The driving visual field and a history of motor vehicle collision involvement in older drivers: A population-based examination

Purpose We designed a visual field test focused on the field utilized while driving to examine associations between field impairment and motor vehicle collision involvement in 2,000 drivers ≥70 years old. Methods The "driving visual field test" involved measuring light sensitivity for 20 targets in each eye, extending 15° superiorly, 30° inferiorly, 60° temporally and 30° nasally. The target locations were selected on the basis that they fell within the field region utilized when viewing through the windshield of a vehicle or viewing the dashboard while driving. Monocular fields were combined into a binocular field based on the more sensitive point from each eye. Severe impairment in the overall field or a region was defined as average sensitivity in the lowest quartile of sensitivity. At-fault collision involvement for five years prior to enrollment was obtained from state records. Poisson regression was used to calculate crude and adjusted rate ratios examining the association between field impairment and at-fault collision involvement. Results Drivers with severe binocular field impairment in the overall driving visual field had a 40% increased rate of at-fault collision involvement (RR 1.40, 95%CI 1.07-1.83). Impairment in the lower and left fields was associated with elevated collision rates (RR 1.40 95%CI 1.07-1.82 and RR 1.49, 95%CI 1.15-1.92, respectively), whereas impairment in the upper and right field regions was not. Conclusions Results suggest that older drivers with severe impairment in the lower or left region of the driving visual field are more likely to have a history of at-fault collision involvement.

Differential effects of refractive blur on day and night-time driving performance

Purpose To investigate the effect of different levels of refractive blur on real-world driving performance measured under day and nighttime conditions. Methods Participants included 12 visually normal, young adults (mean age = 25.8 ± 5.2 years) who drove an instrumented research vehicle around a 4 km closed road circuit with three different levels of binocular spherical refractive blur (+0.50 diopter sphere [DS], +1.00 DS, +2.00 DS) compared with a baseline condition. The subjects wore optimal spherocylinder correction and the additional blur lenses were mounted in modified full-field goggles; the order of testing of the blur conditions was randomized. Driving performance was assessed in two different sessions under day and nighttime conditions and included measures of road signs recognized, hazard detection and avoidance, gap detection, lane-keeping, sign recognition distance, speed, and time to complete the course. Results Refractive blur and time of day had significant effects on driving performance (P < 0.05), where increasing blur and nighttime driving reduced performance on all driving tasks except gap judgment and lane keeping. There was also a significant interaction between blur and time of day (P < 0.05), such that the effects of blur were exacerbated under nighttime driving conditions; performance differences were evident even for +0.50 DS blur relative to baseline for some measures. Conclusions The effects of blur were greatest under nighttime conditions, even for levels of binocular refractive blur as low as +0.50 DS. These results emphasize the importance of accurate and up-to-date refractive correction of even low levels of refractive error when driving at night.

Towards Vision-Based Pose- and Condition-Invariant Place Recognition along Routes

Vision-based place recognition involves recognising familiar places despite changes in environmental conditions or camera viewpoint (pose). Existing training-free methods exhibit excellent invariance to either of these challenges, but not both simultaneously. In this paper, we present a technique for condition-invariant place recognition across large lateral platform pose variance for vehicles or robots travelling along routes. Our approach combines sideways facing cameras with a new multi-scale image comparison technique that generates synthetic views for input into the condition-invariant Sequence Matching Across Route Traversals (SMART) algorithm. We evaluate the system’s performance on multi-lane roads in two different environments across day-night cycles. In the extreme case of day-night place recognition across the entire width of a four-lane-plus-median-strip highway, we demonstrate performance of up to 44% recall at 100% precision, where current state-of-the-art fails.

