Evaluation of the Impact of Spectral

Power Distribution on Driver Performance

PUBLICATION NO. FHWA-HRT-15-047 AUGUST 2015

Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296
FOREWORD

The Federal Highway Administration’s Office of Safety Research and Development focuses on 

conducting research that promotes a safe driving environment while offering practical 

considerations to address the needs of practitioners. Roadway lighting offers significant safety 

benefits but also represents a substantial share of the operating budgets of agencies tasked with 

maintaining the lighting infrastructure. Therefore, there is a need to optimize the safety 

implications and budgetary considerations.

This report provides the details and results of a comprehensive investigation of the impact of 

light-source spectrum on driver visual performance. In a series of human factors experiments, the 

effect of overhead lighting and headlamp spectral power distribution was evaluated with respect 

to driver detection and recognition of large and small objects. The report also discusses the 

spectral interaction of headlamp and roadway lighting on the detection of pedestrians, including 

an evaluation of enhanced pedestrian detection through a momentary peripheral illumination 

mechanism of the vehicle headlamps.

Monique R. Evans

Director, Office of Safety Research and 

Development

Notice

This document is disseminated under the sponsorship of the U.S. Department of Transportation 

in the interest of information exchange. The U.S. Government assumes no liability for the use of 

the information contained in this document. This report does not constitute a standard, 

specification, or regulation.

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear in this report only because they are considered essential to the 

objective of the document.

Quality Assurance Statement

The Federal Highway Administration (FHWA) provides high-quality information to serve 

Government, industry, and the public in a manner that promotes public understanding. Standards 

and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its 

information. FHWA periodically reviews quality issues and adjusts its programs and processes to 

ensure continuous quality improvement.
TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-15-047

2. Government Accession 

No.

3. Recipient’s Catalog No.

4. Title and Subtitle

Evaluation of the Impact of Spectral Power 

Distribution on Driver Performance

5. Report Date

August 2015

6. Performing Organization Code: 

7. Author(s)

Ronald B. Gibbons, Jason Meyer, Travis Terry, 

Rajaram Bhagavathula, Alan Lewis, Michael 

Flanagan, Caroline Connell

8. Performing Organization Report No. 

9. Performing Organization Name and Address

Virginia Tech Transportation Institute

3500 Transportation Research Plaza (0536)

Blacksburg, VA 24061

10. Work Unit No.

11. Contract or Grant No.

DTFH61-10-C-00032

12. Sponsoring Agency Name and Address

Office of Safety Research and Development

Federal Highway Administration

6300 Georgetown Pike

McLean, VA 22101-2296

13. Type of Report and Period Covered

09/24/2010–01/31/2015

14. Sponsoring Agency Code

15. Supplementary Notes

The Contracting Officer’s Representatives were Clayton Chen and Abdul Zineddin, Office of 

Safety Research and Development.

16. Abstract

This project is a complete investigation of the impact of light-source spectrum on driver visual 

performance. In a series of human factors experiments, the effect of overhead lighting and 

headlamp spectral power distribution was evaluated with respect to driver detection and 

recognition of large and small objects and pedestrians. The potential for applying mesopic 

multiplying factors to roadway lighting was also evaluated, as was a momentary peripheral 

illuminator system’s effects on driver visual performance and eye-glance behavior. The results 

indicate that, although the momentary peripheral illuminator improved one measure of pedestrian 

detection, it was also a distraction to drivers. The results also indicate that at higher speeds, 

neither light-source spectrum nor mesopic multiplying factors apply, but mesopic multiplying 

factors are applicable to lighting design for lower-speed roadways and other nondriving 

environments.

17. Key Words

Light sources, Spectral effects roadway 

lighting, Roadway safety, Lighting 

levels, Lighting and safety

18. Distribution Statement

No restrictions. This document is available through 

the National Technical Information Service; 

Springfield, VA 22161.

http://www.ntis.gov/about/contact.aspx

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of 

Pages

236

22. 

Price

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized
ii
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TABLE OF CONTENTS

EXECUTIVE SUMMARY .......................................................................................................... 1

CHAPTER 1. INTRODUCTION................................................................................................ 5

PROJECT OBJECTIVES ....................................................................................................... 5

CHAPTER 2. BACKGROUND................................................................................................... 7

IMPACT OF ROADWAY LIGHTING ................................................................................. 7

Color ..................................................................................................................................... 10

MESOPIC EFFECTS AT NIGHT FOR DRIVERS ........................................................... 11

Models of Mesopic Vision.................................................................................................... 13

LIGHTING AND THE DRIVING ENVIRONMENT........................................................ 17

Visual Acuity ........................................................................................................................ 17

Age Effects on Vision and Driving....................................................................................... 18

Visibility and Contrast .......................................................................................................... 18

Size (Visual Angle)............................................................................................................... 20

