November 2016
Volume 5, Issue 6
Open Access
Articles  |   December 2016
Glaucoma and Driving Risk under Simulated Fog Conditions
Author Affiliations & Notes
  • Alberto Diniz-Filho
    Visual Performance Laboratory, Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA
    Department of Ophthalmology and Otorhinolaryngology, Federal University of Minas Gerais, Belo Horizonte, Brazil
  • Erwin R. Boer
    Visual Performance Laboratory, Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA
  • Ahmed Elhosseiny
    Visual Performance Laboratory, Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA
  • Zhichao Wu
    Visual Performance Laboratory, Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA
  • Masaki Nakanishi
    Visual Performance Laboratory, Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA
  • Felipe A. Medeiros
    Visual Performance Laboratory, Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA
  • Correspondence: Felipe A. Medeiros, Visual Performance Laboratory, Hamilton Glaucoma Center, Department of Ophthalmology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0946, USA. e-mail: fmedeiros@ucsd.edu 
Translational Vision Science & Technology December 2016, Vol.5, 15. doi:10.1167/tvst.5.6.15
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      Alberto Diniz-Filho, Erwin R. Boer, Ahmed Elhosseiny, Zhichao Wu, Masaki Nakanishi, Felipe A. Medeiros; Glaucoma and Driving Risk under Simulated Fog Conditions. Trans. Vis. Sci. Tech. 2016;5(6):15. doi: 10.1167/tvst.5.6.15.

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      © 2017 Association for Research in Vision and Ophthalmology.

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Abstract

Purpose: We evaluate driving risk under simulated fog conditions in glaucoma and healthy subjects.

Methods: This cross-sectional study included 41 glaucoma patients and 25 age-matched healthy subjects who underwent driving simulation. Tests consisted of curve negotiation without and with fog preview at 30 m of distance and two controlled speeds (slow and fast). Inverse time-to-line crossing (invTLC) was used as metric to quantify risk; higher invTLC values indicating higher risk, as less time is available to avoid drifting out of the road. Piecewise regression models were used to investigate the relationship between differences in invTLC in fog and nonfog conditions and visual field loss.

Results: Glaucoma patients had greater increase in driving risk under fog compared to controls, as indicated by invTLC differences (0.490 ± 0.578 s−1 and 0.208 ± 0.106 s−1, respectively; P = 0.002). Mean deviation (MD) of the better eye was significantly associated with driving risk under fog, with a breakpoint of −9 dB identified by piecewise regression. For values below the breakpoint, each 1 dB lower MD of better eye was associated with 0.117 s−1 higher invTLC under fast speed (adjusted R2 = 57.9%; P < 0.001).

Conclusions: Glaucoma patients have a steeper increase in driving risk under fog conditions when compared to healthy subjects, especially when the severity of visual field damage falls below −9 dB of MD in the better eye.

Translational Relevance: By investigating the relationship between driving risk and disease severity breakpoint, this study may provide guidance to clinicians in recognizing glaucoma patients who may be unfit to drive in complex situations such as fog.

