Linking the Detection Response Task and the AttenD Algorithm through the Assessment of Human-Machine Interface Workload


Failures in drivers’ attention allocation become evident when multi-tasking related demands leave vehicle operators unable to detect or respond appropriately to roadway threats or interfere adversely with their ability to appropriately control the vehicle. Robust methods for obtaining evidence and data about demands upon and decrements in the allocation of driver attention are needed as input for design, training, and policy. The detection response task (DRT) is one method (ISO 17488, 2016) that has been forwarded as a method for measuring the attentional effects of cognitive load. The AttenD algorithm is a method intended to measure driver distraction through real-time glance analysis, in which individual glances are converted into a scalar value using simple rules considering glance duration, frequency, and location. In the present work, a relationship between the two tools is explored. A previous multi-tasking driving simulation study, which used the DRT to differentiate the demands of a primary visual-manual human-machine interface (HMI) from alternative auditory-vocal involved multi-modal HMIs, was reanalyzed using AttenD, and the two analyses compared. Results support an association between DRT performance and AttenD algorithm output. Summary statistics produced from AttenD profiles differentiate between the demands of the HMIs considered with more power than analyses of DRT response time and miss rate. Among discussed implications is the possibility that AttenD taps some of the same attentional effects as the DRT. Future research paths, strategies for analysis of past and future datasets, and possible application in driver state- detection are also discussed.