It was an incredible honor to receive the prestigious Glenn Fry Award, and I was humbled to join the ranks of the many impressive vision and clinician scientists who have previously received this award. Importantly, although this was a great personal honor, it also reflects the opportunities provided to me by my home university (Queensland University of Technology), as well as the inspiring researchers that I have collaborated with over many years. I have been lucky enough to host some of these researchers during their sabbatical visits to Australia or have worked with them on short sabbatical visits to their own laboratories. Collectively, they have encouraged me to keep going in an area that is practically and logistically challenging and that some might consider “too applied,” but is highly relevant and translatable; indeed, in recent years, translational research like this has become a much more “desirable” approach within the university sector more generally.
In my Glenn Fry Award lecture presented in New Orleans, I provided an overview of my own research on vision and driving within the context of other relevant research in the area, and I emphasized why this topic is so relevant to the optometric profession. This article reflects that approach, providing a current overview of how different types of visual impairment affect driving ability and safety, and also highlights the visual challenges of nighttime driving, including the visibility of vulnerable road users, which has been a particular focus of my research.
VISION AND DRIVING RESEARCH IS IMPORTANT
Over the last few decades, there has been a significant increase in research focusing on the relationship between vision and driving, as evidenced by a tripling of the number of citations to vision and driving research indexed in the research database PubMed since the 1980s.1 This increase in interest is motivated both by a lack of clear evidence for determining vision standards for driving and by a limited understanding of how different types of visual impairment affect driving ability and safety. Policy makers rely on the literature on vision and driving to develop evidence-based guidelines regarding fitness to drive that are fair to drivers with visual impairment but do not compromise the safety of the wider driving population, including vulnerable road users such as pedestrians and cyclists. In addition, clinicians, including optometrists, depend on the scientific literature and government policies to advise their patients regarding whether they meet the visual requirements for fitness to drive. It is therefore imperative that high-quality evidence is available, enabling appropriate decisions to be made regarding drivers with visual impairment and the safety of the driving community more broadly.
MEASURES OF DRIVING ABILITY AND SAFETY
Studies that have investigated the link between vision and driving have used a range of driving outcome measures, which are designed to measure specific aspects of driving or its component skills. However, although each of these measures can contribute to understanding the association between vision and driving, it is important to recognize that the information they provide is different and not necessarily interchangeable.
Driving safety is typically considered in terms of the rate of motor vehicle collisions over a particular period or as a function of driving exposure. At-fault motor vehicle collisions are most useful for better understanding the role of vision in driving, as evidenced by the stronger associations between visual impairment in older drivers when only at-fault motor vehicle collisions rather than all motor vehicle collisions are considered.2 However, given the relative scarcity of motor vehicle collisions, studies of vision and driving have typically included all, rather than only at-fault motor vehicle collisions, to increase the number of outcome events. In addition, many studies exploring the effect of visual impairment on driving have included self-reported motor vehicle collisions rather than state-recorded motor vehicle collisions.3 Although this is less costly and more convenient than having to obtain permission to access motor vehicle collision records from state or police authorities, it is limited by recall bias, particularly in terms of attribution of blame. However, although motor vehicle collisions are the reference standard in terms of driving safety, an overarching limitation is that they do not provide insight into the mechanisms underlying the impact of visual impairment on driving safety, that is, how visual impairment affects a driver's capacity, behaviors, and safety.
Driving simulators provide a method of assessing driving performance under controlled conditions, allow for assessment of driving in situations that would be unsafe to investigate on roads under normal traffic conditions and enable assessment of drivers whose visual function would not legally allow them to drive on normal roads. Simulators are also becoming more common given the availability of lower-cost commercial systems, involving lower installation costs and less ongoing technical support. There are, however, a number of disadvantages of simulators for assessing the impact of visual impairment on driving. The visual displays of the road environment and other road users in simulators typically do not well represent the visual complexity and variable lighting conditions of actual roads, particularly nighttime roads,4 nor is the movement of other road users, such as pedestrians, well captured in these displays. Additional concerns are that the risks encountered while driving in a simulator may be very different to those involved in everyday driving. In addition, poorer driving performance and increased motor vehicle collisions in a simulator may not translate to the real world, and finally, many drivers experience simulator sickness, particularly older individuals,5,6 who are more likely to be included in studies of visual impairment and driving.
Our studies have largely involved investigation of the impact of visual impairment on driving for participants who are current drivers, involving them driving either on a closed-road circuit or on the open road under in-traffic conditions.
The closed road that we have used for more than two decades in our vision and driving studies is owned and managed by the local state transport authority and consists of a network of roads with standard signs and road markings, hills, bends, and straight stretches, which we have used under both daytime and nighttime conditions (Fig. 1). The main advantages of closed-road designs are that the test drives can be standardized across participants and specific components of driving performance can be evaluated. For example, it is possible to focus on road sign recognition, detection and avoidance of road hazards, and lane keeping, as well as the recognition of other road users such as pedestrians and cyclists for nighttime visibility studies. It is also possible to evaluate the effects of true and simulated visual impairments using repeated-measures designs, making it possible to tease out the effect of visual impairment separate from differences in driving behaviors and risk taking incurred in between-group designs. These simulation studies have explored the effects of optical blur,7–9 visual fields,10–13 and cataracts14–16; however, it is acknowledged that, although this approach allows us to isolate the effects of vision alone, the validity of the various simulations must be considered, and there is only limited opportunity for adaptation to the various impairments or for potential compensatory strategies. Although closed-road designs have allowed us to better understand the role of vision in driving, they lack the complexity and interaction with other traffic inherent under normal everyday driving conditions.
Open-road studies involve participants driving a dual-brake vehicle under normal in-traffic conditions, offering the opportunity to observe drivers in a much more natural driving environment. The use of standardized routes and scoring systems allows for a degree of consistency in the driving assessment. These assessments are typically undertaken where a driving instructor or driver-trained occupational therapist is seated in the front passenger seat and is responsible for the safety of the drive (through access to the dual brake and steering wheel). Driving performance is assessed by a backseat evaluator (either a trained researcher or an occupational therapist), who is masked to the visual status of the driver. At selected locations along the drive, evaluators score performance against a range of criteria (e.g., lane positioning, observation, and steering steadiness), and these locations can also be categorized (e.g., traffic light–controlled intersections and give-way), with overall driving safety being rated on either a 5- or a 10-point scale, where higher values represent safer driving. A disadvantage of this approach is that the ratings are subjective impressions of driving ability. To address this, the raters are masked to the visual status of drivers, and we have shown that interrater reliability is relatively high17–19; some of our studies have also included instrumented vehicles that allow for capture of objective measures of driving ability.20,21
Another approach that has become more popular in recent years is naturalistic driving, where a driver's performance is recorded over an extended period using measuring devices installed into his/her vehicle. Such devices need to be small and unobtrusive, which has only been possible in recent years through technological advances in computers, sensors, data storage and communications, and video technology. The advantages of this approach are that driving performance can be measured under normal everyday driving, and it allows for assessment of performance before and during motor vehicle collisions, as well as near misses. Given that motor vehicle collisions are rare events and near misses are much more common, this approach has many advantages but is only possible through the capacity to convert these extensive data sets into meaningful variables that can be analyzed and interpreted, an area that is developing at a rapid pace. Although there have been only a limited number of studies that have used naturalistic techniques to explore the relationship between vision and driving,22,23 this provides an exciting opportunity to better understand this relationship in the future.
IMPACT OF VISUAL IMPAIRMENT ON DRIVING
A significant body of research over many decades has focused on how different types of visual impairment affect driving ability and safety, using a selection of the measurement approaches described in the previous section. However, there have been many limitations to this research, particularly in some of the less recent studies. For example, some of the earlier motor vehicle collision studies were case-control studies that failed to account for confounding factors, and many of the simulator studies include only small sample sizes and individuals with visual impairment who no longer drive.