Introduction
Glaucoma is a progressive optic neuropathy characterized by degeneration of retinal ganglion cells and their axons. The disease frequently results in visual field loss and is one of the leading causes of visual impairment.1,2 
Driving is the primary mode of transportation worldwide and the ability to drive is intimately associated with health-related quality of life.35 As a visually intensive task, driving potentially could be affected by conditions, such as glaucoma.6,7 Although glaucoma seems to be associated with higher risk for motor vehicle collisions (MVCs),814 the specific driving conditions and disease characteristics that can lead to increased driving risk have not been well clarified. 
Fog is one of the most dangerous conditions to drive in. Collisions in poor visibility conditions, such as fog, tend to result in more severe injuries and involve more vehicles.15 Previous studies have suggested that drivers tend to underestimate their speed when driving in fog conditions and, thus, drive too fast for the available preview.1618 This occurs because a reduction in optic flow and scene contrast suppress perception of speed under these adverse conditions. Since fog causes a reduction in contrast of the visual field, the apparent speed of the vehicle slows, causing the individuals to drive faster.16 
Due to safety concerns, studies of driving risk under fog are difficult to perform using vehicles in a real environment. However, driving simulation has been used extensively to investigate driving risk under fog.1925 In one study, older drivers were found to be at greater risk for MVCs in dense fog due to a decreased ability to quickly detect impeding collision events.23 Due to the loss of visual sensitivity, it is possible that glaucoma patients may have an even more pronounced increase of driving risk under fog compared to healthy subjects. However, to the best of our knowledge, this relationship has not been investigated previously in the literature. 
The purpose of this study was to evaluate whether glaucoma patients experience a greater increase in risk when driving under fog conditions compared to age-matched healthy control subjects. 
Methods
Participants from this study were included in a prospective longitudinal study designed to evaluate functional impairment in glaucoma conducted at the Visual Performance Laboratory of the University of California, San Diego. The institutional review board at the University of California, San Diego approved the methods, and written informed consent was obtained from all participants. The study adhered to the laws of the Health Insurance Portability and Accountability Act, and all study methods complied with the Declaration of Helsinki guidelines for human subject research. 
All participants underwent a comprehensive ophthalmologic examination, including review of medical history, visual acuity, slit-lamp biomicroscopy, intraocular pressure measurement using Goldmann applanation tonometry, corneal pachymetry, gonioscopy, dilated funduscopy examination, stereoscopic optic disc photography, and standard automated perimetry (SAP) using the 24-2 Swedish Interactive Thresholding Algorithm (SITA) Standard of the Humphrey Field Analyzer II (Carl Zeiss Meditec, Inc., Dublin, CA). Only patients with reliable test results (≤33% fixation losses and ≤15% false-positives) were included. Visual acuity was measured using the Early Treatment Diabetic Retinopathy Study chart and letter acuity was expressed as the logarithm of the minimum angle of resolution (logMAR). Only subjects with open angles on gonioscopy were included. Patients with coexisting retinal disease, cataract (Lens Opacities Classification System III grade ≥2),26 uveitis, amblyopia, or nonglaucomatous optic disc neuropathy were excluded from the study. 
Glaucoma was defined by the presence of two or more consecutive abnormal SAP tests, defined as a pattern standard deviation with P < 0.05 and/or glaucoma hemifield test results outside normal limits, and evidence of glaucomatous optic neuropathy based on masked assessment of stereo photographs. A subject was considered to have glaucoma if damage was present in at least one eye. All subjects participating in the study were active drivers with a valid California driver license and completed the Montreal Cognitive Assessment (MoCA) test, a cognitive screening tool developed to detect mild cognitive impairment.27 
Driving Simulator
All subjects drove in a high-fidelity driving simulator (Realtime Technologies, Inc., Royal Oak, MI) consisting of a B pillar forward full-size Ford Fusion cab equipped with a realistic motion platform, force feedback steering, and vacuum assist brake system (Fig. 1). A three-degrees of freedom motion-system allows the vehicle to pitch, roll, and heave to provide vestibular cues along with the visual scene. The visual system consists of three 1920 × 1200 resolution projectors displaying the image on the front screens for a total of 210°. Two LCD displays are used in the side mirror housings and a large flat screen/projector combination behind the cab allows the driver to use side and rear views. A 5.1 sound system with full Doppler effect surrounds the vehicle for realistic vehicle and ambient traffic sounds. 
Figure 1
 
High-fidelity driving simulator (Realtime Technologies, Inc.) consisting of a B pillar forward full-size Ford Fusion cab equipped with a realistic motion platform, three 1920 × 1200 resolution projectors displaying the image on the front screens, two LCD displays used in the side mirror housings, a large flat screen/projector combination behind the cab, and a 5.1 sound system with full Doppler effect.
Figure 1
 
