Assessing driver cortical activity under varying levels of automation with functional near infrared spectroscopy

Abstract

Information about drivers' mental states can be vital to the design of interfaces for highly automated vehicles. Functional near infrared spectroscopy (fNIRS) is a neuroimaging tool that is fast becoming popular to study the cortical activity of participants in HCI experiments and driving simulator studies in particular. The analysis methods of the fNIRS data create requirements in the experimental design such as repeated measures. In this paper, we present a study of the event related cortical activity of the drivers of manual, partially autonomous, and fully autonomous cars when performing lane changes using functional near infrared spectroscopic measures. We also present the experimental methodology that was adopted to meet the needs of the fNIRS measurement and the subsequent analysis. The study (N=28) was conducted in a driving simulator. Participants drove for approximately 7 minutes and performed 8 lane change maneuvers in each mode of automation. Multiple streams of data including 4 time-synced video recordings, NASA TLX questionnaires and fNIRS data were recorded and analyzed. It was found that the dorsolateral prefrontal cortex activation during lane changes performed in a partially autonomous mode of operation was just as high as that during a manual lane change, showing that drivers of partially automated systems are as cognitively engaged as drivers of manually operated vehicles.