Statistical Assessment of the Glare Issue - Human and Natural Elements 

Eun-Ha Choi and Santokh Singh 

National Center for Statistics and Analysis 

National Highway Traffic Safety Administration 

400 Seventh Street S.W., Washington, DC 20590 

 Eun-Ha.Choi@nhtsa.dot.gov, Santokh.Singh@nhtsa.dot.gov 

Abstract 

Visibility is one of the basic requirements for safe driving; any type of obscurity in a driver’s vision can interfere with the 

driving task and impose a threat to the roadway safety. Glare is known to be one of the significant factors causing vision 

obstruction. One would agree that headlamps are not the only source of glare. Sometimes, sunlight may also obstruct a 

driver’s vision and contributing to a crash. 

In this paper, the glare issue and some of its aspects are studied using statistical methods. Crash data are used in the analyses. 

Descriptive statistical and contingency analyses are employed with the objective of a preliminary investigation. The k-nearest 

neighbor approach is used to assess the potential of some factors in jointly contributing to the crash involvement of drivers 

who are exposed to glare. Configural frequency analysis is used for identifying the data segments in which glare, as a crashcontributing factor, is more predominant. Some results based on drivers’ perceptions of glare, reported in a prior study, are 

discussed to compare perception with reality, i.e., how drivers perceive glare and how in real life their driving ability is 

affected by glare. 

Keywords: glare, headlamps, sunlight, vision obstruction 

1. Introduction 

Drivers often confront vehicles in front, from behind, or from both directions that emit blinding glare. Although it has been a 

concern of drivers since electric headlamps replaced oil lamps, in recent years an increasing number of drivers have 

complained to the National Highway Traffic Safety Administration about glare from headlamps. Many drivers described 

being “blinded” for a few seconds after exposure to the glare and needed to slow down, while others mentioned their 

involvement in a crash or a near miss. Glare, especially due to increasing intensity, often causes discomfort to the driver as 

well as vision obstruction that may contribute to the occurrence of a crash. 

At times, the sun’s glare, too, may contribute to crash occurrence. According to Wells [1], who shared her experience in 

‘record online’, “The sun's nasty spectrum shone down on my windshield so brightly it could have signaled a 747 to land 

right there in front of my car. Sun glare causes accidents – multiple-car pile-ups, even.” This driver could be one of the 

thousands who have had such an experience from glare coming from the natural source: Sun. 

The effect of glare remains the same whether it comes from headlamps or sunlight; it obstructs the driver’s vision and affects 

the driving performance. The challenge for vehicle manufacturers and regulators is to provide the driver with a reasonable 

level of protection from glare. Empirical research is necessary in order to address this issue. Using the crash data, the present 

study identifies factors that may contribute to the crash involvement of a driver who is exposed to glare. 

Studies have shown that parameters, such as age, visual health, complexity of location, glare intensity, etc., affect the visual 

performance of a driver [2-6]. However, most of these studies look at the phenomenon by considering one factor at a time 

under controlled conditions. To consider what may actually happen on the roadways when glare is one of the elements in a

crash scenario, this study makes a statistical assessment of the glare issue using the crash data. In the following section, the 

databases used for this purpose are briefly described. The objective of the study is mentioned in Section 3. Section 4 includes 

analysis for identification of some glare-relevant variables for subsequent analysis. Using descriptive analysis, Section 5 

provides a broad picture of the glare issue with respect to the identified variables. A classification model is developed in 

Section 6 to identify variables related to driver, vehicle, and roadway that have the potential to describe a glare-related crash. 

Further analysis is done in Section 7 to identify the data segments in which glare in conjunction with driver age and sex 
 

 

 

 

 

 

 

 

 

 

 

 

 

seems to be a predominant factor. The study concludes with the summary and conclusions in Section 8. References used in 

this study are provided in Section 9. 

2. Data 

The crash data compiled in National Automotive Sampling System-General Estimates System (NASS-GES) [7], to be 

referred to as GES data in the subsequent discussion are used in the analyses conducted in this study. Some results from a 

previous study based on Omnibus Survey Data (OSD) [8] are also included in the discussion. 

GES is a nationally representative probability sample selected from police-reported crashes that occur annually. These data 

provide information about drivers, vehicles, and other factors in crashes that result in fatalities, injuries, and major property 

damages. Four years of GES data (2000 to 2003) are used in this study. In order to segregate the effect of glare, these data are 

restricted to crashes that occurred in clear weather. The Omnibus Survey is a nationally representative telephone survey 

conducted by the Bureau of Transportation Statistics. This database is based on monthly surveys in which respondents are 

asked to express their opinion about different issues; the glare issue was on the questionnaire for the first six months of 2002. 