Our research has taken a different approach, exploring how different types of visual impairment affect driving performance assessed while our participants drive real cars on real roads. These studies have largely been undertaken in closed- and open-road settings for a range of different visual conditions that affect central or peripheral vision. The following provides an overview of some of these studies in the context of the wider literature.
Central Vision Loss
Much of the focus of the research on vision and driving has been on the impact of central visual loss, particularly reductions in visual acuity. Studies over many years have identified only a weak or no significant relationship between reduced visual acuity and increased crash risk.2,24–34 Nevertheless, despite this lack of a strong evidence base, most jurisdictions worldwide include visual acuity in their licensing standards. Given that visual acuity does not well represent the visual complexity of the driving environment, alternative measures of central vision such as contrast sensitivity have been advocated as better measures of vision for driving,31 being more relevant to the impact of ocular diseases that primarily reduce central vision, such as cataracts and age-related macular degeneration, on driving.
Older drivers with cataracts report increased driving difficulties compared with their age-matched counterparts and self-regulate their driving through both avoidance of challenging driving situations (such as driving at nighttime, in poor weather, and in busy traffic) and driving cessation.35,36 Self-regulation has been linked with poorer contrast sensitivity and greater depressive symptoms.37 Despite this self-regulation, the motor vehicle collision risk of older drivers with cataracts is 2.5 times higher than that of age-matched controls,36 and those with a history of motor vehicle collision involvement have been shown to be 8 times more likely to have severe loss of contrast sensitivity in the worse eye (Pelli-Robson score of ≤1.25).38 These findings are supported by our studies of driving performance under closed- and open-road conditions, which have shown that drivers with simulated12,13 and true cataracts39–41 have impaired driving performance compared with controls.
Cataract surgery has been shown to not only result in improvements in visual function but also reduce motor vehicle collision risk by a factor of 2,42 with first-eye surgery having almost three times the benefit of second-eye surgery (61 vs. 23%).43,44 A simulation model based on U.S. data suggested that motor vehicle collision risk is reduced by 21% for earlier surgery, with a net saving of 16% (surgery costs balanced against crash costs) and a 5% increase in Quality Adjusted Life Years.45
In one of our studies, older adults scheduled for bilateral cataract surgery were tested on a closed-road circuit to investigate the benefits of first- and second-eye surgeries on driving performance.46 Visually normal controls were assessed at similar time points to determine whether any improvements in driving ability after surgery in the cataract group were due to repeated testing or true changes in driving performance. Both first- and second-eye surgeries resulted in significant improvements in sign recognition, the ability to detect and avoid large low-contrast obstacles, driving reaction times, and overall driving score, such that at the post–second-eye surgery visit, there was no significant difference in the driving performance between those who had undergone cataract surgery and the controls. Changes in driving performance were significantly associated with improvements in Pelli-Robson contrast sensitivity but not disability glare sensitivity (measured using the Brightness Acuity Tester and the Berkeley Glare Test) or visual acuity and were not significantly associated with self-reported perceptions of driving ability as assessed using the Activities of Daily Vision Scale.41 These findings highlight the importance of measures of visual function other than visual acuity, such as contrast sensitivity, for determining the impact of cataract and the benefits of both first- and second-eye surgeries on driving performance, and that patients' insight into their own driving ability is not necessarily accurate.47,48
Age-related Macular Degeneration
Older drivers with age-related macular degeneration report high levels of driving difficulty and self-regulate their driving habits, including avoiding challenging driving situations (nighttime, unfamiliar areas, rush hour), compared with their age-matched counterparts.35,49,50 However, despite their widely reported driving difficulties, few studies have assessed the driving ability and safety of drivers with age-related macular degeneration, and the findings have been largely inconclusive. Some studies have failed to find a link between age-related macular degeneration and increased motor vehicle collision risk,32 whereas another study found significantly lower motor vehicle collision rates in drivers with intermediate levels of age-related macular degeneration compared with those with normal vision,51 which might have arisen from driver self-regulation.50,52
Simulator studies of small numbers of drivers with age-related macular degeneration demonstrate impairments in some aspects of driving ability, including delayed braking times, slower speeds, and more lane crossings, compared with age-matched controls.53 Other studies of individuals with central field loss, many of whom had age-related macular degeneration, reported impaired pedestrian recognition rates in a driving simulator even when the pedestrians appeared in the seeing field areas,54,55 although vehicle control, including steering and lead car following skills, was not significantly different from controls.56
In our recent on-road study of older drivers with early and intermediate age-related macular degeneration and age-matched controls,18 drivers with age-related macular degeneration were rated as significantly less safe to drive than the control group, with their safety ratings being associated with age-related macular degeneration severity; significant decrements in driving safety were only seen in those drivers with intermediate age-related macular degeneration (Fig. 2). Drivers with age-related macular degeneration also had more critical errors than did the control group, particularly in complex situations involving traffic light–controlled intersections. Specific driving behavior errors exhibited by the drivers with age-related macular degeneration involved poorer observation of the driving environment, problems with lane keeping, and appropriate gap selection. Importantly, of all of the visual function measures included in this study, central motion sensitivity, which involved measurement of the smallest amount of movement detected (Dmin) using a computer-based random-dot kinematogram,40,57 was the only measure significantly associated with driving safety in those with age-related macular degeneration.
Peripheral Vision Loss
Peripheral vision is considered important for the detection of roadside hazards and awareness of other vehicles and pedestrians or cyclists in the periphery. However, the link between visual field defects and driving has challenged researchers for many years, and there has been much debate about the extent of the visual field that is required to facilitate safe driving. Of particular interest are drivers with glaucoma, given that the onset of the disease is slow and patients are typically unaware of the presence of field loss. Also of interest is the driving ability of those with loss of half or one quarter of the visual fields, as in homonymous hemianopia and quadrantanopia, given that many countries across the world do not allow individuals with these conditions to drive.
Older drivers with glaucoma report a number of driving difficulties,58 causing them to avoid challenging driving situations, such as driving at night, in poor weather, during rush hour, or on highways,59 and restrict their driving activities.60 Glaucoma is one of the main reasons that older drivers cease driving,61 particularly in those with moderate/severe loss62; older adults with bilateral glaucoma are almost three times more likely to cease driving than those without glaucoma.63
Drivers with glaucoma have increased self-reported3 and state-recorded motor vehicle collision rates.33,64–67 The extent of glaucomatous field loss has been shown to be important, with studies suggesting that it is only those drivers with more severe glaucomatous field loss who exhibit elevated motor vehicle collision rates.64,68 Our recent study demonstrated that drivers with more severe glaucoma, as assessed using a custom-designed integrated “driving field,” had twice the risk of at-fault motor vehicle collisions.67 The location of visual field loss is also relevant, with some studies highlighting the importance of defects in the lower field for motor vehicle collision risk24,69 and collisions in a simulator,70 whereas other studies highlight the importance of defects in the upper visual field on performance on a computer-based hazard perception test.71 These discordant findings may be due to differences in sample characteristics, as well as the outcome measures used to define driving ability and safety.
