Individual Susceptibility to Vigilance Decrement in Prolonged Assisted Driving Revealed by Alert-State Wearable EEG Assessment

As vehicular automation handles more aspects of the driving task, human drivers are taxed with increasing demands to monitor and handle rare automation failures. Staying vigilant imposes a high-cognitive workload and so is hypothesized to pose varying difficulties across the driving population, leading to differences in individual susceptibility to vigilance decrement. To investigate this, the present study proposes the use of an objective neurometric of mental workload to characterize drivers' severity of vigilance decrement during assisted driving with adaptive cruise control. Drivers performed a car-following task for approximately 1.5 h and performed emergency braking whenever the leading car brakes suddenly. The neurometric, frontal theta / parietal alpha ratio-measured from an alert-state driving period using a wearable electroencephalography headset-was found to be associated with the magnitude of drivers' behavioral changes (cumulative slowing and erraticity of their braking reaction times) over the course of the assisted driving tour. This is the first study to explore the use of an alert-state objective measure in profiling individuals' general susceptibility to vigilance decrements in assisted driving, which is highly relevant in the context of identifying higher risk drivers and designing driver-vehicle interaction systems to enhance automation use safety.

Automated summary

As vehicular automation becomes more advanced, human drivers are faced with the increasing demand to monitor and handle rare automation failures. This study investigates the differences in individual susceptibility to vigilance decrement during assisted driving with adaptive cruise control, using an objective neurometric of mental workload. The findings suggest that the measured neurometric is associated with the magnitude of drivers' behavioral changes, highlighting its potential use in identifying higher risk drivers and improving driver-vehicle interaction systems for enhanced safety.