3. Objective of the Study 

While in OSD the term “glare” refers to glare from other vehicles, in GES, glare is one of the several elements of the variable 

called “Driver’s Vision Obscured By,” namely “Reflected Glare, Bright Sunlight, Headlights.” In the subsequent analysis and 

discussion, the term glare will be used to mean “driver’s vision obscured by reflected glare, bright sunlight, or headlights.” 

To be specific, in GES data, a crash with value 2 recorded for the variable Driver’s Vision Obscured By refers to a crash in 

which the driver’s vision was obstructed by: (i) sunlight or headlamps’ light reflected from other objects, (ii) direct sunlight, 

or (iii) direct light from headlamps. The objective is to find out how glare might have contributed to a crash in combination 

with other factors related to driver, vehicle, and roadway. 

4. Selection of Analysis Variables 

Preliminarily, the variables age, sex, travel speed, maneuver, crash hour, trafficway flow, number of lanes, manner of 

collision, roadway profile, and vehicle role are considered. These variables are selected to consider the driver who is affected 

by the glare, the roadway scenarios under which the crash occurred, and the outcome of the crash. In order to study the extent 

to which glare from headlamps (human source) or sunlight (natural source) is likely to contribute to crash involvement of 

drivers, two groups of drivers were considered from GES 2000-2003 data: drivers whose crash involvement could be 

attributed to vision obstruction as a result of glare and those whose vision was not obstructed at all. The respective percentage 

representations of these two groups in the four years’ data are 1 percent and 58 percent of the total number of drivers 

involved in crashes in clear weather. In the subsequent discussion, the term glare-related be used to refer to a crash whose 

occurrence may be attributed to glare and the term glare-exposed for a class of drivers whose crash involvement may be 

attributed to glare. 

Contingency analysis is conducted to test the hypothesis of no association between glare and some of the possible factors that 

might have contributed to crash involvement of drivers when their vision was obstructed due to glare. For the purpose of this 

analysis, the preliminarily selected variables are categorized as shown in Table 1. The statistical software SAS 8.2 and 

SUDAAN 9.0.1 are used for computing Chi-square values and the associated p-values for each variable listed in Table 1. 

The results based on GES data are presented in Table 2. A Chi-square value with low p-value (<<0.0001) in this table shows 

significant association of the corresponding variable with glare. Specifically, the results show that driver’s age plays a 

significant role in conjunction with glare, while sex does not. The driver’s maneuvering is not associated with his/her crash 

involvement due to being exposed to glare. As regards the roadway, factors such as trafficway flow, roadway profile, and 

number of lanes are likely to contribute to crash involvement when a driver is exposed to glare. The vehicle-related factors, 

vehicle speed, crash hour, manner of collision, and vehicle role are also found significantly associated with glare. 

The variables significantly associated with glare (as established in the above analysis) are subjected to further analysis for 

deeper insight into the glare phenomenon. 
 

 

 

 

 

Table 1. Categorization of Analysis Variables 

VARIABLE (CRASH 

CONTRIBUTING FACTOR) CATEGORIES 

Glare 1: No visual obstruction, 2: Vision obstructed by glare 

Driver Age 19 and below, 20-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75 and above 

Sex Male and Female 

Maneuver 1: Driver did not maneuver to avoid, 2: Maneuver to avoid vehicle, 3: Avoidance maneuver for objects (no 

vehicle) in road 

Manner of collision Not collision, Rear-End, Angle, Head-On, Sideswipe 

Travel speed 25 and below, 26-35, 36-45,46-55, 56-65, 66 and above 

Vehicle role Noncollision, Striking, Struck, Both 

Trafficway flow Not physically divided (two-way traffic), Divided trafficway (with or without barrier), One-way traffic 

Roadway profile Level, Grade, Hillcrest, Sag 

Number of lanes 1, 2, 3, 4, 5, 6 and more 

* Crash hour Midnight- 3 am, 3 - 6 am, 6 - 9 am, 9 – 12 am, 12 - 3 pm , 3 - 6 pm, 6 - 9 pm, 9 - midnight 

* time intervals are exclusive of endpoints 

Table 2. Crash contributing factors and their association with glare 

RESPONSE 

VARIABLE VARIABLE TYPE CRASH VARIABLE ASSOCIATION 

Based on 

Chi-Square (95%) 