Driving simulators have also been used to assess driving ability in individuals with glaucoma but have typically involved relatively small numbers of drivers with a range of visual field loss. Recent studies have shown that drivers with glaucoma exhibit more jerky steering and poorer target detection than do controls, but there were no other between-group differences,72 whereas other studies have reported that patients with advanced glaucoma had significantly more simulator collisions, which were related to reduced integrated visual field sensitivity (involving merging of the two monocular fields),73 particularly in the inferior hemifield.70 Conversely, a study involving a small sample of drivers with binocular glaucomatous field loss reported that some exhibited “safe” driving behaviors through increased visual scanning, leading the authors to conclude that binocular field loss does not necessarily have a negative impact on driving safety, with some drivers being able to compensate for their field loss.74 These findings of potential compensatory strategies in glaucoma are supported, to some extent, by other studies that have explored the role of eye movements on various metrics of driving performance and safety including laboratory-based hazard perception tests,75,76 as well as closed-road measures,77 and remain a topic of ongoing research.78
The specific nature of the driving difficulties of drivers with glaucoma under on-road conditions has also been explored. These studies have highlighted problems with lane keeping, negotiating curves, and anticipatory skills,79 as well as increased numbers of driving instructor interventions.80,81 In our recent on-road study of drivers with mild to moderate glaucoma and those without glaucoma,17 drivers with glaucoma were rated as significantly less safe, made more driving errors, and had almost twice the rate of critical errors compared with those drivers without glaucoma. Driving errors involved lane positioning and planning/approach and were more likely to occur in complex situations including traffic lights and yield/give-way intersections. These results, together with our finding that these drivers self-reported their driving to be relatively good, reinforce the need for evidence-based on-road assessments for evaluating driving fitness and the importance of early detection, not only to minimize progression of disease and its associated visual impairment, but also to ensure safe driving performance.
Individuals with hemianopic or quadrantanopic field defects, regardless of the cause or prognosis, are prohibited from driving in many countries, despite the lack of clear evidence demonstrating that all persons with this condition are unsafe to drive. Studies in this area have typically involved small numbers of participants, with most of them undertaken in driving simulators, given that the strict licensing restrictions for this population preclude on-road driving assessments and evaluation of motor vehicle collisions in countries/states where this population is not legally eligible to drive.
An early simulator study reported that drivers with hemianopic field loss exhibit greater variability in lane position and higher numbers of lane crossings,82 which was supported by a more recent study that also showed that hemianopic drivers adopted a lane position toward their seeing field.83 Detection of pedestrians has also been shown to be impaired in hemianopic participants compared with controls when the pedestrians appear in the blind field84; however, the pedestrians in this task appeared abruptly and remained stationary. A more recent study by the same research group85 used stationary and moving pedestrians and demonstrated that, although detection was low on the blind side for the hemianopes, detection rates were better for moving pedestrians, but reaction times were still longer. Interestingly, simulator studies have shown that some hemianopic participants demonstrate compensatory head movements in the direction of their nonseeing field that enable them to detect pedestrians in their blind field,86 and in another study, the same strategy enabled some hemianopic drivers to detect potential motor vehicle collisions in their blind field in a virtual intersection task.87
Only a limited number of studies have explored the on-road driving performance of those with hemianopic or quadrantanopic field defects. Tant et al.88 reported difficulties in steering stability in a small group of patients with homonymous hemianopia. Although only 14% passed the on-road assessment, it should be noted that the participants were hemianopes referred to the study because their driving was considered unsafe by their carer or by the patients themselves. Conversely, a retrospective review of on-road driving assessments found that some drivers with hemianopic loss and all of those with quadrantanopic loss had the potential for safe driving.89
Drivers with homonymous hemianopic or quadrantanopic field defects and their ability (or not) to maintain safe driving have always fascinated me, and I was lucky enough to have the opportunity to explore this issue during a short sabbatical visit with Cynthia Owsley in Alabama, where individuals with hemianopic and quadrantanopic defects can drive with approval from their treating specialist. In this study, we conducted an on-road assessment of current drivers with hemianopic and quadrantanopic field defects and controls with normal fields. Our study identified steering steadiness and lane keeping as particular problems for some of the hemianopic drivers but highlighted that more than three quarters of the hemianopic drivers had safe driving skills that were indistinguishable from those of the control drivers.19 Importantly, those persons with hemianopic and quadrantanopic defects rated as safe to drive made more head movements into their blind field, in accord with the simulator studies described previously, combined with more stable lane keeping and less sudden braking compared with those hemianopes/quadrantanopes rated as unsafe.21 A recent on-road study also found that a number of drivers with homonymous hemianopia were rated as safe to drive (6/10), with poor lane keeping being cited as the main problem.90 This study also demonstrated that the safe drivers were able to compensate for their field defects by more extensive head and shoulder movements and more eye scans into their blind field, measured using head-mounted eye trackers.
As indicated in this overview and in a recent review regarding the driving ability and safety of people with hemianopic field loss,91 this is a challenging area with a range of different types of studies being undertaken over the last 10 to 15 years. Collectively, the evidence suggests that lane keeping, steering control, and detection of pedestrians are a problem for some drivers with hemianopia; however, on-road studies also clearly suggest that many drivers with hemianopic field loss are able to drive safely, potentially through compensatory head and eye movements. Further research is required to evaluate driving performance in this population using larger sample sizes and importantly to determine whether characteristics, such as compensatory head and eye movements, can be trained in rehabilitation programs aimed at improving driving safety in this population.
Implications for Driver Licensing
From the evidence presented in this review, it is clear that good vision is important for safe driving. However, different types of visual impairment affect driving performance and safety in different ways; thus, licensing decisions must be based on a driver's performance rather than his/her age or disease status. The issue of which tests should be included in licensing remains controversial and is the subject of continuing research.92,93 Although the intent of this review is not to comprehensively discuss which measures are (or should be) included in licensing, the following provides a brief overview of some of the relevant issues.
Visual acuity is the most common vision measure included in licensing, although as described in previous sections of this review, the evidence linking reduced visual acuity and increased crash risk is mixed,94 with many studies reporting no relationship between reduced visual acuity and increased crash risk.2,24,31–34 Nevertheless, drivers of private vehicles are typically required to meet a cutoff value of 20/40 (6/12) with one or both eyes, although there is little evidence to support this cutoff value and there is some variation in this requirement between countries95 and even between states within a country, such as the United States.96 Visual fields are also commonly included in licensing. As outlined in the previous sections, crash risk is higher for those drivers with bilateral severe visual field loss.24,69,97 However, recent studies have also demonstrated the potential for compensation for field loss through head and eye movements,21,90 which makes setting appropriate standards even more challenging. Indeed, visual field requirements for licensure vary between and within countries,98 with regard to both the extent of the visual field and the recommended methods for visual field testing. For example, some U.S. states have no visual field requirements for driving, whereas others enforce stringent requirements involving a maximum of three points missed on a binocular Esterman test within the central 20°.95,99 It has been suggested that merging the two monocular threshold fields to form the integrated visual field may be useful in assessing fitness to drive in patients with a range of field losses,100,101 given that monocular fields are routinely assessed in patients with ocular disease. However, it has been suggested that integrated visual field sensitivity measures do not provide more meaningful information than do better eye measures, with similar relationships reported for various aspects of visual disability including self-reported driving difficulties.102 Definition of the minimum visual field requirements for safe driving and optimal testing strategies remains unresolved and should be the topic of future research.
A number of studies have explored whether other visual measures might better predict driving performance and safety, including contrast sensitivity and motion sensitivity. Contrast sensitivity may provide a better measure of central vision for driving than visual acuity, given that the driving environment contains a wide range of spatial frequencies and contrasts. Indeed, there is some evidence linking contrast sensitivity and crash rates in general populations,31 with contrast sensitivity being associated with retrospective103 but not prospective crashes.2,24,33 Reduced contrast sensitivity has, however, been shown to predict crash risk in selected populations, such as drivers with cataracts.38 Given the dynamic nature of the driving environment, it has been suggested that motion perception also has a role in predicting driving ability and safety. Although the relationship between motion sensitivity and crash risk has not been explored, motion perception has been linked to self-reported driving attention problems,104,105 as well as driving performance assessed on both closed40 and open roads,18,106 and warrants further investigation in future studies.
