This Is Your Brain on Autopilot 2.0: The Influence of Practice on Driver Workload and Engagement During On-Road, Partially Automated Driving

Hum Factors. 2023 Sep 26:187208231201054. doi: 10.1177/00187208231201054. Online ahead of print.

Abstract

Objective: This on-road study employed behavioral and neurophysiological measurement techniques to assess the influence of six weeks of practice driving a Level 2 partially automated vehicle on driver workload and engagement.

Background: Level 2 partial automation requires a driver to maintain supervisory control of the vehicle to detect "edge cases" that the automation is not equipped to handle. There is mixed evidence regarding whether drivers can do so effectively. There is also an open question regarding how practice and familiarity with automation influence driver cognitive states over time.

Method: Behavioral and neurophysiological measures of driver workload and visual engagement were recorded from 30 participants at two testing sessions-with a six-week familiarization period in-between. At both testing sessions, participants drove a vehicle with partial automation engaged (Level 2) and not engaged (Level 0) on two interstate highways while reaction times to the detection response task (DRT) and neurophysiological (EEG) metrics of frontal theta and parietal alpha were recorded.

Results: DRT results demonstrated that partially automated driving placed more cognitive load on drivers than manual driving and six weeks of practice decreased driver workload-though only when the driving environment was relatively simple. EEG metrics of frontal theta and parietal alpha showed null effects of partial automation.

Conclusion: Driver workload was influenced by level of automation, specific highway characteristics, and by practice over time, but only on a behavioral level and not on a neural level.

Application: These findings expand our understanding of the influence of practice on driver cognitive states under Level 2 partial automation.

Keywords: attentional processes; human-automation interaction; levels of automation; mental workload; supervisory control.