Abstract
One debatable issue in traffic safety research is that the cognitive load by secondary tasks reduces primary task performance, i.e., driving. In this paper, the study adopted a version of the n-back task as a cognitively loading secondary task on the primary task, i.e., driving; where drivers drove in three different simulated driving scenarios. This paper has taken a multimodal approach to perform ‘intelligent multivariate data analytics’ based on machine learning (ML). Here, the k-nearest neighbour (k-NN), support vector machine (SVM), and random forest (RF) are used for driver cognitive load classification. Moreover, physiological measures have proven to be sophisticated in cognitive load identification, yet it suffers from confounding factors and noise. Therefore, this work uses multi-component signals, i.e., physiological measures and vehicular features to overcome that problem. Both multiclass and binary classifications have been performed to distinguish normal driving from cognitive load tasks. To identify the optimal feature set, two feature selection algorithms, i.e., sequential forward floating selection (SFFS) and random forest have been applied where out of 323 features, a subset of 42 features has been selected as the best feature subset. For the classification, RF has shown better performance with F1-score of 0.75 and 0.80 than two other algorithms. Moreover, the result shows that using multicomponent features classifiers could classify better than using features from a single source.
Highlights
Driving a vehicle requires dynamic adjustment of cognitive control, here, both visual and physical tasks are crucial to keep the driving performance to an acceptable level within a comfortable effort [1].While driving a vehicle, drivers are often occupied with many other activities such as using a mobile phone, listening to the radio, or having a conversation with a passenger, etc
The best classification accuracy was 66% with 11 EEG features
Apart from the analysis presented in this paper, several other classification experiments [102] have been conducted considering features according to (1) cerebral activities recorded via EEG, (2) cerebral activities recorded via EEG and eye blink waveform via EOG, (3) non-cerebral physiological signals recorded via heart rate variability (HRV), galvanic skin response (GSR), and respiration, and (4) driving behavioural data based on vehicular parameters obtained from the control computer
Summary
Driving a vehicle requires dynamic adjustment of cognitive control, here, both visual and physical tasks are crucial to keep the driving performance to an acceptable level within a comfortable effort [1].While driving a vehicle, drivers are often occupied with many other activities such as using a mobile phone, listening to the radio, or having a conversation with a passenger, etc. Driving a vehicle requires dynamic adjustment of cognitive control, here, both visual and physical tasks are crucial to keep the driving performance to an acceptable level within a comfortable effort [1]. New advanced in-vehicle information systems embedded in the modern vehicles could create distracted driving scenarios and may affect the driving performance [2,3,4]. These secondary activities, i.e., activities not related to driving require extra cognitive processes in ways that the driver can still keep their eyes on the road and hands on the steering wheel while being involved in other activities at the same time, and this refers to the ‘cognitive load activities’. It is reported that more than 90% of traffic crashes are assigned to the driver’s error, whereas 41% of them are due to inattention, distraction, and cognitive load activities [5].
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