It is also important to recognize that driving is a complex task that includes cognitive and motor function in addition to vision, which also have an important role in predicting driving safety.107 The role of cognitive impairment in driving safety has been widely explored, with measures of executive function, such as the Trail Making Test, shown to have a strong relationship with measures of driving ability and safety.108,109 A large body of research has also evaluated the ability of visual processing speeds and divided attention to predict driving safety using the computer-based useful field of view.110,111 Reduced useful field-of-view performance strongly predicts both retrospective103,112 and prospective crashes in general populations of older adults,2,24,113 as well as in those with ocular disease,66 and has been shown to be a strong predictor of near-miss crashes in naturalistic studies.114 The useful field of view is also effective at predicting prospective crash risk when administered in a driver licensing setting,109 providing further support for its inclusion for screening older drivers.114 Multidisciplinary research from our group has also shown that driving ability and safety can be better predicted when combining tests from multiple domains, including vision, cognition, and motor skills106,115; work in this area is ongoing where these test batteries are being trialed in larger-scale studies of older drivers with a range of impairments.
Another area of driving that has always been of interest to me is driving at night. Nighttime driving is dangerous, as evidenced by fatality rates that are up to three times higher than those in daytime driving when adjusted for driving exposure.116 These effects are even more pronounced for fatal crashes involving pedestrians, where nighttime pedestrian fatality rates are up to seven times higher than those in the day117 and of greater severity.118 Analyses of crash databases demonstrate that reduced lighting and poor visibility are the primary factors associated with these high crash rates rather than other factors that vary between daytime and nighttime driving, such as driver fatigue and alcohol consumption.119,120
The visual challenges of nighttime driving are, however, poorly understood and have received limited research investigation. The sabbatical visits of Fred Owens, Rick Tyrrell, and Alex Chaparro to Queensland University of Technology in the 1990s onward provided the stimulus to use the closed-road circuit, which contains a realistic network of rural roads, to explore a range of aspects of nighttime driving. Importantly, closed roads provide an ideal opportunity to accurately capture the lighting conditions of nighttime roads, which is challenging in simulators, and also provide the key advantage that researchers can control conditions in ways that are not possible on public roads. In our studies, we were able to study drivers' ability to recognize pedestrians,121 road workers,122 and bicyclists.123 We implemented simulated construction sites,124 low- and high-clutter environments,125 and situations in which dark foam obstacles were positioned on the road surface in the vehicle's lane of travel.15 For reasons of standardization and safety, there are no other vehicles on the road and therefore no oncoming headlights. This was overcome to some extent through the use of headlamps linked to sensors, where drivers passing through the sensors initiate the onset of the headlamp beam to simulate the effects of oncoming vehicles, or simulated headlamps were mounted on the car bonnet and initiated at set intervals to simulate oncoming vehicles.126
Night Vision and Driving
Driving at nighttime is one of the most challenging driving situations for most drivers. Approximately one in three older adults report vision-related night-driving difficulties, with 20 to 50% restricting or ceasing night driving.127,128 Indeed, our studies have revealed that the most common visual problems that older drivers report for night driving include glare from oncoming headlights, haloes, and starbursts and also difficulty seeing lane markings.129
A particular focus of our research has been to explore methods to increase the nighttime visibility of vulnerable road users, given that reduced visibility has been shown to be a key contributory factor to their high fatality rates at night. One approach for increasing the conspicuity of pedestrians at nighttime is the use of clothing that incorporates retroreflective materials, which are typically positioned on the torso in the form of a retroreflective vest. However, alternative placements of retroreflective materials have been shown to be more effective for increasing pedestrian conspicuity. One such configuration is “biomotion,” where retroreflective materials positioned on the major movable joints create the sense of “biological motion.” The sensitivity of the visual system to biological motion was first discovered by Johansson,130 who demonstrated that motion information produced from lights positioned on the major joints (ankles, knees, waist, shoulders, elbows, wrists) when viewed in darkness enabled observers to recognize that the wearers were humans engaged in a range of natural activities. Subsequent studies demonstrated that, based only on the motion information available in these point-light displays, observers can quickly recognize an actor's sex and emotions as well as the identity of their friends and the weight of unseen objects lifted by the wearer (see Blake and Shiffrar131 for a review of the literature). Early studies including video-based demonstrations highlighted the application of biomotion to enhance drivers' ability to recognize pedestrians from a safe distance at night, where retroreflective strips positioned on the moveable joints create a sense of biological motion when illuminated by the headlights of oncoming vehicles.132,133
Building on this early work, we conducted a series of studies that involved pedestrians (experimenters) walking in place at the roadside under nighttime conditions, while participants drove a vehicle around the circuit and pressed a touchpad when they first recognized the presence of a pedestrian. In one study, pedestrians wore black clothing, white clothing, black clothing with a retroreflective vest, or black clothing with retroreflective strips on the moveable joints, in the biomotion configuration.121 Although the total surface area of the retroreflective markings was equal in the vest and biomotion conditions, their conspicuity was very different: drivers using low-beam headlamps responded to the biomotion configuration from a distance that was 3.4 times greater than the distance at which they responded to the vest configuration and 26.5 times greater than the black condition (see Supplementary Video demonstration of the benefits of biomotion clothing, available at http://links.lww.com/OPX/A410). In another experiment, we explored whether participants appreciated the differences in conspicuity conferred by these different clothing conditions when walking in place at the side of the same road circuit at night. The results demonstrated that pedestrians greatly overestimated their own conspicuity, failed to appreciate the extent to which different clothing configurations affected their conspicuity, and greatly underestimated the benefits of biomotion clothing.134 More recently, we have demonstrated that biomotion clothing enables recognition of the walking direction of pedestrians, which is clearly important when judging whether a pedestrian is likely to be entering or exiting the roadway.135 The recording of drivers' eye movements in this and other studies (Fig. 3) demonstrates that the benefits of biomotion clothing arise because oncoming drivers fixate pedestrians wearing biomotion clothing sooner and recognize that the bright light points resulting from the retroreflective strips are a human form more rapidly than when a pedestrian wears a retroreflective vest.135
Our nighttime studies have also explored how the characteristics of the driver affect performance and their ability to recognize other road users. We demonstrated the important role of driver age, where older drivers respond to the presence of pedestrians from much shorter distances and are more likely to fail to detect the pedestrian compared with younger drivers.121,122,136,137 In one study, we demonstrated that the mean distance at which older drivers first detected a pedestrian was only 58% of that of younger drivers.121 In support of these findings, another study showed that older drivers responded to the presence of a road worker at a mean distance that was less than half that of younger drivers; interestingly, motion sensitivity was the best predictor of these differences in pedestrian recognition distances.136
The visual status of the driver is also a key factor affecting driving visibility at night, and nighttime driving is one of the first aspects of driving that those with visual impairments including cataracts, age-related macular degeneration, and glaucoma restrict or cease altogether.35,50,59 We have explored two common causes of reversible visual impairment, cataracts and uncorrected refractive error, in a series of studies that used a repeated-measures design to compare driving with simulated visual impairments mounted in wide-field goggles compared with when drivers wore their optimal correction.
Investigation of the impact of optical blur is important because large numbers of individuals drive with uncorrected refractive errors, with one study reporting that uncorrected refractive errors accounted for 80% of drivers who failed to meet the legal vision limits for driving.34 Simulator studies from other research groups and our own closed-road driving studies indicate that, although steering accuracy and lane keeping are relatively robust to even high levels of blur,8,138,139 recognition of nighttime road signs and pedestrians is negatively affected by blur8,9,14 and presbyopic contact lens corrections.140 Importantly, we have shown that the effects of blur are greater at night compared with during the day8 and that even low levels of refractive blur reduce drivers' recognition of pedestrians at night,9 reinforcing the importance of accurate correction of refractive error for nighttime driving.