P-Value 

GLARE 

Subjective 

Age group 40.65 << 0.0001 

Travel speed 221.36 << 0.0001 

Sex 0.27 0.6066* 

Maneuver 1.92 0.3823* 

Circumstantial 

Trafficway flow 105.94 << 0.0001 

Roadway profile 29.05 << 0.0001 

Number of lanes 79.98 << 0.0001 

Crash hour 705.71 << 0.0001 

Consequential 

Manner of collision 180.41 << 0.0001 

Vehicle role 546.53 << 0.0001 

* Not significant 

Data Source: GES 2000-2003 

5. Broad picture 

A broad picture is provided to show the extent to which glare may contribute to a crash in combination with each of the 

factors found to be significantly associated with glare. Percent frequency distributions over the defined categories of these 

factors are used for this purpose (Figure 1-8). The comparison of distributions for each variable in the presence or absence of 

glare enables an assessment of the difference glare can make in a driver’s crash involvement. The results in Figure 1 show 

that 38.5 percent of the crash-involved drivers reported as exposed to glare were of age 45 years and older. This percentage is 

higher than the percentage (30 percent) of the drivers, involved in crashes without vision obstruction, who fell in the same 

age group. On the other hand, 61.5 percent of the glare-exposed drivers were below 45 years of age, which is lower than 70 

percent of the drivers in the no-vision-obstruction category who were of the same age group. Also, when the crash occurred, 

most of the glare-exposed drivers (62 percent) were on roadways with two lanes as compared to 50 percent of those who 

were involved in crashes without vision obstruction when they were on the same type of roadway (Figure 4). 

14% 

12% 

14% 

12% 

21% 

19% 

20% 19% 

14% 

17% 

8% 

9% 

5% 

6% 6% 

10% 

15% 

20% 

25% 

<=19 

20-24 

25-34 

35-44 

45-54 

55-65 

65-74 

74< 

52% 

80% 

42% 

18% 20% 

30% 

40% 

50% 

60% 

70% 

80% 

90% 

Noncolli 

sion 

Striking 

Struck 

Both 
14%

12%

14%

12%

21%

19%

20% 19%

14%

17%

8%

9%

5%

6%

3%

6%

0%

5%

10%

15%

20%

25%

(a) No vision obstruction (b) Glare

<=19

20-24

25-34

35-44

45-54

55-65

65-74

74<

 

2% 1%

52%

80%

42%

18%

5% 2%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

(a) No vision

obstruction

(b) Glare

Noncolli

sion

Striking

Struck

Both

Figure1. Percent frequency distribution of drivers by Driver age: Figure2. Percent frequency distribution of drivers by Vehicle (a) in 

no vision obstruction group (b) in glare group. roles: (a) in no vision obstruction group (b) in glare group. 

 

15% 14%

39%

36% 34%

45%

2% 3%

8%

3%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

(a) No vision obstruction (b) Glare

Not Coll w /

MVIT

Rear-End

Angle

Head-On

Sidsw ip

 

3% 3%

50%

62%

16%

12%

16%

11% 12%

9%

3% 2%

0%

10%

20%

30%

40%

50%

60%

70%

(a) No vision obstruction (b) Glare

One

Two

Three

Four

Five

Six or

more

Figure3. Percent frequency distribution of drivers by Manner of Figure4. Percent frequency distribution of drivers by Number of 

collision: (a) in no vision obstruction group (b) in glare group. travel lanes: (a) in no vision obstruction group (b) in glare group. 

60%

75%

35%

22%

5% 3%

0%

10%

20%

30%

40%

50%

60%

70%

80%

(a) No vision obstruction (b) Glare

Not

physically

divided (two

way traffic)

Divided

Trafficway

(with or

without

barrier)

One way

traffic

 

64%

60%

13%

19%

10% 13%

7% 6% 4%1.7% 1.2%0.5%

0%

10%

20%

30%

40%

50%

60%

70%

(a) No vision obstruction (b) Glare

<26

26-35

36-45

46-55

56-65

65<

 Figure5. Percent frequency distribution of drivers by Traffic flow: Figure6. Percent frequency distribution of drivers by Travel speed: 

(a) in no vision obstruction group (b) in glare group. (a) in no vision obstruction group (b) in glare group. 

