Detection of collision events on curved trajectories: Optical information from invariant rate-of-bearing change

Ni, Rui; Andersen, George J.

doi:10.3758/pp/70.7.1314

Cited by 20 publications

(17 citation statements)

References 45 publications

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“…The approaching bright red spherical object (2.4 m diameter) translated towards the observer at a speed of 90 km/h. The object motion trajectories were generated using the same method employed in previous research (Andersen and Enriquez, 2006; Ni and Andersen, 2008). The initial position of the object was chosen randomly from an arc (+/− 20 degrees from the center of the display) at a fixed distance of 223 m from the viewpoint.…”

Section: Methodsmentioning

confidence: 99%

“…Under these conditions a collision event is defined by the combination of two sources of information: a constant bearing in the visual scene (i.e. the position of the object in the visual field is constant) and expansion of the object (Andersen, Cisneros, Atchley and Saidpour, 1999; See also Ni and Andersen, 2008). For this type of collision event two factors might affect the ability of drivers to detect a collision when fog is present.…”

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confidence: 99%

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Aging and the detection of imminent collisions under simulated fog conditions

Bian

Guindon

et al. 2012

Accident Analysis & Prevention

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The present study examined age-related differences in collision detection performance when contrast of the driving scene was reduced by simulated fog. Older and younger drivers were presented with a collision detection scenario in a simulator in which an object moved at a constant speed on a linear trajectory towards the driver. Drivers were shown part of the motion path of an approaching object that would eventually either collide with or pass by the driver and were required to determine whether or not the object would collide with the driver. Driver motion was either stationary or moving along a linear path down the roadway. A no fog condition and three different levels of fog were examined. Detection performance decreased when dense fog was simulated for older but not for younger observers. An age-related decrement was also found with shorter display durations (longer time to contact). When the vehicle was moving decrements in performance were observed for both younger and older drivers. These results suggest that under inclement weather conditions with reduced visibility, such as fog, older drivers may have an increased crash risk due to a decreased ability to detect impending collision events.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Aging and the detection of imminent collisions under simulated fog conditions

Bian

Guindon

et al. 2012

Accident Analysis & Prevention

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“…The BA model is widely used to account for collision detection, interception, and obstacle avoidance in both humans (Cutting et al, 1995; Lenoir et al, 1999, 2002; Chardenon et al, 2004; Fajen and Warren, 2004; Ni and Andersen, 2008; Shaffer and Gregory, 2009) and non-human animals (Lanchester and Mark, 1975; Olberg et al, 2000; Ghose et al, 2006). In addition, the behavioral dynamics model (Fajen and Warren, 2003; Warren, 2006), which is one of the few general models of visually guided locomotion in humans, implements the BA strategy in its moving target (Fajen and Warren, 2007) and moving obstacle (Cohen et al, 2006) components.…”

Section: The Bearing Angle Modelmentioning

confidence: 99%

Guiding locomotion in complex, dynamic environments

Fajen¹

2013

Front. Behav. Neurosci.

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Locomotion in complex, dynamic environments is an integral part of many daily activities, including walking in crowded spaces, driving on busy roadways, and playing sports. Many of the tasks that humans perform in such environments involve interactions with moving objects—that is, they require people to coordinate their own movement with the movements of other objects. A widely adopted framework for research on the detection, avoidance, and interception of moving objects is the bearing angle model, according to which observers move so as to keep the bearing angle of the object constant for interception and varying for obstacle avoidance. The bearing angle model offers a simple, parsimonious account of visual control but has several significant limitations and does not easily scale up to more complex tasks. In this paper, I introduce an alternative account of how humans choose actions and guide locomotion in the presence of moving objects. I show how the new approach addresses the limitations of the bearing angle model and accounts for a variety of behaviors involving moving objects, including (1) choosing whether to pass in front of or behind a moving obstacle, (2) perceiving whether a gap between a pair of moving obstacles is passable, (3) avoiding a collision while passing through single or multiple lanes of traffic, (4) coordinating speed and direction of locomotion during interception, (5) simultaneously intercepting a moving target while avoiding a stationary or moving obstacle, and (6) knowing whether to abandon the chase of a moving target. I also summarize data from recent studies that support the new approach.

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“…In order to avoid crashes and safely navigate drivers must perform a wide range of visual tasks including steering control (Wallis, Chatziastros, Tresilian & Tomasevic, 2007; Hildreth, Beusmans, Boer & Royden, 2000), collision detection (Andersen & Kim, 1999; Andersen, Cisneros, Atchley, & Saidpour, 1999; Ni & Andersen, 2008), braking (Rock, Harris & Yates, 2006; Fajen, 2005), and car following (Andersen & Sauer, 2007). Often drivers must perform several tasks within short time intervals (e.g., maintain a safe headway distance from a lead vehicle while montoring traffic signals).…”

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confidence: 99%

Limits of spatial attention in three-dimensional space and dual-task driving performance

Andersen

Bian

et al. 2011

Accident Analysis & Prevention

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The present study examined the limits of spatial attention while performing two driving relevant tasks that varied in depth. The first task was to maintain a fixed headway distance behind a lead vehicle that varied speed. The second task was to detect a light-change target in an array of lights located above the roadway. In Experiment 1 the light detection task required drivers to encode color and location. The results indicated that reaction time to detect a light-change target increased and accuracy decreased as a function of the horizontal location of the light-change target and as a function of the distance from the driver. In a second experiment the light change task was changed to a singleton search (detect the onset of a yellow light) and the workload of the car following task was systematically varied. The results of Experiment 2 indicated that RT increased as a function of task workload, the 2D position of the light-change target and the distance of the light-change target. A multiple regression analysis indicated that the effect of distance on light detection performance was not due to changes in the projected size of the light target. In Experiment 3 we found that the distance effect in detecting a light change could not be explained by the location of eye fixations. The results demonstrate that when drivers attend to a roadway scene attention is limited in three-dimensional space. These results have important implications for developing tests for assessing crash risk among drivers as well as the design of in vehicle technologies such as head-up displays.

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Detection of collision events on curved trajectories: Optical information from invariant rate-of-bearing change

Cited by 20 publications

References 45 publications

Aging and the detection of imminent collisions under simulated fog conditions

Aging and the detection of imminent collisions under simulated fog conditions

Guiding locomotion in complex, dynamic environments

Limits of spatial attention in three-dimensional space and dual-task driving performance

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