Abstract
Driver cognitive distraction is a critical factor in road safety, and its evaluation, especially under real conditions, presents challenges to researchers and engineers. In this study, we considered mental workload from a secondary task as a potential source of cognitive distraction and aimed to estimate the increased cognitive load on the driver with a four-channel near-infrared spectroscopy (NIRS) device by introducing a machine-learning method for hemodynamic data. To produce added cognitive workload in a driver beyond just driving, two levels of an auditory presentation n-back task were used. A total of 60 experimental data sets from the NIRS device during two driving tasks were obtained and analyzed by machine-learning algorithms. We used two techniques to prevent overfitting of the classification models: (1) k-fold cross-validation and principal-component analysis, and (2) retaining 25% of the data (testing data) for testing of the model after classification. Six types of classifier were trained and tested: decision tree, discriminant analysis, logistic regression, the support vector machine, the nearest neighbor classifier, and the ensemble classifier. Cognitive workload levels were well classified from the NIRS data in the cases of subject-dependent classification (the accuracy of classification increased from 81.30 to 95.40%, and the accuracy of prediction of the testing data was 82.18 to 96.08%), subject 26 independent classification (the accuracy of classification increased from 84.90 to 89.50%, and the accuracy of prediction of the testing data increased from 84.08 to 89.91%), and channel-independent classification (classification 82.90%, prediction 82.74%). NIRS data in conjunction with an artificial intelligence method can therefore be used to classify mental workload as a source of potential cognitive distraction in real time under naturalistic conditions; this information may be utilized in driver assistance systems to prevent road accidents.
Highlights
Driver distraction is a major cause of traffic accidents (NHTSA, 2015)
Because the central goal of our research was to identify and improve a metric that might permit the detection of mental workload in real time and which could operate under real conditions in the presence of, for example, vibration from the vehicle, we examined only physical metrics in the present study
The accuracies in classifying the driver’s mental workload from each channel were found to be in the range 84.9 to 89.5%, and the accuracy in predicting testing data increased from 84.08 to 89.91%. These results indicated that individual characteristics affected the accuracy of classification of the mental workload
Summary
Driver distraction is a major cause of traffic accidents (NHTSA, 2015). An analysis by the US Highway Traffic Safety Administration (NHTSA) showed that driver distraction can be categorized into three types: visual distraction, manual distraction, and cognitive distraction (NHTSA, 2012). Cognitive distraction is defined as the mental workload associated with a task that involves thinking about something other than driving. The detection of cognitive distraction imposed by a secondary task while driving might play an important role in creating a new driver-assistance system to reduce the incidence of traffic accidents. Dong et al (2011) categorized techniques for measuring mental workload while driving into five groups: (1) subjective metrics, (2) biological metrics, (3) physical metrics, (4) performance metrics, and (5) combinations of these metrics. Because the central goal of our research was to identify and improve a metric that might permit the detection of mental workload in real time and which could operate under real conditions in the presence of, for example, vibration from the vehicle, we examined only physical metrics in the present study
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