methods. Utilizing convolutional layers, CNN has excellent feature extraction abilities, whereas LSTM can learn sequential dependencies. The National Tsing Hua University (NTHU) driver drowsiness dataset is used to test the model and compare it to several other current models as well as state-of-the-art models. The proposed model outperformed state-of-the-art models, with results up to 98.30% for training accuracy and 97.31% for validation accuracy.

Keywords

• driver drowsiness
• deep learning
• cnn
• lstm
• nthu dataset