High-fidelity driving simulator (Realtime Technologies, Inc.) consisting of a B pillar forward full-size Ford Fusion cab equipped with a realistic motion platform, three 1920 × 1200 resolution projectors displaying the image on the front screens, two LCD displays used in the side mirror housings, a large flat screen/projector combination behind the cab, and a 5.1 sound system with full Doppler effect.
The tests consisted of a curve negotiation task on a winding country road without and with fog (Fig. 2). The curve negotiation task required drivers to drive on an ordinary winding road within a lane width of 3.6 m for 2 minutes. After a practice session, the curve negotiation task was performed without fog. Subsequently, drivers were required to drive the same scenario, this time with a fog that limited preview to 30 m. The nonfog and fog drives were completed at two automatically controlled speeds of 35 (slow) and 55 (fast) miles per hour, which means that the drivers could not control their speed (accelerate or decelerate the vehicle) during the test. This allowed that both groups performed the tasks in the same scenarios and at the same speeds, assuring an equally demanding task for both groups, thereby eliminating the effect of individual differences that would be caused by speed choice. 
Figure 2
 
Curve negotiation task under fog condition. The task required drivers to drive on an ordinary winding road within a lane width of 3.6 m with a fog that limited preview to 30 m.
Figure 2
 
Curve negotiation task under fog condition. The task required drivers to drive on an ordinary winding road within a lane width of 3.6 m with a fog that limited preview to 30 m.
The road curvature profile and the two speeds were chosen such that the driving task was more demanding at the fast speed.28 By using a sum of sinusoids for the road curvature profile, continuous steering was required (never a constant radius turn), and the participants would be less likely to recognize certain curve patterns that could cause them to steer also based on memory rather than solely on perception. Such a road that requires continuous visually-based vehicle control is expected to increase performance assessment sensitivity. 
A preview time of approximately 2 seconds to the visibility limit of road marking ahead is considered an absolute minimum limit for safe driving.29 The fog preview distance at 30 m at these particular speeds was chosen such that the slow speed fell on this 2-second limit while the high speed caused drivers to experience a high level of visual demand to protect their safety margins and, thus, their risk. These conditions allowed us to assess driving performance and risk under normal conditions and under simulated high-risk conditions caused by a short preview. 
Driving performance was quantified using time-to-line crossing (TLC). Mathematically, it is defined as the distance between the center of the vehicle and the point at which the vehicle's heading vector intersects one of the two lane edges divided by the speed (Fig. 3).30 It quantifies a safety margin measuring the time available to the driver before the car would leave the lane.28,31 Time-to-line crossing has been shown to be a valuable method to quantify curve-driving performance.30,32 On a straight road, TLC can become very large or even infinite. To avoid this numerical singularity in data analysis, the inverse TLC (invTLC) can be used.33 Also, since drivers try to avoid risk by having a larger safety margin, invTLC transfers to a risk measure. Higher invTLC values indicate greater risk, as less time is available to avoid drifting out of the road, and the driver, therefore, experiences smaller safety margins. For example, an invTLC of 0.5 s−1 indicates that the driver is about to drift out of the road within 2 seconds if the current heading were maintained or equivalently if the driver would let go of the steering wheel.28 
Figure 3
 
Distance to line crossing (DLC) is the distance between the center of the vehicle and the point at which the vehicle's heading vector intersects one of the two-lane edge. Solid arrows show the straight heading DLC and dashed arrows show the actual path if the current steering angle is maintained. TLC = DLC/speed.
Figure 3
 
Distance to line crossing (DLC) is the distance between the center of the vehicle and the point at which the vehicle's heading vector intersects one of the two-lane edge. Solid arrows show the straight heading DLC and dashed arrows show the actual path if the current steering angle is maintained. TLC = DLC/speed.
Statistical Analysis
Univariable and multivariable regression models were used to investigate differences in driving risk between glaucoma patients and healthy control subjects under different fog and speed conditions, and also to investigate the effect of severity of visual field loss (SAP mean deviation [MD] of better eye) on driving risk. Piecewise regression models, in which the independent variable is partitioned into intervals and separate regression lines are fitted to each interval, also were used to determine whether a breakpoint in the MD of the better eye existed that could be interpreted as a critical value below which driving risk may begin to become significantly greater due to the inability to compensate for the visual loss. Multivariable models adjusted for potentially confounding variables, such as visual acuity, age, sex, race, cognitive impairment (MoCA score), and driving exposure (average mileage driven per week). 
All statistical analyses were performed using commercially available software Stata, version 14 (StataCorp LP, College Station, TX). The α level (type I error) was set at 0.05. 
Results
The study included 41 glaucoma patients and 25 healthy control subjects. Table 1 describes the demographic and clinical characteristics of included subjects. Average SAP MD of the worse and better eyes of glaucoma patients were −12.8 ± 10.3 dB and −6.5 ± 7.2 dB, respectively (Table 1). 
Table 1
 
