Classification of Drivers' Workload Using Physiological Signals in Conditional Automation

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 best model 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.

The Validity of Physiological Measures to Identify Differences in Intrinsic Cognitive Load

“…For example, how to manage cognitive load during medical training and reallife practice (see Fraser et al, 2015;Johannessen et al, 2020;Szulewski et al, 2020) is critical. Similarly, in driving a car it can be advantageous from a safety perspective to be able to monitor the driver's cognitive states and implement interventions accordingly (see Lohani et al, 2019;Meteier et al, 2021). When driving, real-time continuous data is required which can be collected through physiological measures, but using subjective measures is impossible.…”

Relevant Physiological Indicators for Assessing Workload in Conditionally Automated Driving, Through Three-Class Classification and Regression

“…They are compared and discussed on several parameters that can affect the accuracy of a model trained with machine learning techniques, including the environmental settings, the task used to induce MWL, the time intervals used for calculating physiological indicators, the number of classes, and the evaluation approach. Previous studies were conducted in different environments, such as laboratories (Haapalainen et al, 2010;Ferreira et al, 2014;Hogervorst et al, 2014), driving simulators (Son et al, 2013;Darzi et al, 2018;Meteier et al, 2021) or on roads (Solovey et al, 2014). For the driving studies, participants were required to drive manually and perform an additional NDRT to manipulate the level of MWL, except for Meteier et al (2021) study in which the car drove in conditional automation, and participants were required to count backward orally.…”

Non-Intrusive Contact Respiratory Sensor for Vehicles

“…Physiological signals are one source of data providing intrinsic information about the driver’s condition. The relevance of these to assess the state of the driver according to various components such as fatigue and drowsiness [ 3 , 4 , 5 , 6 , 7 , 8 ], workload [ 8 , 9 , 10 , 11 , 12 , 13 , 13 ] or stress [ 8 , 14 , 15 , 16 , 17 ] has been proven in scientific literature.…”

“…In particular, the respiratory signal can be a valuable data source. The breathing pattern may change when drivers are drowsy [ 18 ], when they are talking, or when their cognitive load increases [ 12 ]. Measures of respiratory rate and respiratory variability can be calculated from the raw respiration signal.…”