Contrast Polarity Factor ........................................................................................................ 21

Exposure Time ...................................................................................................................... 21

Age Factor............................................................................................................................. 21

Transient Adaptation............................................................................................................. 22

Eccentricity ........................................................................................................................... 22

MOMENTARY PERIPHERAL ILLUMINATORS........................................................... 22

Problems Detecting Pedestrians at Night.............................................................................. 23

Headlamp Technologies........................................................................................................ 24

SPECIFICATION DEVELOPMENT FOR AN MPI SYSTEM........................................ 27

Conceptual Design of an MPI System.................................................................................. 27

Pedestrian-Detection Systems............................................................................................... 28

CHAPTER 3. EXPERIMENTAL APPROACH...................................................................... 31

MATERIALS AND FACILITIES ........................................................................................ 32

Virginia Smart Road ............................................................................................................. 32

Overhead-Lighting Systems.................................................................................................. 33

Vehicles and Headlamp Systems.......................................................................................... 35

Visual Objects....................................................................................................................... 37

DEPENDENT VARIABLES ................................................................................................. 40

Detection Distance ................................................................................................................ 40

Color-Recognition Distance.................................................................................................. 41

Orientation-Recognition Distance ........................................................................................ 42

PROCEDURES....................................................................................................................... 42

Participant Selection ............................................................................................................. 42

Participant Screening ............................................................................................................ 43

Experimental Runs................................................................................................................ 44

PHOTOMETRIC SYSTEMS................................................................................................ 44

Luminance-Based Systems................................................................................................... 44

Illuminance-Based Systems.................................................................................................. 45
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DATA REDUCTION PROCEDURES ................................................................................. 46

Detection/Recognition Distances.......................................................................................... 46

Luminance Data .................................................................................................................... 46

CHAPTER 4. SCOPING EXPERIMENT................................................................................ 47

INTRODUCTION .................................................................................................................. 47

Research Objectives.............................................................................................................. 47

EXPERIMENTAL DESIGN ................................................................................................. 47

Independent Variables........................................................................................................... 48

Dependent Variables............................................................................................................. 51

METHODS.............................................................................................................................. 51

Facilities and Equipment....................................................................................................... 51

Participants............................................................................................................................ 51

Procedure .............................................................................................................................. 52

Data Analysis........................................................................................................................ 52

RESULTS ................................................................................................................................ 53

Overhead-Lighting Type and Level...................................................................................... 53

No Overhead Lighting .......................................................................................................... 65

Luminance Analysis.............................................................................................................. 69

DISCUSSION.......................................................................................................................... 73

CONCLUSIONS ..................................................................................................................... 74

Design of Further Experiments............................................................................................. 76

LIMITATIONS....................................................................................................................... 76

CHAPTER 5. MPI SYSTEM PERFORMANCE EXPERIMENT........................................ 77

INTRODUCTION .................................................................................................................. 77

Research Objectives.............................................................................................................. 77

EXPERIMENTAL DESIGN ................................................................................................. 78

Independent Variables........................................................................................................... 78

Dependent Variables............................................................................................................. 81

Facilities and Equipment....................................................................................................... 83

Participants............................................................................................................................ 83

Procedure .............................................................................................................................. 84

Data Analysis........................................................................................................................ 84

RESULTS ................................................................................................................................ 85

Detection and Color-Recognition Distances......................................................................... 85

Detection Rate by Pedestrian Illuminated by MPI System................................................... 96

Mean Fixation Duration by MPI System Configuration....................................................... 97

DISCUSSION.......................................................................................................................... 98

CONCLUSIONS ..................................................................................................................... 99

FUTURE RESEARCH........................................................................................................... 99

CHAPTER 6. OVERHEAD-LIGHTING LEVEL EXPERIMENT.................................... 101

INTRODUCTION ................................................................................................................ 101

Research Objectives............................................................................................................ 101

EXPERIMENTAL DESIGN ............................................................................................... 102

Independent Variables......................................................................................................... 102

Dependent Variables........................................................................................................... 106
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METHODS............................................................................................................................ 107

Participants.......................................................................................................................... 107

Procedure ............................................................................................................................ 107

Data Analysis...................................................................................................................... 108

RESULTS .............................................................................................................................. 108

Lighting Characteristics...................................................................................................... 108

Detection Distance Versus Lighting Conditions................................................................. 109

Orientation-Recognition Distance ...................................................................................... 120

Luminance........................................................................................................................... 123

DISCUSSION........................................................................................................................ 128

Zones of Visibility .............................................................................................................. 128

Stopping Sight Distance...................................................................................................... 129

Weber Contrast and Detection Distance ............................................................................. 131

Visibility Level ................................................................................................................... 139

CONCLUSIONS ................................................................................................................... 142

CHAPTER 7. MESOPIC MODELING EXPERIMENT...................................................... 145

INTRODUCTION ................................................................................................................ 145