Demographic and Clinical Characteristics of Subjects Included in the Study
Table 1
 
Demographic and Clinical Characteristics of Subjects Included in the Study
Table 2 shows the results of driving risk as assessed by invTLC in glaucoma and control subjects under fog and nonfog conditions. In the nonfog clear condition, statistically significant differences were seen between glaucoma and healthy controls, with higher invTLC values for glaucoma patients at slow speed (0.349 ± 0.020 vs. 0.339 ± 0.012 s−1, respectively; P = 0.027), as well as at fast speed (0.809 ± 0.171 vs. 0.723 ± 0.050 s−1, respectively; P = 0.004). Under fog, driving risk also was greater in glaucoma patients at slow (0.373 ± 0.037 vs. 0.356 ± 0.013 s−1, respectively; P = 0.044) and fast (1.299 ± 0.581 vs. 0.931 ± 0.097 s−1, respectively; P < 0.001) speeds. 
Table 2
 
Results of Driving Risk Metric (invTLC) in Glaucoma Patients and Healthy Control Subjects
Table 2
 
Results of Driving Risk Metric (invTLC) in Glaucoma Patients and Healthy Control Subjects
We were interested in determining whether glaucoma patients experienced a significantly greater increase in driving risk under simulated fog conditions compared to controls. Therefore, we calculated and compared the differences in invTLC between fog and nonfog for the two groups. When driving at fast speed under fog, glaucoma patients showed a significantly greater increase in risk compared to controls. The mean invTLC difference was 0.490 ± 0.578 s−1 for glaucoma subjects compared to 0.208 ± 0.106 s−1 for healthy controls (P = 0.002; Table 2). Such difference was not observed in slow speed. 
A breakpoint of −9 dB that yielded the best fitting model in a piecewise regression was determined. In this model, for disease severity better than the breakpoint (MD of better eye higher than −9 dB), there was no significant relationship between increase in driving risk under fog and severity of visual field loss. However, the relationship between driving risk and disease severity became highly significant when considering MD values below or equal to the breakpoint, as indicated in Figure 4. When MD of the better eye was less than or equal to −9 dB, each 1 dB lower MD was associated with 0.004 s−1 higher invTLC difference in slow speed (adjusted R2 = 29.2%; P = 0.016) and 0.117 s−1 higher invTLC difference in fast speed (adjusted R2 = 57.9%; P < 0.001). In the multivariable model, when MD of the better eye was less than or equal to −9 dB, severity of visual field loss still was significantly associated with increase in driving risk. Each 1 dB lower MD of the better eye was associated with 0.105 s−1 higher invTLC difference in fast speed (P < 0.001; Table 3). 
Figure 4
 
Scatterplot with piecewise fitted regression line illustrating the relationship between difference in invTLC (s−1; fog minus nonfog conditions) and MD of better eye (dB) at fast speed.
Figure 4
 
Scatterplot with piecewise fitted regression line illustrating the relationship between difference in invTLC (s−1; fog minus nonfog conditions) and MD of better eye (dB) at fast speed.
Table 3
 
Results of the Piecewise Univariable and Multivariable Regression Models for Explaining Relationship Between Differences in Driving Risk Metric (invTLC) under Fog and Nonfog Conditions in Fast Speed and Severity of Visual Field Loss (MD of Better Eye)* in Glaucoma Patients and Healthy Control Subjects
Table 3
 