78%

73%

2% 4% 0.3% 0.5%

20%

22%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

(a) No vision obstruction (b) Glare

level

Grade

Hillcrest

Sag

 

2%

0.4%

12%

39%

14%

9%

21%

7%

27%

29%

13% 14%

7%

2% 3%

0.4%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

(a) No vision obstruction (b) Glare

0-3am

3-6am

6-9am

9-12am

12-3pm

3-6pm

6-9pm

9-midnite

Figure7. Percent frequency distribution of drivers by Roadway Figure8. Percent frequency distribution of drivers by Crash hour: 

profile: (a) in no vision obstruction group (b) in glare group. (a) in no vision obstruction group (b) in glare group. 

(Figure 1 – Figure8) Data Source: GES 2000-2003 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Similarly, 80 percent of the glare-exposed drivers were in the striking role as compared to 52 percent of those who were in the 

same role even though their vision was not obstructed (Figure 2). Especially, if the trafficway is not physically divided, glareexposed drivers has higher percentage (75 percent) as compared to no-vision-obstructed drivers (60 percent) (Figure 5). In angle 

and head-on collisions, the group disturbed by glare has higher percentages (45 percent and 3.1 percent, respectively) than the 

group without vision obstruction, for which the respective percentages for these manners of collision are 35.6 percent and 2.2 

percent (Figure 3). Among the glare-exposed drivers, the percentage of drivers who were driving at a speed between 26 and 45 

mph is higher (32 percent) than 23 percent from the no vision obstruction category who were driving in the same speed range 

(Figure 6). While the glare group has higher percentages than the group with no vision obstruction in grade (22.4 percent) and 

hillcrest (4.3 percent) roadway profiles, the group not visually obstructed has higher percentage (77.6 percent) in level roadway 

profile (Figure 7). 38.9 percent of the glare-exposed drivers were involved in crashes between 6:00 a.m. and 9:00 a. m. (Figure 

8). On the other hand, only 12.2 percent of the drivers with no vision obstruction were involved in crashes during the same time 

interval. The crash involvement of glare-exposed drivers was more frequent (29.9 percent) during 5:00 p.m. to 8:00 p.m. as 

compared to 27.3 percent of those who were involved in crashes without vision obstruction during the same time period. 

6. Descriptive Potential of the Glare-Associated Variables 

Using contingency analysis, some factors were tested for their individual associations with glare. These factors fall into three 

categories; namely the subjective (age, sex, travel speed, maneuver), circumstantial (crash hour, number of lanes, trafficway 

flow, roadway profile), and consequential (vehicle role, manner of collision). While consequential factors merely show what 

type of outcome is likely when a glare-exposed driver is involved in a crash, the circumstantial variables point out factors that 

are conducive to a glare-exposed driver’s crash involvement. The subjective factors come into play when a driver confronts a 

glare scenario. From the crash occurrence point of view, it is important to know if these variables can jointly describe the 

occurrence of a crash following a glare-caused driving scenario. For this purpose, a statistical distance-based measure, to be 

referred to as a classifier, is developed using GES 2000-2002 data (training data for the classifier). Since the variable 

‘trafficway flow’ is categorical and significantly associated with number of lanes ( χ

2 

= 208.02 with p − value << 0.0001), it is 

excluded from the classifier. On the other hand, the variable ‘roadway profile’ being a categorical variable does not qualify for 

classification analysis. Thus, the classifier developed in this study is based on the variables, driver age, crash hour, travel speed, 

and number of lanes, found significantly associated with glare in an earlier section. If the resulting classifier has a significant 

classifying power to classify cases into two groups: glare and no vision obstruction, then the variables significantly jointly 

contribute to the crash involvement of drivers. Based on this argument, the developed classifier is used to cluster GES data of 

another year (2003) (test data) in two classes: ‘glare’ and ‘no vision obstruction’. Squared distance function is used for 

developing the classifier [9] 

2 −1 D (X 1

, X 2) = (X 1 − X 2) ′ Σ (X 1 − X 2) , 

where X1 and X2 are two observation vectors of the glare-associated variables characterizing a crash and Σ is an estimated 

covariance matrix. The algorithm partitions the data space into subspaces through many stages using the distances of the 

observations in each subspace from the observation that is being assigned to a subspace. The prior probabilities of the 

subpopulations (subspaces) are used in calculating the posterior probability Π(j / X) of membership in each class, which is 

updated at each stage, using the formula 

m j(X) π j

Π( j / X) = , 

∑ m k (X) π k 

k 

where π j

 is the prior probability of the j-th class and m j

(X ) is the proportion of observations in group j in 15-nearest 

neighbors of X . The algorithm finally converges to distinct classes, glare and no vision obstruction, in the present case. The 

classification results for a sample of 1,000 cases selected randomly from GES 2003 data are presented in Table 3. The results 

show that 80 percent of the glare cases and 69 percent of no vision obstruction cases were classified correctly. This in turn 

shows that the variables used in the classifier have the potential to describe crash involvement of drivers who experienced glare. 