Research Objectives............................................................................................................ 145

EXPERIMENTAL DESIGN ............................................................................................... 146

Independent Variables......................................................................................................... 146

Dependent Variables........................................................................................................... 148

METHODS............................................................................................................................ 149

Facilities and Equipment..................................................................................................... 149

Participants.......................................................................................................................... 150

Procedure ............................................................................................................................ 151

Data Analysis...................................................................................................................... 151

RESULTS .............................................................................................................................. 152

Static ................................................................................................................................... 152

Dynamic.............................................................................................................................. 158

DISCUSSION........................................................................................................................ 163

Static Experiment................................................................................................................ 164

Dynamic Experiment .......................................................................................................... 168

CONCLUSIONS ................................................................................................................... 173

CHAPTER 8. FINAL PERFORMANCE EXPERIMENT................................................... 175

INTRODUCTION ................................................................................................................ 175

Research Objectives............................................................................................................ 176

EXPERIMENTAL DESIGN ............................................................................................... 176

Independent Variables......................................................................................................... 177

Covariate ............................................................................................................................. 179

Dependent Variables........................................................................................................... 179

Facilities and Equipment..................................................................................................... 180

Participants.......................................................................................................................... 180

Procedure ............................................................................................................................ 180

Data Analysis...................................................................................................................... 180
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RESULTS .............................................................................................................................. 181

Main Effects........................................................................................................................ 181

Two-Way Interactions......................................................................................................... 188

Contrast and Detection Distance......................................................................................... 195

DISCUSSION........................................................................................................................ 199

MPI......................................................................................................................................... 199

Age...................................................................................................................................... 200

Color ................................................................................................................................... 201

Adaptation Luminance........................................................................................................ 201

Offset................................................................................................................................... 202

Speed................................................................................................................................... 202

CHAPTER 9. SUMMARY....................................................................................................... 205

CHAPTER 10. CONCLUSIONS AND FUTURE CONSIDERATIONS............................ 207

CONCLUSIONS ................................................................................................................... 207

Effect of Overhead-Lighting SPD on Driver Visual Performance ..................................... 207

Combined Effect of Overhead Lighting and Headlamps on Driver Visual 

Performance .................................................................................................................. 207

Applicability of Mesopic Models to the Driving Environment .......................................... 208

Impact of MPI System on Driver Visual Performance ....................................................... 208

Additional Findings ............................................................................................................ 208

CONSIDERATIONS............................................................................................................ 209

FUTURE RESEARCH......................................................................................................... 209

CHAPTER 11. APPLICATION TO PRACTICE ................................................................. 211

REFERENCES.......................................................................................................................... 213
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LIST OF FIGURES

Figure 1. Graph. Spectral power distribution of HPS light sources................................................ 8

Figure 2. Graph. Spectrum power distribution of three LED light sources.................................... 9

Figure 3. Diagram. Change in light source appearance with CCT............................................... 10

Figure 4. Diagram. Ranges of visual sensitivity........................................................................... 12

Figure 5. Equation. CIE formula for calculating mesopic spectral sensitivity ............................. 16

Figure 6. Equation. Luminance difference threshold.................................................................... 19

Figure 7. Equation. VL formula.................................................................................................... 19

Figure 8. Equation. Formula for calculating Weber contrast........................................................ 20

Figure 9. Diagram. Variables for calculating visual angle ........................................................... 20

Figure 10. Equation. Formula for visual angle in degrees............................................................ 20

Figure 11. Equation. Calculation for negative contrast threshold ................................................ 21

Figure 12. Equation. Calculation of the contrast polarity factor................................................... 21

Figure 13. Equation. Age factor calculation for younger age group (23 to 64 years) .................. 22

Figure 14. Equation. Age factor calculation for older age group (64 to 75 years)....................... 22

Figure 15. Graph. Headlamp beam pattern................................................................................... 24

Figure 16. Chart. Comparison between halogen and HID headlamps.......................................... 25

Figure 17. Graph. Comparison of reaction times between halogen and HID headlamps............. 25

Figure 18. Graph. Reaction times with different HID headlamp types........................................ 26

Figure 19. Diagram. MPI headlamp illuminating pedestrian by flashing on and swiveling ........ 27

Figure 20. Diagram. Conceptual design of an MPI system.......................................................... 28

Figure 21. Diagram. General structure of a pedestrian-detection system..................................... 29

Figure 22. Flowchart. Relationship among experiments performed............................................. 31

Figure 23. Diagram. Smart Road test track................................................................................... 33

Figure 24. Graph. Mesopic modeling experiment—SPDs of overhead-lighting types used 

in the study.............................................................................................................................. 34

Figure 25. Photo. Test vehicle with headlamps and interchangeable gels.................................... 35

Figure 26. Graph. SPD of low-power, color-filtered headlamps.................................................. 36

