2005
DOI: 10.1037/0096-1523.31.3.480
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Abstract: This study explored visual control strategies for braking to avoid collision by manipulating information about speed of self-motion. Participants watched computer-generated displays and used a brake to stop at an object in the path of motion. Global optic flow rate and edge rate were manipulated by adjusting eye-height and ground-texture size. Stopping distance, initiation of braking, and the magnitude of brake adjustments were influenced by both optical variables, but global optic flow rate had a stronger eff… Show more

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Cited by 87 publications
(147 citation statements)
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“…It is well established that something like a threshold on might be determining brake onset timing in routine driving (Kiefer et al, 2003(Kiefer et al, , 2005Treiber et al, 2013;Kusano et al, 2015), which makes sense given that, at least for stationary obstacles, can be used to estimate the deceleration required to avoid collision (Fajen, 2005). However, the present results tentatively suggest that in critical situations, drivers do not factor in their own speed into their braking decision.…”
Section: Deceleration Timing From Looming Thresholds?contrasting
confidence: 53%
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“…It is well established that something like a threshold on might be determining brake onset timing in routine driving (Kiefer et al, 2003(Kiefer et al, , 2005Treiber et al, 2013;Kusano et al, 2015), which makes sense given that, at least for stationary obstacles, can be used to estimate the deceleration required to avoid collision (Fajen, 2005). However, the present results tentatively suggest that in critical situations, drivers do not factor in their own speed into their braking decision.…”
Section: Deceleration Timing From Looming Thresholds?contrasting
confidence: 53%
“…Overall, a much more powerful explanation for the present observations can be obtained from response threshold models of brake timing, which postulate that drivers initiate braking once some visual cue, for example , reaches a certain threshold value (Lee, 1976;Kiefer et al, 2003Kiefer et al, , 2005Flach et al, 2004;Fajen, 2005Fajen, , 2008Wada et al, 2009;Treiber et al, 2013). Previously, such models have mainly been studied in the context of routine driving, but the results presented here, for example in Figure 6, suggest that a threshold-based model also works very well for describing behavior in surprise rear-end emergencies: In eyes-on-threat events, few drivers responded before visual looming reached s (or rad s, or m s ), and most drivers responded within a second after reaching this threshold 3 .…”
Section: Deceleration Timing From Looming Thresholds?mentioning
confidence: 99%
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“…These tasks include steering control (Wallis, Chatziastros, Tresilian & Tomasevic, 2007), braking (Fajen, 2005), car following (Ni, Kang & Andersen, 2010), and collision detection (Ni & Andersen, 2008). Previous studies have shown that observers' ability to attend and process information in a visual scene is very limited (Rensink, 2002).…”
Section: Introductionmentioning
confidence: 99%