In a series of closed-road studies, we explored the effects of simulated cataracts on nighttime driving and found reductions in the ability to recognize road signs and road hazards and pedestrians at night, despite slower driving speeds.14,15 The detrimental impact of cataracts was shown to be greater than that for optical blur even when visual acuity was reduced by the same amount; this is likely to have resulted from the additional reductions in contrast sensitivity and increased light scatter associated with the simulated cataracts.14
The characteristics of the road environment are also important in terms of driving ability and safety at night. Two key factors were explored including glare from oncoming headlamps, which is a common complaint of all drivers, particularly older drivers,129 and the complexity (clutter) of the driving situation.35
Oncoming headlamp glare can impair drivers' ability to respond to pedestrians at night. Closed- and open-road studies demonstrate that glare reduces the likelihood that drivers can recognize road hazards as well as real14,121,126,141 and simulated pedestrians at night,142 and these effects are exacerbated in the presence of visual impairments including simulated cataracts and optical blur.14 In our recent closed-road study, we also demonstrated that the detrimental effects of glare on nighttime driving ability are predicted by mesopic measures of visual function.126 Interestingly, other researchers have reported that drivers underestimate the detrimental impact of glare on visual function and on their own ability to recognize pedestrians at night.141,143
Exploration of the effects of the complexity of the driving environment at night is important, particularly given that it has been suggested that the advantages of biomotion clothing are reduced in visually cluttered driving environments.144 In closed-road studies, we demonstrated the strong advantage of retroreflective markings in the biomotion configuration regardless of whether visual clutter was present.125 In follow-up studies conducted in both closed- and open-road settings, we have also demonstrated that workers in cluttered road construction sites are more conspicuous when they wear biomotion markings in addition to a retroreflective vest.122,124
Importantly, citing evidence from our nighttime research, the Australia/New Zealand Standards “Committee SF-004-03” introduced the biomotion configuration into AS/NZS 4602.1 2011 (high-visibility safety garments). The Queensland Department of Transport and Main Roads was an early adopter along with Vic Rail (Victorian state railway authority) with others following, as high-visibility clothing providers produced work clothing incorporating the biomotion configuration. There has also been significant interest from the mining industry for their workers. The International Standard for High Visibility Clothing ISO 201471:2013 also refers to the “biomotion effect.” Collectively, this evidence highlights the very positive road safety benefits of our research over many years (also extending to train drivers and potentially mine workers) and something I am particularly proud of.
In summing up this review, I would like to emphasize that understanding the relationship between vision and driving is critical for drivers as well as pedestrians and other vulnerable road users, and this is the case for day and nighttime driving conditions. Good vision is important for safe driving, and different types of visual impairment can affect driving performance and safety in different ways. It is critical that licensing decisions are based on performance rather than the age or the disease status of the driver, and this includes measures of visual performance, along with cognitive and motor ability, given that they all have a role in driving ability and safety.
There is evidence that interventions such as optimal refractive correction and cataract extraction have the potential to improve driving safety, with even small amounts of blur being important for nighttime driving. For road users, including recreational and occupational pedestrians and cyclists who wish to make themselves visible to oncoming drivers at night, clothing matters: retroreflective materials should be worn on the moveable joints in a biomotion configuration rather than on the torso. Finally, optometrists have a key role in driving safety and have the opportunity to save the lives and not only the eyes of their patients!
1. Owsley C, Wood JM, McGwin G Jr. A Roadmap for Interpreting the Literature on Vision and Driving. Surv Ophthalmol 2015;60:250–62.
2. Cross JM, McGwin G Jr., Rubin GS, et al. Visual and Medical Risk Factors for Motor Vehicle Collision Involvement among Older Drivers. Br J Ophthalmol 2009;93:400–4.
3. Tanabe S, Yuki K, Ozeki N, et al. The Association between Primary Open-angle Glaucoma and Motor Vehicle Collisions. Invest Ophthalmol Vis Sci 2011;52:4177–81.
4. Wood J, Chaparro A. Night Driving: How Low Illumination Affects Driving and the Challenges of Simulation. In: Fisher DL, Rizzo M, Caird JK, Lee JD, eds. Handbook of Driving Simulation for Engineering, Medicine and Psychology. Boca Raton, FL: CRC Press/Taylor & Francis; 2011:chapter 28.
5. Brooks JO, Goodenough RR, Crisler MC, et al. Simulator Sickness during Driving Simulation Studies. Accid Anal Prev 2010;42:788–96.
6. Classen S, Bewernitz M, Shechtman O. Driving Simulator Sickness: An Evidence-based Review of the Literature. Am J Occup Ther 2011;65:179–88.
7. Higgins KE, Wood J, Tait A. Vision and Driving: Selective Effect of Optical Blur on Different Driving Tasks. Hum Factors 1998;40:224–32.
8. Wood JM, Collins MJ, Chaparro A, et al. Differential Effects of Refractive Blur on Day and Nighttime Driving Performance. Invest Ophthalmol Vis Sci 2014;55:2284–9.
9. Wood JM, Marszalek R, Carberry T, et al. Effects of Different Levels of Refractive Blur on Nighttime Pedestrian Visibility. Invest Ophthalmol Vis Sci 2015;56:4480–5.
10. Wood JM, Troutbeck R. Effect of Restriction of the Binocular Visual Field on Driving Performance. Ophthalmic Physiol Opt 1992;12:291–8.
11. Wood JM, Dique T, Troutbeck R. The Effect of Artificial Visual Impairment on Functional Fields and Driving Performance. Clin Vis Sci 1993;8:563–75.
12. Wood JM, Troutbeck R. Effect of Visual Impairment on Driving. Hum Factors 1994;36:476–87.
13. Wood JM, Troutbeck R. Elderly Drivers and Simulated Visual Impairment. Optom Vis Sci 1995;72:115–24.
14. Wood JM, Tyrrell RA, Chaparro A, et al. Even Moderate Visual Impairments Degrade Drivers' Ability to See Pedestrians at Night. Invest Ophthalmol Vis Sci 2012;53:2586–92.
15. Wood J, Chaparro A, Carberry T, et al. Effect of Simulated Visual Impairment on Nighttime Driving Performance. Optom Vis Sci 2010;87:379–86.
16. Wood J, Chaparro A, Hickson L. Interaction between Visual Status, Driver Age and Distracters on Daytime Driving Performance. Vision Res 2009;49:2225–31.
17. Wood JM, Black AA, Mallon K, et al. Glaucoma and Driving: On-road Driving Characteristics. PLoS One 2016;11:e0158318.
18. Wood JM, Black AA, Mallon K, et al. Effects of Age-related Macular Degeneration on Driving Performance. Invest Ophthalmol Vis Sci 2018;59:273–9.
19. Wood JM, McGwin G Jr., Elgin J, et al. On-road Driving Performance by Persons with Hemianopia and Quadrantanopia. Invest Ophthalmol Vis Sci 2009;50:577–85.
20. Wood JM, McGwin G Jr., Elgin J, et al. Characteristics of On-road Driving Performance of Persons with Central Vision Loss Who Use Bioptic Telescopes. Invest Ophthalmol Vis Sci 2013;54:3790–7.
21. Wood JM, McGwin G Jr., Elgin J, et al. Hemianopic and Quadrantanopic Field Loss, Eye and Head Movements, and Driving. Invest Ophthalmol Vis Sci 2011;52:1220–5.
22. Keay L, Munoz B, Turano KA, et al. Visual and Cognitive Deficits Predict Stopping or Restricting Driving: The Salisbury Eye Evaluation Driving Study (SEEDS). Invest Ophthalmol Vis Sci 2009;50:107–13.
23. Owsley C, McGwin G Jr., Antin JF, et al. The Alabama VIP Older Driver Study Rationale and Design: Examining the Relationship between Vision Impairment and Driving Using Naturalistic Driving Techniques. BMC Ophthalmol 2018;18:32.
24. Rubin GS, Ng ES, Bandeen-Roche K, et al. A Prospective, Population-based Study of the Role of Visual Impairment in Motor Vehicle Crashes among Older Drivers: The SEE Study. Invest Ophthalmol Vis Sci 2007;48:1483–91.
25. Burg A. The Relationship between Vision Test Scores and Driving Record: General Findings. Los Angeles, CA: UCLA Institute of Transportation and Traffic Engineering; 1967.
