Abstract
In future conditionally automated driving, drivers may be asked to take over control of the car while it is driving autonomously. Performing a non-driving-related task could degrade their takeover performance, which could be detected by continuous assessment of drivers' mental load. In this regard, three physiological signals from 80 subjects were collected during 1 h of conditionally automated driving in a simulator. Participants were asked to perform a non-driving cognitive task (N-back) for 90 s, 15 times during driving. The modality and difficulty of the task were experimentally manipulated. The experiment yielded a dataset of drivers' physiological indicators during the task sequences, which was used to predict drivers' workload. This was done by classifying task difficulty (three classes) and regressing participants' reported level of subjective workload after each task (on a 0–20 scale). Classification of task modality was also studied. For each task, the effect of sensor fusion and task performance were studied. The implemented pipeline consisted of a repeated cross validation approach with grid search applied to three machine learning algorithms. The results showed that three different levels of mental load could be classified with a f1-score of 0.713 using the skin conductance and respiration signals as inputs of a random forest classifier. The best regression model predicted the subjective level of workload with a mean absolute error of 3.195 using the three signals. The accuracy of the model increased with participants' task performance. However, classification of task modality (visual or auditory) was not successful. Some physiological indicators such as estimates of respiratory sinus arrhythmia, respiratory amplitude, and temporal indices of heart rate variability were found to be relevant measures of mental workload. Their use should be preferred for ongoing assessment of driver workload in automated driving.
Highlights
A recent study of critical reasons for traffic crashes found that the driver was at fault in 94% of the cases (Singh, 2015)
This work studied the assessment of mental workload through physiological data in the specific context of automated driving
Statistical analysis showed an effect of task difficulty on drivers’ heart and respiratory rates, but not on the tonic level of the Electrodermal activity (EDA). This could be explained by the low engagement of the drivers in the task or by the repeated requests to take over control during the experiment
Summary
A recent study of critical reasons for traffic crashes found that the driver was at fault in 94% of the cases (Singh, 2015) It includes recognition errors (including driver inattention and distractions), decision errors (driving too fast, misjudging the gap), performance errors, and non-performance errors (such as sleeping). To address this issue, car manufacturers are automating several functions of the driving task to assist the driver. In 2021, the last cars sold on the market are defined as partially automated vehicles and classified as Level 2 in the Society of Automotive Engineers (SAE) taxonomy (Society of Automotive Engineers, 2018). Depending on the level of driver workload, the driver-vehicle interaction must be continuously adapted to ensure safe use of the automation and improve the user experience
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