Evaluation of drivers' mental workload based on multi-modal physiological signals

Abstract

Accurately assessing the driver's mental workload is of great significance to reduce the traffic accidents caused by the driver's mental overload. This study aims to evaluate drivers' mental workload in simulated typical driving scenarios, with <italic>N</italic>-back cognitive tasks used to manipulate varied levels of task difficulty. We collect data on multi-modal physiological signals including electroencephalogram (EEG), electrocardiogram (ECG), and electrodermal activity (EDA) signals, and subjective mental load of the National Aeronautics and Space Administration task load index (NASA_TLX) during the task completion process of the driver in the experiment, and propose a series of mental workload classification models based on feature analysis and pattern recognition of the multi-modal physiological signals. These classification models are verified by machine learning algorithms of random forest, decision tree and <italic>k</italic>-nearest neighbor models. The results show that the accuracy of classification models varies with different modalities of physiological signals. EEG-based classification models yield the highest accuracy among single-modal classification models, followed by EDA-based and ECG-based models. Multi-modal-based classification models generally perform better than single-modal classification models. The random forest classification algorithm based on three-modal physiological signals of EEG, ECG and EDA has the highest classification accuracy.