Results of the Piecewise Univariable and Multivariable Regression Models for Explaining Relationship Between Differences in Driving Risk Metric (invTLC) under Fog and Nonfog Conditions in Fast Speed and Severity of Visual Field Loss (MD of Better Eye)* in Glaucoma Patients and Healthy Control Subjects
Discussion
The presence of fog reduces visibility, optic flow, and effective field of view, making for a challenging driving experience. However, even though some degradation of driving performance under fog can be expected for most drivers, our results showed that glaucoma patients had a steeper increase in driving risk under fog compared to healthy control subjects. 
In a curve negotiation task, the driver must match the road curvature and keep a proper distance from the lane edges. In our study, we found that the presence of fog significantly reduced the time to drift out of the road for glaucoma patients compared to healthy controls, as indicated by the invTLC metric. This finding indicates that glaucoma patients had a lower safety margin and could be at higher risk for MVCs when driving under fog. We showed that severity of visual field loss as assessed by SAP was significantly associated with greater increase in driving risk under fog conditions at slow and fast speeds. Interestingly, a breakpoint was identified in the relationship between driving risk and disease severity. For patients with disease severity (SAP MD in the better eye) above the breakpoint of −9 dB, no statistically significant relationship was seen between disease severity and driving risk. In contrast, a steep relationship was seen for those with disease severity below the breakpoint. These findings seem to suggest the possibility that, up to disease severity levels of −9 dB in the better eye, the visual system still seems to be able to compensate and provide adequate input for successful task performance, avoiding excessive risk. However, below the breakpoint, there seems to be a quick “breakdown” leading to rapid degradation in performance and increase in risk. It should be noted, however, that the value of −9 dB obtained in our study was specific to the tasks and population studied and may differ in other situations. 
In fog, visibility (i.e., contrast) is reduced with increasing distance. Therefore, when the driver looks straight ahead, the regions of better visibility are located near the vehicle and are viewed with the peripheral field, whereas the regions of worse visibility are located far from the vehicle and are viewed with the central field.34 When negotiating a curve, the far region is needed to detect which direction the road is going so that anticipatory steering can be employed, whereas the near region (peripheral) is needed to detect large lateral deviations and drifts so that corrective steering can be applied. An increase in driving risk under fog would be expected for healthy and glaucoma subjects due to the difficulty in performing anticipatory steering from the reduced vision at far distance caused by the fog. This was, in fact, shown in our study, as glaucoma and healthy subjects had greater risk in fog compared to nonfog situations. In addition, patients with glaucoma and significant visual field loss also would have greater difficulty in performing corrective steering measures, as these are mostly dependent on the peripheral vision. This likely explains the significant increase in driving risk under fog for glaucoma patients compared to healthy subjects, as shown by reduced times to drift out of the lane. It is important to note that even though differences in driving risk between glaucoma and healthy subjects also were seen in nonfog conditions, these differences were much smaller in nonfog than in fog. That is, when challenged by fog, glaucoma patients with substantial visual field loss had a steeper increase in driving risk compared to healthy subjects. 
Our results showed that the effect of fog was worse under faster speeds. This is expected, as driving in fast speed will be more demanding, especially under fog. Although drivers know they should slow down because fog reduces visibility, many still will drive too quickly. Previous studies have suggested that drivers tend to underestimate their speed when driving under fog conditions.1618 In foggy conditions, drivers may think they are driving more slowly than they actually are and, therefore, increase their speed. Due to their reduced field of view, speed misestimation could be even worse for glaucoma patients. In our study, we used fixed speeds for the driving tasks to avoid confounding effects of different speeds that could be adopted by patients. In the present study, we used a controlled driving speed to decrease its confounding effect in the study of the association between fog and driving risk. However, future investigations should evaluate the effect of speed estimation on driving risk in glaucoma. 
