Citation

Seet M, Harvy J, Bose R, Dragomir A, Bezerianos A, Thakor N. IEEE Trans. Intel. Transp. Syst. 2022; 23(1): 548-557.

Copyright

(Copyright © 2022, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2020.3013278

PMID

unavailable

Abstract

Trust in autonomous vehicles (AV) is of critical importance and demands comprehensive interdisciplinary research. While most studies utilize subjective measures, we employ electroencephalography (EEG) to study in a more objective manner the cognitive states associated with trust during AV driving. Subjects drove a simulated AV in Conditional Automation Driving (SAE L3) and Full Automation Driving (SAE L5) modes. In the experimental design, malfunctions were induced at both automation levels. Self-reported trust in the AV was reduced immediately after Full Automation malfunctions, but not after Conditional Automation malfunctions when subjects were able to take over vehicle control to avoid danger. EEG analyses reveal that during Full Automation malfunctions, there was a significant enhancement in approach motivation (i.e. desire to re-engage) and a disruption of right frontal functional clustering that supports executive cognition (i.e. planning and decision-making). No neurocognitive disruptions were observed during Conditional Automation malfunctions. Our results demonstrate that it is not automation malfunctions per se (e.g. failure to decelerate) that deteriorate trust, but rather the inability for human drivers to adaptively mitigate the risk of negative outcomes (e.g. risk of crashing) resulting from those malfunctions. This is reflected in changes in brain activity associated with motivational state and action planning. Keeping the human driver on-the-loop protects against trust loss. Frontal alpha EEG is a neural correlate of trust-in-automation, with potential for trust monitoring using wearable technology to support driver-vehicle adaptivity.

Language: en

Keywords

Automation; Autonomous vehicles; autonomous vehicles (AVs); Brain modeling; cognition; electroencephalography; Electroencephalography; Human factors; Junctions; Monitoring