Retinal anatomy of the New Zealand kiwi: Structural traits consistent with their nocturnal behavior

Kiwi (Apteryx spp.) have a visual system unlike that of other nocturnal birds, and have specializations to their auditory, olfactory and tactile systems. Eye size, binocular visual fields and visual brain centers in kiwi are proportionally the smallest yet recorded among birds. Given the many unique features of the kiwi visual system, we examined the laminar organization of the kiwi retina to determine if they evolved increased light sensitivity with a shift to a nocturnal niche or if they retained features of their diurnal ancestor. The laminar organization of the kiwi retina was consistent with an ability to detect low light levels similar to that of other nocturnal species. In particular, the retina appeared to have a high proportion of rod photoreceptors compared to diurnal species, as evidenced by a thick outer nuclear layer, and also numerous thin photoreceptor segments intercalated among the conical shaped cone photoreceptor inner segments. Therefore, the retinal structure of kiwi was consistent with increased light sensitivity, although other features of the visual system, such as eye size, suggest a reduced reliance on vision. The unique combination of a nocturnal retina and smaller than expected eye size, binocular visual fields and brain regions make the kiwi visual system unlike that of any bird examined to date. Whether these features of their visual system are an evolutionary design that meets their specific visual needs or are a remnant of a kiwi ancestor that relied more heavily on vision is yet to be determined.

Kiwi Forego Vision in the Guidance of Their Nocturnal Activities

Background In vision, there is a trade-off between sensitivity and resolution, and any eye which maximises information gain at low light levels needs to be large. This imposes exacting constraints upon vision in nocturnal flying birds. Eyes are essentially heavy, fluid-filled chambers, and in flying birds their increased size is countered by selection for both reduced body mass and the distribution of mass towards the body core. Freed from these mass constraints, it would be predicted that in flightless birds nocturnality should favour the evolution of large eyes and reliance upon visual cues for the guidance of activity. Methodology/Principal Findings We show that in Kiwi (Apterygidae), flightlessness and nocturnality have, in fact, resulted in the opposite outcome. Kiwi show minimal reliance upon vision indicated by eye structure, visual field topography, and brain structures, and increased reliance upon tactile and olfactory information. Conclusions/Significance This lack of reliance upon vision and increased reliance upon tactile and olfactory information in Kiwi is markedly similar to the situation in nocturnal mammals that exploit the forest floor. That Kiwi and mammals evolved to exploit these habitats quite independently provides evidence for convergent evolution in their sensory capacities that are tuned to a common set of perceptual challenges found in forest floor habitats at night and which cannot be met by the vertebrate visual system. We propose that the Kiwi visual system has undergone adaptive regressive evolution driven by the trade-off between the relatively low rate of gain of visual information that is possible at low light levels, and the metabolic costs of extracting that information.

Large-scale habitat mapping using vision-based AUVs : experiences, challenges & vehicle design

This paper overviews the development of a vision-based AUV along with a set of complementary operational strategies to allow reliable autonomous data collection in relatively shallow water and coral reef environments. The development of the AUV, called Starbug, encountered many challenges in terms of vehicle design, navigation and control. Some of these challenges are discussed with focus on operational strategies for estimating and reducing the total navigation error when using lower-resolution sensing modalities. Results are presented from recent field trials which illustrate the ability of the vehicle and associated operational strategies to enable rapid collection of visual data sets suitable for marine research applications.

Towards coordinated vision-based docking using an Autonomous Surface Vehicle

This paper describes the development and experimental evaluation of a novel vision-based Autonomous Surface Vehicle with the purpose of performing coordinated docking manoeuvres with a target, such as an Autonomous Underwater Vehicle, on the water’s surface. The system architecture integrates two small processor units; the first performs vehicle control and implements a virtual force obstacle avoidance and docking strategy, with the second performing vision-based target segmentation and tracking. Furthermore, the architecture utilises wireless sensor network technology allowing the vehicle to be observed by, and even integrated within an ad-hoc sensor network. The system performance is demonstrated through real-world experiments.