Evidence suggests that glaucoma patients seem more likely to avoid high-risk driving situations.35 Ono et al.36 showed that, among 252 glaucoma subjects, 18% avoided driving in fog. However, in a recent study (Correa PC, et al. IOVS 2016:57:ARVO E-Abstract 3420), we showed that there is only a relatively weak relationship between subjective concerns about driving ability and objective driving performance in patients with glaucoma. Only approximately one-third of patients who were identified as having unsatisfactory driving performance on a simulator actually had significant concerns about their ability to drive, suggesting that a large number of patients with glaucoma may not adopt necessary precautions to avoid risky driving situations. By investigating the relationship between driving risk and disease severity breakpoint, our study may provide guidance to clinicians in recognizing glaucoma patients who may be unfit to drive in complex situations, such as fog. Our results also suggested that individualized assessment of driving fitness using driving simulators could be helpful in providing further assessment of driving risk.3840 
The present study had limitations. Even though driving simulator metrics have been shown to be predictive of real-life driving metrics, it is possible that our estimates of risk may differ from those that would occur in real-life situations. However, exposing subjects to the risky situations simulated in our study would be difficult, if not impossible, in real life. In addition, behind-the-wheel investigations would not allow controlled experimental conditions, such as exposing the patients to the same amount of fog under the same road and speed conditions. As another limitation of our study, the relatively small sample did not allow us to investigate the relationship between patterns and locations of visual field defects and their impact on driving risk. Future studies should investigate whether specific patterns of defects may affect driving risk. 
In conclusion, patients with glaucoma had a significantly greater increase in driving risk when challenged by simulated fog conditions compared to healthy control subjects. Driving risk under fog showed a tight relationship with disease severity for patients with SAP MD in the better eye lower than −9 dB, suggesting a breakpoint below which the visual system seems unable to adequately compensate for the demands of driving in this situation. Therefore, subjects with visual field damage that falls below this breakpoint may be at increased risk when performing complex driving tasks. 
Acknowledgments
Supported in part by National Institutes of Health/National Eye Institute (Bethesda, MD) Grants EY021818 and EY025056 (FAM), and Core Grant P30EY022589; an unrestricted grant from Research to Prevent Blindness; fellowships from Brazilian National Council for Scientific and Technological Development (CNPq) 233829/2014-8 (ADF); fellowships from National Health and Medical Research Council (NHMRC) 1104985 (ZW). The authors alone are responsible for the content and writing of this paper. The study was registered at ClinicalTrials.gov with registration number NCT00221897. 
Disclosure: A. Diniz-Filho, None; E.R. Boer, None; A. Elhosseiny, None; Z. Wu, None; M. Nakanishi, None; F.A. Medeiros, Alcon Laboratories (F), Bausch & Lomb (F), Carl Zeiss Meditec (F), Heidelberg Engineering (F), Merck (F), Allergan (F), Sensimed (F), Topcon (F), Reichert (F), National Eye Institute (F), Allergan (C), Carl Zeiss Meditec (C), Novartis (C) 
References
Weinreb RN, Aung T, Medeiros FA. The pathophysiology and treatment of glaucoma: a review. JAMA. 2014 ; 311: 1901–1911.
Tham YC, Li X, Wong TY, et al. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology. 2014 ; 121: 2081–2090.
Fonda SJ, Wallace RB, Herzog AR. Changes in driving patterns and worsening depressive symptoms among older adults. J Gerontol B Psychol Sci Soc Sci. 2001 ; 56: S343–S351.
Ragland DR, Satariano WA, MacLeod KE. Driving cessation and increased depressive symptoms. J Gerontol A Biol Sci Med Sci. 2005 ; 60: 399–403.
Ramulu PY, West SK, Munoz B, et al. Driving cessation and driving limitation in glaucoma: the Salisbury Eye Evaluation Project. Ophthalmology. 2009 ; 116: 1846–1853.
Owsley C, McGwin GJr. Vision and driving. Vision Res. 2010 ; 50: 2348–2361.
Medeiros FA, Weinreb RN, Boer ER, Rosen PN. Driving simulation as a performance-based test of visual impairment in glaucoma. J Glaucoma. 2012 ; 21: 221–227.
