Classification of Drivers Workload Using Physiological Signals in Conditional Automation

Published in Frontiers in Psychology, section Performance Science, 2021

Recommended citation: Meteier Q, Capallera M, Ruffieux S, Angelini L, Abou Khaled O, Mugellini E, Widmer M and Sonderegger A (2021). Classification of Drivers’ Workload Using Physiological Signals in Conditional Automation. Front. Psychol. 12:596038. doi: 10.3389/fpsyg.2021.596038

Abstract :

The use of automation in cars is increasing. In future vehicles, drivers will no longer be in charge of the main driving task and may be allowed to perform a secondary task. However, they might be requested to regain control of the car if a hazardous situation occurs (i.e., conditionally automated driving). Performing a secondary task might increase drivers’ mental workload and consequently decrease the takeover performance if the workload level exceeds a certain threshold. Knowledge about the driver’s mental state might hence be useful for increasing safety in conditionally automated vehicles. Measuring drivers’ workload continuously is essential to support the driver and hence limit the number of accidents in takeover situations. This goal can be achieved using machine learning techniques to evaluate and classify the drivers’ workload in real-time. To evaluate the usefulness of physiological data as an indicator for workload in conditionally automated driving, three physiological signals from 90 subjects were collected during 25 min of automated driving in a fixed-base simulator. Half of the participants performed a verbal cognitive task to induce mental workload while the other half only had to monitor the environment of the car. Three classifiers, sensor fusion and levels of data segmentation were compared. Results show that the bes tmodel was able to successfully classify the condition of the driver with an accuracy of 95%. In some cases, the model benefited from sensors’ fusion. Increasing the segmentation level (e.g., size of the time window to compute physiological indicators) increased the performance of the model for windows smaller than 4 min, but decreased for windows larger than 4 min. In conclusion, the study showed that a high level of drivers’mental workload can be accurately detected while driving in conditional automation based on 4-min recordings of respiration and skin conductance.

Keywords :

automated driving, classification, driver, workload, physiology, secondary task, machine learning

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