Now or never

Abstract

Merging onto a highway is a safety-critical task resulting in a large number of traffic accidents; fundamental research into merging behavior of human drivers can help reduce this toll. Two cognitive processes critical to merging, attention allocation and decision making, have been extensively studied in real-world and simulated driving scenarios. However, how these processes interact during highway merging remains poorly understood. While the relationship between attention and decision making has been widely examined in cognitive science, this work has largely relied on simple decision-making paradigms involving choices between static items on a computer screen, which limits the understanding of more dynamic and naturalistic decisions such as in driving. To address this gap, we investigated the relationship between attention and decision making in a simplified highway merging task. In a video-based experiment, participants (N=24) repeatedly made merging gap acceptance decisions based on the dynamic information about the distance and time-to-arrival to the end of the merging lane and the gap to the target-lane vehicle (available in the front view and the side mirror, respectively). Participants’ decisions, response times, and eye movements were recorded. We found that decisions to accept a gap were considerably faster than decisions to reject a gap. Decision outcomes and timing depended on the distance to and time-to-arrival of the target-lane vehicle, but also on the time pressure due to approaching the end of the merging lane. Most importantly, under high time pressure, a greater proportion of time spent looking at the side mirror was associated with a lower probability of accepting the gap. This finding indicates that differences in visual information sampling can be closely linked to decision outcomes when time budgets are constrained. Our results provide initial empirical insights relevant for future cognitive modeling of the interplay between decision making and attention during highway merging. This work can inform early-stage exploration of driver monitoring and support systems for partially automated driving.