Szlyk JP, Mahler CL, Seiple W, et al. Driving performance of glaucoma patients correlates with peripheral visual field loss. J Glaucoma. 2005 ; 14: 145–150.
Haymes SA, Leblanc RP, Nicolela MT, et al. Risk of falls and motor vehicle collisions in glaucoma. Invest Ophthalmol Vis Sci. 2007 ; 48: 1149–1155.
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: 4177–4181.
McGwin GJr, Huisingh C, Jain SG, et al. Binocular visual field impairment in glaucoma and at-fault motor vehicle collisions. J Glaucoma. 2015 ; 24: 138–143.
Ono T, Yuki K, Asaoka R, et al. Glaucomatous visual field defect severity and the prevalence of motor vehicle collisions in Japanese: a hospital/clinic-based cross-sectional study. J Ophthalmol. 2015 ; 2015: 1–8.
Blane A. Through the looking glass: a review of the literature investigating the impact of glaucoma on crash risk, driving performance, and driver self-regulation in older drivers. J Glaucoma. 2016 ; 25: 113–121.
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: 109–116.
Abdel-Aty M, Ekram AA, Huang H, Choi K. A study on crashes related to visibility obstruction due to fog and smoke. Accid Anal Prev. 2011 ; 43: 1730–1737.
Snowden RJ, Stimpson N, Ruddle RA. Speed perception fogs up as visibility drops. Nature. 1998 ; 392: 450.
Horswill MS, Plooy AM. Reducing contrast makes speeds in a video-based driving simulator harder to discriminate as well as making them appear slower. Perception. 2008 ; 37: 1269–1275.
Owens DA, Wood J, Carberry T. Effects of reduced contrast on the perception and control of speed when driving. Perception. 2010 ; 39: 1199–1215.
Ni R, Kang JJ, Andersen GJ. Age-related declines in car following performance under simulated fog conditions. Accid Anal Prev. 2010 ; 42: 818–826.
Trick LM, Toxopeus R, Wilson D. The effects of visibility conditions, traffic density, and navigational challenge on speed compensation and driving performance in older adults. Accid Anal Prev. 2010 ; 42: 1661–1671.
Brooks JO, Crisler MC, Klein N, et al. Speed choice and driving performance in simulated foggy conditions. Accid Anal Prev. 2011 ; 43: 698–705.
Mueller AS, Trick LM. Driving in fog: the effects of driving experience and visibility on speed compensation and hazard avoidance. Accid Anal Prev. 2012 ; 48: 472–479.
Ni R, Bian Z, Guindon A, Andersen GJ. Aging and the detection of imminent collisions under simulated fog conditions. Accid Anal Prev. 2012 ; 49: 525–531.
Saffarian M, Happee R, Winter JC. Why do drivers maintain short headways in fog? A driving-simulator study evaluating feeling of risk and lateral control during automated and manual car following. Ergonomics. 2012 ; 55: 971–985.
Li X, Yan X, Wong SC. Effects of fog, driver experience and gender on driving behavior on S-curved road segments. Accid Anal Prev. 2015 ; 77: 91–104.
Chylack LT,Jr, Wolfe JK, Singer DM, et al. The Lens Opacities Classification System III. The Longitudinal Study of Cataract Study Group. Arch Ophthalmol. 1993 ; 111: 831–836.
Nasreddine ZS, Phillips NA, Bedirian V, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005 ; 53: 695–699.
van Winsum W, Godthelp H. Speed choice and steering behavior in curve driving. Hum Factors. 1996 ; 38: 434–441.
MacAdam CC. Application of an optimal preview control for simulation of closed-loop automobile driving. IEEE Trans Syst Man Cybern. 1981 ; 11: 393–399.
Godthelp H, Milgram P, Blaauw GJ. The development of a time-related measure to describe driving strategy. Hum Factors. 1984 ; 26: 257–268.
van Winsum W, Brookhuis KA, de Waard D. A comparison of different ways to approximate time-to-line crossing (TLC) during car driving. Accid Anal Prev. 2000 ; 32: 47–56.
Godthelp H. Vehicle control during curve driving. Hum Factors. 1986 ; 28: 211–221.
Green P. Standard definitions for driving measures and statistics: overview and status of recommended practice J2944. Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. Eindhoven, Netherlands: ACM, 2013: 184–191.
Snowden RJ, Freeman TC. The visual perception of motion. Curr Biol. 2004 ; 14: R828–R831.
McGwin GJr, Mays A, Joiner W, et al. Is glaucoma associated with motor vehicle collision involvement and driving avoidance? Invest Ophthalmol Vis Sci. 2004 ; 45: 3934–3939.
Ono T, Yuki K, Awano-Tanabe S, et al. Driving self-restriction and motor vehicle collision occurrence in glaucoma. Optom Vis Sci. 2015 ; 92: 357–364.
Kübler TC, Kasneci E, Rosenstiel W, et al. Driving with glaucoma: task performance and gaze movements. Optom Vis Sci. 2015 ; 92: 1037–1046.
Prado Vega R, van Leeuwen PM, Rendón Vélez 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.
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.
Wood JM, Black AA, Mallon K, et al. Glaucoma and driving: on-road driving characteristics. PLoS One. 2016 ; 11: e0158318.
Figure 1
 
