The use of cardiac-based metrics to assess secondary task engagement during automated and manual driving: An experimental simulation study

Most driver monitoring systems (DMS) rely on cameras facing the driver while detecting their gaze or head position. Both future automated driving (AD) in-vehicle interactions and AD vehicle interior designs (e.g., seating arrangement) might drastically reduce the effectiveness of such camera-based DMS solutions, however. Thus, alternative solutions that do not rely on cameras, and therefore compatible with upcoming AD journey experiences, are worth being investigated. Here, we studied the behavior of several cardiac-based indices. We aimed to determine the effects of engaging in non-driving-related tasks (NDRT) in a semi-dynamic driving simulator on heart rate and heart rate variability parameters (here, we report the standard deviation of R-R intervals [SDRR]). We developed a 2 (AD vs. manual driving [MD] modalities) by 2 (one-hand vs. two-hand concurrent Task modality) within-participants experimental design. Thirty-two expert drivers drove along two highway scenarios (∼ 22 minutes each) in daylight conditions. Each scenario included four distraction periods. In each distraction period, participants performed a concurrent task in addition to their main task (i.e., driving the simulator during MD, supervising the system during AD). We monitored participants’ cardiac activity and collected performance levels on the NDRT, driving performance, as well as subjective ratings of task load. Cardiac-based indices clearly differentiated Task modality, with the two-hand task inducing higher heart rate and SDRR. Driving modality (MD vs. AD) only influenced heart rate, which increased during MD. Driving performance and subjective ratings, as well as performance on the NDRT, were able to reflect the experimental manipulation, with the two-hand concurrent task (in MD) being the most disruptive and demanding condition. Overall, these findings have the potential to improve future DMS design and road safety by providing accurate measurements of driver engagement. They can be key to assess future driver-vehicle interactions using for example, non-contact, more realistic, heart-rate radar-based sensor solutions.