26. Hofstetter HW. Visual Acuity and Highway Accidents. J Am Optom Assoc 1976;47:887–93.
27. Davison PA. Inter-relationships between British Drivers' Visual Abilities, Age and Road Accident Histories. Ophthalmic Physiol Opt 1985;5:195–204.
28. Gresset J, Meyer F. Risk of Accidents among Elderly Car Drivers with Visual Acuity Equal to 6/12 or 6/15 and Lack of Binocular Vision. Ophthalmic Physiol Opt 1994;14:33–7.
29. Marottoli RA, Richardson ED, Stowe MH, et al. Development of a Test Battery to Identify Older Drivers at Risk for Self-reported Adverse Driving Events. J Am Geriatr Soc 1998;46:562–8.
30. Ivers RQ, Mitchell P, Cumming RG. Sensory Impairment and Driving: The Blue Mountains Eye Study. Am J Public Health 1999;89:85–7.
31. Decina LE, Staplin L. Retrospective Evaluation of Alternative Vision Screening Criteria for Older and Younger Drivers. Accid Anal Prev 1993;25:267–75.
32. McCloskey LW, Koepsell TD, Wolf ME, et al. Motor Vehicle Collision Injuries and Sensory Impairments of Older Drivers. Age Ageing 1994;23:267–73.
33. Owsley C, McGwin G Jr., Ball K. Vision Impairment, Eye Disease, and Injurious Motor Vehicle Crashes in the Elderly. Ophthalmic Epidemiol 1998;5:101–13.
34. Keeffe JE, Jin CF, Weih LM, et al. Vision Impairment and Older Drivers: Who's Driving? Br J Ophthalmol 2002;86:1118–21.
35. Ball K, Owsley C, Stalvey B, et al. Driving Avoidance and Functional Impairment in Older Drivers. Accid Anal Prev 1998;30:313–22.
36. Owsley C, Stalvey B, Wells J, et al. Older Drivers and Cataract: Driving Habits and Crash Risk. J Gerontol A Biol Sci Med Sci 1999;54:M203–11.
37. Fraser ML, Meuleners LB, Ng JQ, et al. Driver Self-regulation and Depressive Symptoms in Cataract Patients Awaiting Surgery: A Cross-sectional Study. BMC Ophthalmol 2013;13:45.
38. Owsley C, Stalvey BT, Wells J, et al. Visual Risk Factors for Crash Involvement in Older Drivers with Cataract. Arch Ophthalmol 2001;119:881–7.
39. Wood JM, Mallon K. Comparison of Driving Performance of Young and Old Drivers (with and without Visual Impairment) Measured During In-traffic Conditions. Optom Vis Sci 2001;78:343–9.
40. Wood JM. Age and Visual Impairment Decrease Driving Performance as Measured on a Closed-road Circuit. Hum Factors 2002;44:482–94.
41. Wood JM, Carberry TP. Older Drivers and Cataracts: Measures of Driving Performance Before and After Cataract Surgery. Transport Res Rec 2004;1865:7–13.
42. Owsley C, McGwin G Jr., Sloane M, et al. Impact of Cataract Surgery on Motor Vehicle Crash Involvement by Older Adults. JAMA 2002;288:841–9.
43. Agramunt S, Meuleners LB, Fraser ML, et al. First and Second Eye Cataract Surgery and Driver Self-regulation among Older Drivers with Bilateral Cataract: A Prospective Cohort Study. BMC Geriatr 2018;18:51.
44. Meuleners LB, Brameld K, Fraser ML, et al. The Impact of First- and Second-eye Cataract Surgery on Motor Vehicle Crashes and Associated Costs. Age Ageing 2019;48:128–33.
45. Mennemeyer ST, Owsley C, McGwin G Jr. Reducing Older Driver Motor Vehicle Collisions via Earlier Cataract Surgery. Accid Anal Prev 2013;61:203–11.
46. Wood JM, Carberry TP. Bilateral Cataract Surgery and Driving Performance. Br J Ophthalmol 2006;90:1277–80.
47. Wong IY, Smith SS, Sullivan KA. The Relationship between Cognitive Ability, Insight and Self-regulatory Behaviors: Findings from the Older Driver Population. Accid Anal Prev 2012;49:316–21.
48. Wood JM, Lacherez PF, Anstey KJ. Not All Older Adults Have Insight into Their Driving Abilities: Evidence from an On-road Assessment and Implications for Policy. J Gerontol A Biol Sci Med Sci 2013;68:559–66.
49. Weaver Moore L, Miller M. Driving Strategies Used by Older Adults with Macular Degeneration: Assessing the Risks. Appl Nurs Res 2005;18:110–6.
50. Sengupta S, van Landingham SW, Solomon SD, et al. Driving Habits in Older Patients with Central Vision Loss. Ophthalmology 2014;121:727–32.
51. McGwin G Jr., Mitchell B, Searcey K, et al. Examining the Association between Age-related Macular Degeneration and Motor Vehicle Collision Involvement: A Retrospective Cohort Study. Br J Ophthalmol 2013;97:1173–6.
52. DeCarlo DK, Scilley K, Wells J, et al. Driving Habits and Health-related Quality of Life in Patients with Age-related Maculopathy. Optom Vis Sci 2003;80:207–13.
53. Szlyk JP, Pizzimenti CE, Fishman GA, et al. A Comparison of Driving in Older Subjects with and without Age-related Macular Degeneration. Arch Ophthalmol 1995;113:1033–40.
54. Bronstad PM, Bowers AR, Albu A, et al. Driving with Central Field Loss I: Effect of Central Scotomas on Responses to Hazards. JAMA Ophthalmol 2013;131:303–9.
55. Bronstad PM, Albu A, Bowers AR, et al. Driving with Central Visual Field Loss II: How Scotomas Above or Below the Preferred Retinal Locus (PRL) Affect Hazard Detection in a Driving Simulator. PLoS One 2015;10:e0136517.
56. Bronstad PM, Albu A, Goldstein R, et al. Driving with Central Field Loss III: Vehicle Control. Clin Exp Optom 2016;99:435–40.
57. Bullimore MA, Wood JM, Swenson K. Motion Perception in Glaucoma. Invest Ophthalmol Vis Sci 1993;34:3526–33.
58. Janz NK, Musch DC, Gillespie BW, et al. Collaborative Initial Glaucoma Treatment Study (CIGTS) Investigators. Evaluating Clinical Change and Visual Function Concerns in Drivers and Nondrivers with Glaucoma. Invest Ophthalmol Vis Sci 2009;50:1718–25.
59. McGwin G Jr., Mays A, Joiner W, et al. Is Glaucoma Associated with Motor Vehicle Collision Involvement and Driving Avoidance? Invest Ophthalmol Vis Sci 2004;45:3934–9.
60. Bechetoille A, Arnould B, Bron A, et al. Measurement of Health-related Quality of Life with Glaucoma: Validation of the Glau-QOL 36-item Questionnaire. Acta Ophthalmol 2008;86:71–80.
61. Hakamies-Blomqvist L, Wahlstrom B. Why Do Older Drivers Give up Driving? Accid Anal Prev 1998;30:305–12.
62. Tam ALC, Trope GE, Buys YM, et al. Self-perceived Impact of Glaucomatous Visual Field Loss and Visual Disabilities on Driving Difficulty and Cessation. J Glaucoma 2018;27:981–6.
63. Ramulu PY, West SK, Munoz B, et al. Driving Cessation and Driving Limitation in Glaucoma: The Salisbury Eye Evaluation Project. Ophthalmology 2009;116:1846–53.
64. McGwin G Jr., Xie A, Mays A, et al. Visual Field Defects and the Risk of Motor Vehicle Collisions among Patients with Glaucoma. Invest Ophthalmol Vis Sci 2005;46:4437–41.
