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Pedestrians with low vision are at risk of injury when hazards, such as steps and posts, have low visibility. This study aims at validating the software implementation of a computational model that estimates hazard visibility. The model takes as input a photorealistic 3D rendering of an architectural space, and the acuity and contrast sensitivity of a low-vision observer, and outputs estimates of the visibility of hazards in the space. Our experiments explored whether the model could predict the likelihood of observers correctly identifying hazards. In Experiment 1, we tested fourteen normally sighted subjects with blur goggles that simulated moderate or severe acuity reduction. In Experiment 2, we tested ten low-vision subjects with moderate to severe acuity reduction. Subjects viewed computer-generated images of a walkway containing five possible targets ahead-big step-up, big step-down, small step-up, small step-down, or a flat continuation. Each subject saw these stimuli with variations of lighting and viewpoint in 250 trials and indicated which of the five targets was present. The model generated a score on each trial that estimated the visibility of the target. If the model is valid, the scores should be predictive of how accurately the subjects identified the targets. We used logistic regression to examine the correlation between the scores and the participants' responses. For twelve of the fourteen normally sighted subjects with artificial acuity reduction and all ten low-vision subjects, there was a significant relationship between the scores and the participant's probability of correct identification. These experiments provide evidence for the validity of a computational model that predicts the visibility of architectural hazards. It lays the foundation for future validation of this hazard evaluation tool, which may be useful for architects to assess the visibility of hazards in their designs, thereby enhancing the accessibility of spaces for people with low vision., Competing Interests: The authors have declared that no competing interests exist.

Architects and lighting designers have difficulty designing spaces that are accessible to those with low vision, since the complex nature of most architectural spaces requires a site-specific analysis of the visibility of mobility hazards and key landmarks needed for navigation. We describe a method that can be utilized in the architectural design process for simulating the effects of reduced acuity and contrast on visibility. The key contribution is the development of a way to parameterize the simulation using standard clinical measures of acuity and contrast sensitivity. While these measures are known to be imperfect predictors of visual function, they provide a way of characterizing general levels of visual performance that is familiar to both those working in low vision and our target end-users in the architectural and lighting-design communities. We validate the simulation using a letter-recognition task.

For low vision navigation, misperceiving the locations of hazards can have serious consequences. Potential sources of such misperceptions are hazards that are not visually associated with the ground plane, thus, depriving the viewer of important perspective cues for egocentric distance. In Experiment 1, we assessed absolute distance and size judgments to targets on stands under degraded vision conditions. Normally sighted observers wore blur goggles that severely reduced acuity and contrast, and viewed targets placed on either detectable or undetectable stands. Participants in the detectable stand condition demonstrated accurate distance judgments, whereas participants in the undetectable stand condition overestimated target distances. Similarly, the perceived size of targets in the undetectable stand condition was judged to be significantly larger than in the detectable stand condition, suggesting a perceptual coupling of size and distance in conditions of degraded vision. In Experiment 2, we investigated size and implied distance perception of targets elevated above a visible horizon for individuals in an induced state of degraded vision. When participants' size judgments are inserted into the size-distance invariance hypothesis (SDIH) formula, distance to above-horizon objects increased compared to those below the horizon. Together, our results emphasize the importance of salient visible ground-contact information for accurate distance perception. The absence of this ground-contact information could be the source of perceptual errors leading to potential hazards for low vision individuals with severely degraded acuity and contrast sensitivity.

Critical to low-vision navigation are the abilities to recover scale and update a 3-D representation of space. In order to investigate whether these abilities are present under low-vision conditions, we employed the triangulation task of eyes-closed indirect walking to previously viewed targets on the ground. This task requires that the observer continually update the location of the target without any further visual feedback of his/her movement or the target's location. Normally sighted participants were tested monocularly in a degraded vision condition and a normal vision condition on both indirect and direct walking to previously viewed targets. Surprisingly, we found no difference in walked distances between the degraded and normal vision conditions. Our results provide evidence for intact spatial updating even under severely degraded vision conditions, indicating that participants can recover scale and update a 3-D representation of space under simulated low vision.

Most people with low vision rely on their remaining functional vision for mobility. Our goal is to provide tools to help design architectural spaces in which safe and effective mobility is possible by those with low vision – spaces that we refer to as visually accessible. We describe an approach that starts with a 3D CAD model of a planned space and produces labeled images indicating whether or not structures that are potential mobility hazards are visible at a particular level of low vision. There are two main parts to the analysis. The first, previously described, represents low-vision status by filtering a calibrated luminance image generated from the CAD model and associated lighting and materials information to produce a new image with unseen detail removed. The second part, described in this paper, uses both these filtered images and information about the geometry of the space obtained from the CAD model and related lighting and surface material specifications to produce a quantitative estimate of the likelihood of particular hazards being visible. We provide examples of the workflow required, a discussion of the novelty and implications of the approach, and a short discussion of needed future work. [ABSTRACT FROM AUTHOR]