As an example, the efficiency of the classifier based on these variables is demonstrated by identifying ages of both groups of 

drivers and hours of their crash involvement (Figure 9). The identification results are presented in Figure 9(a) which shows 

driver age versus crash hour scatter plot of drivers who were involved in crashes possibly due to glare (□), those who were 

crash-involved without vision obstruction (•), and ties (*), i.e., the drivers who could not be classified into one of the above 

categories. The pattern of glare-exposed drivers over time periods 6:00 a.m. to 9:00 a.m. and 5:00 p.m. to 8:00 p.m. in this 

figure is in good agreement with the actual pattern of such drivers shown in Figure 9(b). 
Table 3. Classification Summary using 15-Nearest Neighbors 

Number of Observations and Percent Classified 

 

FROM 

 TO 

 

Glare group No vision obstruction 

group 

Other (ties) Total 

Glare group 

117 30 0 147 

80% 20% 0.00 100.00 

No vision 

group 

obstruction 265 584 4 853 

31% 68.5% 0.5% 100.00 

Total 382 614 4 1000 

38% 61.5% 0.5% 100.00 

Data Source: GES 2000-2003 

Figure 9. Comparison of original scatter of crash involved drivers as described by Driver age and Crash hour and the classification plot using15-nearest neighbors classifier based on Driver age, Crash hour, Travel speed, and Number of lanes. Driver age (years) 

Figure 9 (a). 1,000 cases originally falling into glare ( ) and no vision obstruction ( ) groups. 

Figure 9 (b). 1,000 cases classified by classifier into glare ( ) no vision obstruction ( ) groups, and ties (*). 

Crash hour

Crash hour

Driver age (years) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

7. Data Segmentation Based on Age and Sex in the Presence and Absence of Glare 

In a crash system with its elements drivers, vehicles, and roadway, it is the driver who is affected first and directly by the glare 

resulting in the emergence of a crash scenario. The following analysis explores the driver attributes in conjunction with glare. 

Although contingency analysis of the crash data show that sex of the driver may not be a contributing factor in crashinvolvement of drivers who are exposed to glare, an earlier study, by Singh and Perel [10] on glare perception of drivers based 

on the OSD opinion survey data, shows that the driver’s perception of glare depends on both age and sex. Configural Frequency 

Analysis (CFA) is used to explore the crash data subspaces in which these factors have significant influence. The results are 

presented in Table 4, where the highlighted groups indicate the corresponding sex versus age groups for which the observed 

frequencies are significantly higher than the expected frequencies. These differences are significant at 99 percent level of 

confidence (after the a priori determined confidence level 95 percent is adjusted using Bonferroni adjustment [11]). These are 

the groups for which more-than-expected drivers are observed to have been involved in crashes. 

Table 4. Observed and expected frequencies for data segments based on driver age and sex 