65. Hu PS, Trumble DA, Foley DJ, et al. Crash Risks of Older Drivers: A Panel Data Analysis. Accid Anal Prev 1998;30:569–81.
66. Haymes SA, Leblanc RP, Nicolela MT, et al. Risk of Falls and Motor Vehicle Collisions in Glaucoma. Invest Ophthalmol Vis Sci 2007;48:1149–55.
67. Kwon M, Huisingh C, Rhodes LA, et al. Association between Glaucoma and At-fault Motor Vehicle Collision Involvement among Older Drivers: A Population-based Study. Ophthalmology 2016;123:109–16.
68. McGwin G Jr., Huisingh C, Jain SG, et al. Binocular Visual Field Impairment in Glaucoma and At-fault Motor Vehicle Collisions. J Glaucoma 2015;24:138–43.
69. Huisingh C, McGwin G Jr., Wood J, et al. The Driving Visual Field and a History of Motor Vehicle Collision Involvement in Older Drivers: A Population-based Examination. Invest Ophthalmol Vis Sci 2015;56:132–8.
70. Kunimatsu-Sanuki S, Iwase A, Araie M, et al. The Role of Specific Visual Subfields in Collisions with Oncoming Cars During Simulated Driving in Patients with Advanced Glaucoma. Br J Ophthalmol 2017;101:896–901.
71. Glen FC, Smith ND, Crabb DP. Impact of Superior and Inferior Visual Field Loss on Hazard Detection in a Computer-based Driving Test. Br J Ophthalmol 2015;99:613–7.
72. Prado Vega R, van Leeuwen PM, Rendon Velez E, et al. Obstacle Avoidance, Visual Detection Performance, and Eye-scanning Behavior of Glaucoma Patients in a Driving Simulator: A Preliminary Study. PLoS One 2013;8:e77294.
73. Kunimatsu-Sanuki S, Iwase A, Araie M, et al. An Assessment of Driving Fitness in Patients with Visual Impairment to Understand the Elevated Risk of Motor Vehicle Accidents. BMJ Open 2015;5:e006379.
74. Kubler TC, Kasneci E, Rosenstiel W, et al. Driving with Glaucoma: Task Performance and Gaze Movements. Optom Vis Sci 2015;92:1037–46.
75. Crabb DP, Smith ND, Rauscher FG, et al. Exploring Eye Movements in Patients with Glaucoma When Viewing a Driving Scene. PLoS One 2010;5:e9710.
76. Lee SS, Black AA, Wood JM. Effect of Glaucoma on Eye Movement Patterns and Laboratory-based Hazard Detection Ability. PLoS One 2017;12:e0178876.
77. Lee SS, Black AA, Wood JM. Scanning Behavior and Daytime Driving Performance of Older Adults with Glaucoma. J Glaucoma 2018;27:558–65.
78. Kasneci E, Black AA, Wood JM. Eye-tracking as a Tool to Evaluate Functional Ability in Everyday Tasks in Glaucoma. J Ophthalmol 2017;2017:6425913.
79. Bowers A, Peli E, Elgin J, et al. On-road Driving with Moderate Visual Field Loss. Optom Vis Sci 2005;82:657–67.
80. Haymes SA, LeBlanc RP, Nicolela MT, et al. Glaucoma and On-road Driving Performance. Invest Ophthalmol Vis Sci 2008;49:3035–41.
81. Bhorade AM, Yom VH, Barco P, et al. On-road Driving Performance of Patients with Bilateral Moderate and Advanced Glaucoma. Am J Ophthalmol 2016;166:43–51.
82. Szlyk JP, Brigell M, Seiple W. Effects of Age and Hemianopic Visual Field Loss on Driving. Optom Vis Sci 1993;70:1031–7.
83. Bowers AR, Mandel AJ, Goldstein RB, et al. Driving with Hemianopia, II: Lane Position and Steering in a Driving Simulator. Invest Ophthalmol Vis Sci 2010;51:6605–13.
84. Bowers AR, Mandel AJ, Goldstein RB, et al. Driving with Hemianopia, I: Detection Performance in a Driving Simulator. Invest Ophthalmol Vis Sci 2009;50:5137–47.
85. Alberti CF, Peli E, Bowers AR. Driving with Hemianopia: III. Detection of Stationary and Approaching Pedestrians in a Simulator. Invest Ophthalmol Vis Sci 2014;55:368–74.
86. Bowers AR, Ananyev E, Mandel AJ, et al. Driving with Hemianopia: IV. Head Scanning and Detection at Intersections in a Simulator. Invest Ophthalmol Vis Sci 2014;55:1540–8.
87. Papageorgiou E, Hardiess G, Wietholter H, et al. The Neural Correlates of Impaired Collision Avoidance in Hemianopic Patients. Acta Ophthalmol 2012;90:e198–205.
88. Tant MLM, Brouwer WH, Cornelissen FW, et al. Driving and Visuospatial Performance in People with Hemianopia. Neuropsychol Rehabil 2002;12:419–37.
89. Racette L, Casson EJ. The Impact of Visual Field Loss on Driving Performance: Evidence from On-road Driving Assessments. Optom Vis Sci 2005;82:668–74.
90. Kasneci E, Sippel K, Aehling K, et al. Driving with Binocular Visual Field Loss? A Study on a Supervised On-road Parcours with Simultaneous Eye and Head Tracking. PLoS One 2014;9:e87470.
91. Bowers AR. Driving with Homonymous Visual Field Loss: A Review of the Literature. Clin Exp Optom 2016;99:402–18.
92. Owsley C. The Vision and Driving Challenge. J Neuroophthalmol 2010;30:115–6.
93. Desapriya E, Harjee R, Brubacher J, et al. Vision Screening of Older Drivers for Preventing Road Traffic Injuries and Fatalities. Cochrane Database Syst Rev 2014;CD006252.
94. Charman WN. Vision and Driving—A Literature Review and Commentary. Ophthalmic Physiol Opt 1997;17:371–91.
95. Bro T, Lindblom B. Strain out a Gnat and Swallow a Camel?—Vision and Driving in the Nordic Countries. Acta Ophthalmol 2018;96:623–30.
96. Owsley C, McGwin G Jr. Vision and Driving. Vision Res 2010;50:2348–61.
97. Johnson CA, Keltner JL. Incidence of Visual Field Loss in 20,000 Eyes and Its Relationship to Driving Performance. Arch Ophthalmol 1983;101:371–5.
98. Johnson CA, Wilkinson ME. Vision and Driving: The United States. J Neuroophthalmol 2010;30:170–6.
99. Bron AM, Viswanathan AC, Thelen U, et al. International Vision Requirements for Driver Licensing and Disability Pensions: Using a Milestone Approach in Characterization of Progressive Eye Disease. Clin Ophthalmol 2010;4:1361–9.
100. Crabb DP, Fitzke FW, Hitchings RA, et al. A Practical Approach to Measuring the Visual Field Component of Fitness to Drive. Br J Ophthalmol 2004;88:1191–6.
101. Chisholm CM, Rauscher FG, Crabb DC, et al. Assessing Visual Fields for Driving in Patients with Paracentral Scotomata. Br J Ophthalmol 2008;92:225–30.
102. Arora KS, Boland MV, Friedman DS, et al. The Relationship between Better-eye and Integrated Visual Field Mean Deviation and Visual Disability. Ophthalmology 2013;120:2476–84.
103. Ball K, Owsley C, Sloane ME, et al. Visual Attention Problems as a Predictor of Vehicle Crashes in Older Drivers. Invest Ophthalmol Vis Sci 1993;34:3110–23.
104. Raghuram A, Lakshminarayanan V. Motion Perception Tasks as Potential Correlates to Driving Difficulty in the Elderly. J Mod Opt 2006;53:1343–62.