Vision-based operations of a large industrial vehicle : autonomous hot metal carrier

Hot metal carriers (HMCs) are large forklift-type vehicles used to move molten metal in aluminum smelters. This paper reports on field experiments that demonstrate that HMCs can operate autonomously and in particular can use vision as a primary sensor to locate the load of aluminum. We present our complete system but focus on the vision system elements and also detail experiments demonstrating reliable operation of the materials handling task. Two key experiments are described, lasting 2 and 5 h, in which the HMC traveled 15 km in total and handled the load 80 times.

Improving robot vision models for object detection through interaction

We propose a method for learning specific object representations that can be applied (and reused) in visual detection and identification tasks. A machine learning technique called Cartesian Genetic Programming (CGP) is used to create these models based on a series of images. Our research investigates how manipulation actions might allow for the development of better visual models and therefore better robot vision. This paper describes how visual object representations can be learned and improved by performing object manipulation actions, such as, poke, push and pick-up with a humanoid robot. The improvement can be measured and allows for the robot to select and perform the `right' action, i.e. the action with the best possible improvement of the detector.

Vision-based control for mining automation

The mining industry is highly suitable for the application of robotics and automation technology since the work is both arduous and dangerous. Visual servoing is a means of integrating noncontact visual sensing with machine control to augment or replace operator based control. This article describes two of our current mining automation projects in order to demonstrate some, perhaps unusual, applications of visual servoing, and also to illustrate some very real problems with robust computer vision

Special issue on robot vision

The International Journal of Robotics Research (IJRR) has a long history of publishing the state-of-the-art in the field of robotic vision. This is the fourth special issue devoted to the topic. Previous special issues were published in 2012 (Volume 31, No. 4), 2010 (Volume 29, Nos 2–3) and 2007 (Volume 26, No. 7, jointly with the International Journal of Computer Vision). In a closely related field was the special issue on Visual Servoing published in IJRR, 2003 (Volume 22, Nos 10–11). These issues nicely summarize the highlights and progress of the past 12 years of research devoted to the use of visual perception for robotics.

A roadmap for interpreting the literature on vision and driving

Over the past several decades there has been a sharp increase in the number of studies focused on the relationship between vision and driving. The intensified attention to this topic has most likely been stimulated by the lack of an evidence basis for determining vision standards for driving licensure and a poor understanding about how vision impairment impacts driver safety and performance. Clinicians depend on the literature on vision and driving to advise visually impaired patients appropriately about driving fitness. Policy makers also depend on the scientific literature in order to develop guidelines that are evidence-based and are thus fair to persons who are visually impaired. Thus it is important for clinicians and policy makers alike to understand how various study designs and measurement methods should be interpreted so that the conclusions and recommendations they make are not overly broad, too narrowly constrained, or even misguided. We offer a methodological framework to guide interpretations of studies on vision and driving that can also serve as a heuristic for researchers in the area. Here, we discuss research designs and general measurement methods for the study of vision as they relate to driver safety, driver performance, and driver-centered (self-reported) outcomes.

Fear of falling in vision impairment

Falls are the leading cause of injury-related morbidity and mortality among older adults. In addition to the resulting physical injury and potential disability after a fall, there are also important psychological consequences, including depression, anxiety, activity restriction, and fear of falling. Fear of falling affects 20 to 43% of community-dwelling older adults and is not limited to those who have previously experienced a fall. About half of older adults who experience fear of falling subsequently restrict their physical and everyday activities, which can lead to functional decline, depression, increased falls risk, and reduced quality of life. Although there is clear evidence that older adults with visual impairment have higher falls risk, only a limited number of studies have investigated fear of falling in older adults with visual impairment and the findings have been mixed. Recent studies suggest increased levels of fear of falling among older adults with various eye conditions, including glaucoma and age-related macular degeneration, whereas other studies have failed to find differences. Interventions, which are still in their infancy in the general population, are also largely unexplored in those with visual impairment. The major aims of this review were to provide an overview of the literature on fear of falling, its measurement, and risk factors among older populations, with specific focus on older adults with visual impairment, and to identify directions for future research in this area.