How do drivers perceive risks during automated driving scenarios? An fNIRS neuroimaging study - Perello-March, Jaume; Burns, Christopher G.; Woodman, Roger; Birrell, Stewart; Elliott, Mark T. (Sage, 2024)
Objective Using brain haemodynamic responses to measure perceived risk from traffic complexity during automated driving. Background Although well-established during manual driving, the effects of driver risk perception during automated driving remain unknown. The use of fNIRS in this paper for assessing drivers’ states posits it could become a novel method for measuring risk perception. Methods Twenty-three volunteers participated in an empirical driving simulator experiment with automated driving capability. Driving conditions involved suburban and urban scenarios with varying levels of traffic complexity, culminating in an unexpected hazardous event. Perceived risk was measured via fNIRS within the prefrontal cortical haemoglobin oxygenation and from self-reports. Results Prefrontal cortical haemoglobin oxygenation levels significantly increased, following self-reported perceived risk and traffic complexity, particularly during the hazardous scenario. Conclusion This paper has demonstrated that fNIRS is a valuable research tool for measuring variations in perceived risk from traffic complexity during highly automated driving. Even though the responsibility over the driving task is delegated to the automated system and dispositional trust is high, drivers perceive moderate risk when traffic complexity builds up gradually, reflected in a corresponding significant increase in blood oxygenation levels, with both subjective (self-reports) and objective (fNIRS) increasing further during the hazardous scenario. Application Little is known regarding the effects of drivers’ risk perception with automated driving. Building upon our experimental findings, future work can use fNIRS to investigate the mental processes for risk assessment and the effects of perceived risk on driving behaviours to promote the safe adoption of automated driving technology.
Keywords: aggressive and risky driving; autonomous driving; cognitive neuroscience; human-automation interaction; risk assessment