VISION 

OBSTRUCTION SEX AGE GROUP OBSERVED EXPECTED OBSRVD -EXPCTD Z-VALUE 

NO 

MALE 

Below 19 28793 26962 1831 11.15* 

19 - 24 32770 28229 4541 27.03* 

25 - 34 44536 42139 2397 11.67* 

35 - 44 40495 39415 1080 5.44* 

45 - 54 29515 32729 -3214 17.77 

55 - 64 19830 19542 288 2.06* 

65 - 74 10348 12746 -2398 21.24 

Above 74 9297 10652 -1354 13.12 

FEMALE 

Below 19 22841 20846 1995 13.81* 

19 - 24 26592 23747 2845 18.46* 

25 - 34 33012 31258 1754 9.92* 

35 - 44 31290 32136 -846 4.72 

45 - 54 20003 23093 -3090 20.34 

55 - 64 8880 11674 -2794 25.86 

65 - 74 5324 6225 -901 11.42 

Above 74 5532 7665 -2133 24.37 

GLARE 

MALE 

Below 19 21931 23762 -1831 11.88 

19 - 24 20337 24878 -4541 28.79 

25 - 34 34742 37138 -2396 12.43 

35 - 44 33657 34737 -1080 5.79 

45 - 54 32059 28845 3214 18.93* 

55 - 64 16935 17222 -287 2.19 

65 - 74 13630 11233 2397 22.62* 

Above 74 10742 9388 1354 13.98* 

FEMALE 

Below 19 16377 18372 -1995 14.72 

19 - 24 18084 20929 -2845 19.67 

25 - 34 25794 27548 -1754 10.57 

35 - 44 29168 28322 846 5.03* 

45 - 54 23443 20352 3091 21.66* 

55 - 64 13083 10289 2794 27.55* 

65 - 74 6387 5486 901 12.16* 

Above 74 8889 6756 2133 25.96* 

* significant at 99 percent confidence level 

Data Source: GES 2000-2003 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Specifically, the results show that more-than-expected male drivers of age below 45 years and female drivers below 35 years 

were involved in crashes due to factors other than glare. In contrast, older drivers in both sexes (males 45 and older and females 

35 and older) were observed in crashes significantly more than expected when exposed to glare. 

Based on these results, one may infer that male drivers 45 and older and female drivers 35 and older are more likely to be 

affected by glare. This finding is in agreement with the findings by McGwin et al. [2] based on experiments conducted to study 

the effect of glare on older drivers. 

8. Summary and Conclusions 

The results of this study based on the GES data indicate that glare from headlamps as well as sunlight is a contributing factor in 

crashes. These crashes show particular patterns with respect to driver, vehicle, and roadway. For example, in general, older 

drivers are more likely to get involved in crashes if glare obstructs their vision. Similarly, most of such drivers end up striking 

other vehicles, especially if the trafficway is not physically divided. Roadway profile plays a significant role in the occurrence 

of a glare-related crash, as does the number of lanes. The classifier based on driver age, crash hour, travel speed, and number of 

lanes could correctly classify 80 percent of the glare-related crashes. This shows that these variables have the potential to 

describe a glare-related crash, which in turn suggests how they may jointly contribute to the occurrence of a crash in a glarecaused driving scenario. However, the list of variables considered in this study is not exhaustive, and more research is required. 

Obviously, the intensity of light from headlamps can somehow be controlled, but not from the sun. So far as crash avoidance is 

concerned, when looking at the overall phenomenon and the distribution of crashes over a period of 24 hours in a day, it 

becomes clear that future efforts concerning glare should include both headlamps and sunlight. What is needed is a common 

approach to both headlamp and sunlight glare and develop counter measures to reduce glare before it strikes drivers’ eyes. 

9. References 

[1] Wells, Kristina (2004), “Sun's glare a pain in the eye”, Times Herald-Record November 08, 2004. 

[2] McGwin, Geraldn Jr., Chapman,Victoria and Owsley, Cynthia (2000), “Visual risk factors for driving difficulty among 

older drivers,” Accident Analysis & Prevention: Vol. 32: 735-744. 

[3] Theeuwes, Jan, Alferdinck, Johan W. A. M., and Perel, Michael (2002), “Relation between Glare and Driving 

Performance,” Human Factors: Vol. 44: 95-107. 

[4] Theeuwes, Jan and Alferdinck, Johan W. A. M. (1996), “The relation between discomfort glare and driving behavior.” 

Report No. DOT HS 808 452, U.S. Department of Transportation, National Highway Traffic Safety Administration, 

Washington, DC, pp. 1–64. 

[5] Bullough, John D. (2000), "Modeling Peripheral visibility under headlamp illumination," Transportation Research Board 

16th Biennial Symposium on Visibility and Simulation, Iowa City, IA. 

[6] Akashi, Yukio, Derlofske, John Van, Bullough, John D., and Chen, Jie (2005), “The effect of glare on visual performance 

under reduced headlamp illumination.” Society of Automotive Engineers 2005 World Congress, Detroit, MI, April 11-14. 

In Lighting Technology and Human Factors (pp. 235-242), SP-1932. Warrendale, PA: Society of Automotive Engineers. 

[7] National Highway Traffic Safety Administration, General Estimates System, 2000-2003. 

[8] Bureau of Transportation Statistics, Omnibus Survey Data, January to May 2002. 

[9] Duda, Richard O., Hart, Peter E., and Stork, David G. (2001), Pattern classification, John Wiley & Sons, New York. 

[10] Singh, Santokh and Perel, Mike, (2004), “Drivers’ Perceptions of Headlight Glare from Oncoming and Following 

Vehicles,” Report No. DOT HS 809 669, National Highway Traffic Safety Administration, Washington, DC. 

[11] Von Eye, Alexander (1990), Introduction to Configural Frequency Analysis, Cambridge University Press, New York.