Use of change blindness to measure different abilities to detect relevant changes in natural driving scenes

Change detection in urban and rural driving scenes: Effects of target type and safety relevance on change blindness

“…were also better at detecting a car appearing in urban versus rural environments, which the authors 53 suggested could be due to contrast and salience (which was lower in rural images) and/or 54 expectations (i.e., drivers expect cars to appear suddenly in urban areas; Koustanaï et al, 2012). 55 found young adult drivers were less likely to detect changes when accompanied by an attractive 121 opposite-sex passenger, compared with participants driving alone.…”

“…Finally, a French study using a one-shot task manipulated the relevance of changes involving 48 cars (Koustanaï et al, 2012). A car was either added or moved (e.g., to simulate turning, or to appear 49 closer) within a driving scene, and task instructions were varied to manipulate the relevance of these 50 changes.…”

The scene and the unseen: Manipulating photographs for experiments on change blindness and scene memory

“…Examples include studies of change detection in traffic situations (Galpin et al, 2009;Koustanaï, Van Elslande, & Bastien, 2012), video camera surveillance (Scott-Brown & Cronin, 2007), studies on the role of expertise in perception (Curran, Gibson, Horne, Young, & Bozell, 2009;Jones, Jones, Smith, & Copley, 2003;Werner & Thies, 2000), or studies on the interplay between bottom-up and top-down processes in scene perception (Stirk & Underwood, 2007). In sum, we believe that the benefits of using natural images for change detection may outweigh the time necessary to create them.…”

A multinomial choice model approach for dynamic driver vision transitions

“…Attributes related to salience, such as color and conspicuity, represent the ease of differentiating and identifying targets (Gershon et al, 2012;Koustanaï et al, 2012;Mcintyre et al, 2012). Unfortunately, the 100-car event database did not provide the salience information of each glanced target.…”

“…Drivers focus primarily on future trajectories where they may encounter possible threats (Summala et al, 1996;Vollrath, 2012, 2013). Areas containing threats involving high crash risk might attract greater attention from drivers than other less relevant focal points (Martens and Fox, 2007;Koustanaï et al, 2012). Thus, this model took the intersection into considerations to determine the possible effect of drivers approaching or passing an intersection.…”