105. Henderson S, Donderi DC. Peripheral Motion Contrast Sensitivity and Older Drivers' Detection Failure Accident Risk. Proceedings of the Third International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, June 27–30, 2005, Rockport, Maine, Iowa City, IA: Public Policy Center, University of Iowa, 2005:41–50.
106. Wood JM, Anstey KJ, Kerr GK, et al. A Multidomain Approach for Predicting Older Driver Safety under In-traffic Road Conditions. J Am Geriatr Soc 2008;56:986–93.
107. Anstey KJ, Wood J, Lord S, et al. Cognitive, Sensory and Physical Factors Enabling Driving Safety in Older Adults. Clin Psychol Rev 2005;25:45–65.
108. Staplin L, Gish KW, Sifrit KJ. Using Cognitive Status to Predict Crash Risk: Blazing New Trails? J Safety Res 2014;48:19–25.
109. Ball KK, Roenker DL, Wadley VG, et al. Can High-risk Older Drivers Be Identified through Performance-based Measures in a Department of Motor Vehicles Setting? J Am Geriatr Soc 2006;54:77–84.
110. Ball KK, Beard BL, Roenker DL, et al. Age and Visual Search: Expanding the Useful Field of View. J Am Optom Assoc 1988;5:2210–9.
111. Ball K, Owsley C. The Useful Field of View Test: A New Technique for Evaluating Age-related Declines in Visual Function. J Am Optom Assoc 1993;63:71–9.
112. Owsley C, Ball K, Sloane ME, et al. Visual/Cognitive Correlates of Vehicle Accidents in Older Drivers. Psychol Aging 1991;6:403–15.
113. Owsley C, Ball K, McGwin G Jr., et al. Visual Processing Impairment and Risk of Motor Vehicle Crash among Older Adults. JAMA 1998;279:1083–8.
114. Huisingh C, Levitan EB, Irvin MR, et al. Visual Sensory and Visual-cognitive Function and Rate of Crash and Near-crash Involvement among Older Drivers Using Naturalistic Driving Data. Invest Ophthalmol Vis Sci 2017;58:2959–67.
115. Wood JM, Horswill MS, Lacherez PF, et al. Evaluation of Screening Tests for Predicting Older Driver Performance and Safety Assessed by an On-road Test. Accid Anal Prev 2013;50:1161–8.
116. US National Highway Traffic Safety Administration (NHTSA). Passenger Vehicle Occupant Fatalities by Day and Night—A Contrast: DOT HS 810 637. Washington, DC: NHTSA's National Center for Statistics and Analysis; 2007. Available at: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/810637
. Accessed June 28, 2019.
117. Sullivan JM, Flannagan MJ. Determining the Potential Safety Benefit of Improved Lighting in Three Pedestrian Crash Scenarios. Accid Anal Prev 2007;39:638–47.
118. Mohamed MG, Saunier N, Miranda-Moreno LF, et al. A Clustering Regression Approach: A Comprehensive Injury Severity Analysis of Pedestrian-vehicle Crashes in New York, US and Montreal, Canada. Safety Sci 2013;54:27–37.
119. Owens DA, Sivak M. Differentiation of Visibility and Alcohol as Contributors to Twilight Road Fatalities. Hum Factors 1996;38:680–9.
120. Sullivan JM, Flannagan MJ. The Role of Ambient Light Level in Fatal Crashes: Inferences from Daylight Saving Time Transitions. Accid Anal Prev 2002;34:487–98.
121. Wood JM, Tyrrell RA, Carberry TP. Limitations in Drivers' Ability to Recognize Pedestrians at Night. Hum Factors 2005;47:644–53.
122. Wood JM, Marszalek R, Lacherez P, et al. Configuring Retroreflective Markings to Enhance the Night-time Conspicuity of Road Workers. Accid Anal Prev 2014;70:209–14.
123. Wood JM, Tyrrell RA, Marszalek R, et al. Using Reflective Clothing to Enhance the Conspicuity of Bicyclists at Night. Accid Anal Prev 2012;45:726–30.
124. Wood JM, Tyrrell RA, Marszalek R, et al. Using Biological Motion to Enhance the Conspicuity of Roadway Workers. Accid Anal Prev 2011;43:1036–41.
125. Tyrrell RA, Wood JM, Chaparro A, et al. Seeing Pedestrians at Night: Visual Clutter Does Not Mask Biological Motion. Accid Anal Prev 2009;41:506–12.
126. Kimlin JA, Black AA, Wood JM. Nighttime Driving in Older Adults: Effects of Glare and Association with Mesopic Visual Function. Invest Ophthalmol Vis Sci 2017;58:2796–803.
127. Lyman JM, McGwin G Jr., Sims RV. Factors Related to Driving Difficulty and Habits in Older Drivers. Accid Anal Prev 2001;33:413–21.
128. Naumann RB, Dellinger AM, Kresnow MJ. Driving Self-restriction in High-risk Conditions: How Do Older Drivers Compare to Others? J Safety Res 2011;42:67–71.
129. Kimlin JA, Black AA, Djaja N, et al. Development and Validation of a Vision and Night Driving Questionnaire. Ophthalmic Physiol Opt 2016;36:465–76.
130. Johansson G. Visual Perception of Biological Motion and a Model for Its Analysis. Percept Psychophys 1973;14:201–11.
131. Blake R, Shiffrar M. Perception of Human Motion. Ann Rev Psychol 2007;58:47–73.
132. Blomberg RD, Hale A, Preusser DF. Experimental Evaluation of Alternative Conspicuity-enhancement Techniques for Pedestrians and Bicyclists. J Safety Res 1986;17:1–12.
133. Owens DA, Antonoff RJ, Francis EL. Biological Motion and Nighttime Pedestrian Conspicuity. Hum Factors 1994;36:718–32.
134. Tyrrell RA, Wood JM, Carberry TP. On-road Measures of Pedestrians' Estimates of Their Own Nighttime Conspicuity. J Safety Res 2004;35:483–90.
135. Wood JM, Tyrrell RA, Lacherez P, et al. Night-time Pedestrian Conspicuity: Effects of Clothing on Drivers' Eye Movements. Ophthalmic Physiol Opt 2017;37:184–90.
136. Wood JM, Lacherez P, Tyrrell RA. Seeing Pedestrians at Night: Effect of Driver Age and Visual Abilities. Ophthalmic Physiol Opt 2014;34:452–8.
137. Owens DA, Wood JM, Owens JM. Effects of Age and Illumination on Night Driving: A Road Test. Hum Factors 2007;49:1115–31.
138. Brooks JO, Tyrrell RA, Frank TA. The Effects of Severe Visual Challenges on Steering Performance in Visually Healthy Young Drivers. Optom Vis Sci 2005;82:689–97.
139. Owens DA, Tyrrell RA. Effects of Luminance, Blur, and Age on Nighttime Visual Guidance: A Test of the Selective Degradation Hypothesis. J Exp Psychol Appl 1999;5:115–28.
140. Chu BS, Wood JM, Collins MJ. The Effect of Presbyopic Vision Corrections on Nighttime Driving Performance. Invest Ophthalmol Vis Sci 2010;51:4861–6.
141. Whetsel Borzendowski SA, Stafford Sewall AA, Rosopa PJ, et al. Drivers' Judgments of the Effect of Headlight Glare on Their Ability to See Pedestrians at Night. J Safety Res 2015;53:31–7.
142. Theeuwes J, Alferdinck JW, Perel M. Relation between Glare and Driving Performance. Hum Factors 2002;44:95–107.
143. Stafford Sewall AA, Whetsel Borzendowski SA, Tyrrell RA. The Accuracy of Drivers' Judgments of the Effects of Headlight Glare on their Own Visual Acuity. Perception 2014;43:1203–13.
144. Moberly N, Langham M. Pedestrian Conspicuity at Night: Failure to Observe a Biological Motion Advantage in a High-clutter Environment. Appl Cognit Psychol 2002;16:477–85.