High-fidelity driving simulator (Realtime Technologies, Inc.) consisting of a B pillar forward full-size Ford Fusion cab equipped with a realistic motion platform, three 1920 × 1200 resolution projectors displaying the image on the front screens, two LCD displays used in the side mirror housings, a large flat screen/projector combination behind the cab, and a 5.1 sound system with full Doppler effect.
Figure 1
 
High-fidelity driving simulator (Realtime Technologies, Inc.) consisting of a B pillar forward full-size Ford Fusion cab equipped with a realistic motion platform, three 1920 × 1200 resolution projectors displaying the image on the front screens, two LCD displays used in the side mirror housings, a large flat screen/projector combination behind the cab, and a 5.1 sound system with full Doppler effect.
Figure 2
 
Curve negotiation task under fog condition. The task required drivers to drive on an ordinary winding road within a lane width of 3.6 m with a fog that limited preview to 30 m.
Figure 2
 
Curve negotiation task under fog condition. The task required drivers to drive on an ordinary winding road within a lane width of 3.6 m with a fog that limited preview to 30 m.
Figure 3
 
Distance to line crossing (DLC) is the distance between the center of the vehicle and the point at which the vehicle's heading vector intersects one of the two-lane edge. Solid arrows show the straight heading DLC and dashed arrows show the actual path if the current steering angle is maintained. TLC = DLC/speed.
Figure 3
 
Distance to line crossing (DLC) is the distance between the center of the vehicle and the point at which the vehicle's heading vector intersects one of the two-lane edge. Solid arrows show the straight heading DLC and dashed arrows show the actual path if the current steering angle is maintained. TLC = DLC/speed.
Figure 4
 
Scatterplot with piecewise fitted regression line illustrating the relationship between difference in invTLC (s−1; fog minus nonfog conditions) and MD of better eye (dB) at fast speed.
Figure 4
 
Scatterplot with piecewise fitted regression line illustrating the relationship between difference in invTLC (s−1; fog minus nonfog conditions) and MD of better eye (dB) at fast speed.
Table 1
 
Demographic and Clinical Characteristics of Subjects Included in the Study
Table 1
 
Demographic and Clinical Characteristics of Subjects Included in the Study
Table 2
 
Results of Driving Risk Metric (invTLC) in Glaucoma Patients and Healthy Control Subjects
Table 2
 
Results of Driving Risk Metric (invTLC) in Glaucoma Patients and Healthy Control Subjects
Table 3
 
Results of the Piecewise Univariable and Multivariable Regression Models for Explaining Relationship Between Differences in Driving Risk Metric (invTLC) under Fog and Nonfog Conditions in Fast Speed and Severity of Visual Field Loss (MD of Better Eye)* in Glaucoma Patients and Healthy Control Subjects
Table 3
 
Results of the Piecewise Univariable and Multivariable Regression Models for Explaining Relationship Between Differences in Driving Risk Metric (invTLC) under Fog and Nonfog Conditions in Fast Speed and Severity of Visual Field Loss (MD of Better Eye)* in Glaucoma Patients and Healthy Control Subjects
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