Gaze behavior when approaching an intersection: Dwell time distribution and comparison with a quantitative prediction. (November 2015) - Lemonnier, Sophie; Brémond, Roland; Baccino, Thierry
Highlights: The approach of an intersection is investigated in terms of visual attention. Oculometric data is collected in a driving simulator experiment. Area of interests are associated with the driving subtasks. Dwell time and transition are compared in Stop, Yield, and Priority intersections. A quantitative, predictive model of the dwell time is compared to the data. Abstract: The allocation of overt visual attention is investigated in a multi-task and dynamical situation: driving. The Expectancy–Value model of attention allocation stipulates that visual exploration depends on the expectancy and the value of the task-related information available in each Area Of Interest (AOI). We consider the approach to an intersection as a multi-task situation where two subtasks are involved: vehicle control and interactions with other drivers. Each of these subtasks is associated with some specific visual information present in the associated AOIs: the driver's lane and the intersecting road at the intersection. An experiment was conducted in a driving simulator, coupled with a head-mounted eye-tracker. The intersecting road's AOI's Expectancy was manipulated with the traffic density, and its Value was manipulated with the priority rule before the intersection (stop, yield, and priority). The distribution of visual attention and the dynamics of visual exploration were analyzed on 20 participants, taking into account the dwell time in the AOIs associated to the driving subtasks, and the gaze transitions between the AOIs. The results suggest that visual attention to intersecting roads varied with the priority rule, and impacted the visual attention associated with the vehicle control subtask. In addition, a quantitative model was used to improve the understanding of the Expectancy and Value factors. The comparison of the data with the model's predictions enables quantifying the observed differences between the experimental factors. Finally, the results associated with the traffic density are discussed in relation to the nature of the relevant information while approaching the intersection.