Driver Fatigue Prediction Using Different Sensor Data with Deep Learning

Abstract

Fatigue driving is one of the main causes of traffic accidents. If we can predict that a driver will be fatigued in the near future, and give warnings and parking suggestions to let the driver stop and rest, traffic accidents can be reduced effectively. In the past, most research work on fatigue driving used conventional Back Propagation Neural Network (BPNN) model or Support Vector Machine (SVM) to make predictions. BPNN and SVM could not co-relate sequential instances of data in that series, thus making predictions not so accurate. A system is proposed that uses Recurrent Neural Network (RNN) with Long Short-term Memory (LSTM) building blocks to predict driver fatigue. Two parameters, Heart Rate Variability (HRV) and the Percentage of Eyelid Closure (PERCLOS) are used as an input data to the LSTM model so as to predict whether the driver will be fatigued in some future time slot. Experiments show that the system PERCLOS best using LSTM-based driver fatigue in prediction module that can achieve a true positive rate of 75% and an accuracy of 88%. Whereas fatigue prediction of the HRV best uses BPNN-based prediction module that yields a true positive rate and accuracy of 80% and 88% respectively.