How do environmental characteristics at intersections change in their relevance for drivers before entering an intersection: analysis of drivers’ gaze and driving behavior in a driving simulator study

Artikel i vetenskaplig tidskrift, 2014

Abstract Accident studies in Germany found that in about 90 % of intersection accidents, failure to acquire the relevant information of the driving situation was the main reason for drivers’ errors (Vollrath et al. in Ableitung von Anforderungen an Fahrerassistenzsysteme aus Sicht der Verkehrssicherheit. Wirtschaftsverlag NW, Bremerhaven, 2006). Studies of bicycle–car accidents assume that improper attention allocation strategies and unjustified expectations by drivers are important for this kind of error (Ra¨sa¨nen and Summala in Accid Anal Prev 30:657–666, 1998). Aim of the study was to examine the psychological processes of drivers’ attention allocation and driving behavior in different intersection situations varied by two environmental characteristics. A give way T-intersection was varied by (1) low and high traffic density of oncoming cars from the left and (2) number of task-relevant information areas (in addition to the oncoming cars from the left with or without pedestrians on the right). It was examined how these environmental characteristics change in their relevance for drivers while entering the intersections. The analysis was conducted in three intersection epochs (Approaching, Waiting, Accelerating). A total of 40 subjects (26 male, 14 female), ranged in age from 19 to 55 years (M = 31.0 years), participated in the study. The results showed that drivers’ attention allocation (e.g., mean gaze duration) and driving behavior (e.g., waiting time) systematically depends on these environmental characteristics which require different actions of the driver and change in their relevance when entering an intersection. The results support the idea of attention allocation strategies by drivers which are specific for certain driving situations. These findings can support approaches of driver modeling at intersections.