Abstract
This paper presents a novel feature selection method to design a non-invasive driver drowsiness detection system based on steering wheel data. The proposed feature selector can select the most related features to the drowsiness level to improve the classification accuracy. This method is based on the combination of the filter and wrapper feature selection algorithms using adaptive neuro-fuzzy inference system (ANFIS). In this method firstly, four different filter indexes are applied on extracted features from steering wheel data. After that, output values of each filter index are imported as inputs to a fuzzy inference system to determine the importance degree of each feature and select the most important features. Then, the selected features are imported to a support vector machine (SVM) for binary classification to classify the driving conditions in two classes of drowsy and awake. Finally, the classifier accuracy is exploited to adjust parameters of an adaptive fuzzy system using a particle swarm optimization (PSO) algorithm. The experimental data were collected from about 20.5 h of driving in the simulator. The results show that the drowsiness detection system is working with a high accuracy and also confirm that this method is more accurate than the recent available algorithms.
Highlights
Proposed neuro‐fuzzy feature selection method as well as each single filter methods that have been explained in pervious sections have been applied on driving simulator dataset to
Proposed neuro-fuzzy feature selection method as well as each single filter methods that have been explained in pervious sections have been applied on driving simulator dataset to detect the drowsiness in bus drivers
True Positive (TP): number of drowsy states that correctly classified as drowsy; True Negative (TN): number of awake states that correctly identified as awake; False Negative (FN): number of drowsy states that incorrectly identified as awake; False Positive (FP): number of awake states that incorrectly identified as drowsy; Using the above indexes, accuracy percentage of each method is calculated with Equation (12): Accuracy =
Summary
Drowsiness detection is an important factor for road safety, in manual driving as well as in future semi-automated driving. About 20 to 30% of fatal road accidents are reported to be attributable to driver drowsiness [1,2]. In SAE level 3 automated driving, drivers will be allowed to do a secondary task, but the automation system has to hand-over vehicle guidance whenever it cannot manage the situation. A hand-over to a drowsy driver will eventually not be manageable, so a restriction of vehicle automation to alert drivers detected by a reliable drowsiness detection system could be an option for oncoming SAE level 3 systems. Two general strategies could be considered to detect driver drowsiness: intrusive and non-intrusive. The drowsiness state is analyzed using processing of physiological outputs such as electroencephalographic (EEG) and electrooculographic (EOG) information [3]
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