Figure 27. Graph. SPD of high-power, color-filtered headlamps................................................. 36

Figure 28. Graph. SPD of low-power, neutral-density-filtered headlamps.................................. 37

Figure 29. Photo. Clothing colors on confederate pedestrian....................................................... 38

Figure 30. Graph. Pedestrian clothing reflectance........................................................................ 38

Figure 31. Photo. Targets.............................................................................................................. 39

Figure 32. Graph. Target reflectance relative to diffuse white ..................................................... 40

Figure 33. Diagram. Measuring detection and color-recognition distances................................. 41

Figure 34. Screenshot. Luminance analysis software................................................................... 45

Figure 35. Photo. Pedestrian on roadway ..................................................................................... 50

Figure 36. Photo. Target on roadway............................................................................................ 51

Figure 37. Chart. Scoping experiment—mean pedestrian-detection distance by overheadlighting type and level, with SNK results............................................................................... 54

Figure 38. Chart. Scoping experiment—mean pedestrian color-recognition distance by 

overhead-lighting type and level, with SNK results............................................................... 55

Figure 39. Chart. Scoping experiment—mean pedestrian-detection distance by clothing 

color ........................................................................................................................................ 56
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Figure 40. Chart. Scoping experiment—mean pedestrian clothing color-recognition 

distance by clothing color....................................................................................................... 57

Figure 41. Chart. Scoping experiment—mean off-axis pedestrian-detection distance by 

overhead-lighting type and level............................................................................................. 58

Figure 42. Chart. Scoping experiment—mean off-axis pedestrian color-recognition 

distance by overhead-lighting type and pedestrian position................................................... 59

Figure 43. Chart. Scoping experiment—pedestrian-detection distance by position and 

clothing color .......................................................................................................................... 60

Figure 44. Chart. Scoping experiment—pedestrian color-recognition distance by position 

and clothing color ................................................................................................................... 60

Figure 45. Chart. Scoping experiment—mean pedestrian-detection distance by pedestrian 

position, speed, and overhead-lighting type ........................................................................... 61

Figure 46. Chart. Scoping experiment—detection distances by overhead-lighting type and 

level for constant-contrast pedestrians.................................................................................... 62

Figure 47. Chart. Scoping experiment—mean target-detection distance by target color............. 64

Figure 48. Chart. Scoping experiment—mean target color-recognition distance by target 

color ........................................................................................................................................ 64

Figure 49. Chart. Scoping experiment—target color-recognition distance by target color 

and overhead-lighting type ..................................................................................................... 65

Figure 50. Chart. Scoping experiment—mean pedestrian-detection distance by clothing 

color for no overhead lighting................................................................................................. 66

Figure 51. Chart. Scoping experiment—mean pedestrian color-recognition distance by 

clothing color for no overhead lighting .................................................................................. 67

Figure 52. Chart. Scoping experiment—mean target-detection distance by target color for 

no overhead lighting ............................................................................................................... 68

Figure 53. Chart. Scoping experiment—mean target color-recognition distance by target 

color for no overhead lighting................................................................................................. 69

Figure 54. Scatter Plot. Scoping experiment—pedestrian-detection distance, luminance, 

and clothing color ................................................................................................................... 70

Figure 55. Chart. Scoping experiment—pedestrian mean Weber contrast at time of 

detection by overhead-lighting color and clothing color........................................................ 71

Figure 56. Photo. Uniformity of HPS and LED lighting .............................................................. 72

Figure 57. Scatter Plot. Scoping experiment—target-detection distance and luminance............. 73

Figure 58. Photo. MPI system with straight (left) and swiveling (right) headlamp ..................... 79

Figure 59. Diagram. MPI system performance experiment—MPI system configurations .......... 80

Figure 60. Diagram. MPI system performance experiment—pedestrian opposite from 

area illuminated by MPI system ............................................................................................. 81

Figure 61. Photo. MPI system performance experiment—eye-tracker goggles........................... 82

Figure 62. Screenshot. MPI system performance experiment—pupil location (left) and 

dot representing gaze direction overlaid on scene (right)....................................................... 83

Figure 63. Chart. MPI system performance experiment—pedestrian-detection and colorrecognition distances versus headlamp color.......................................................................... 87

Figure 64. Chart. MPI system performance experiment—pedestrian-detection and colorrecognition distances versus headlamp color and overhead lighting...................................... 87

Figure 65. Chart. MPI system performance experiment—pedestrian-detection and colorrecognition distance versus MPI system configuration .......................................................... 88
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Figure 66. Chart. MPI system performance experiment—pedestrian-detection and colorrecognition distances versus MPI system configuration and overhead lighting..................... 89

Figure 67. Chart. MPI system performance experiment—pedestrian-detection and colorrecognition distances versus MPI system configuration and headlamp color........................ 90

Figure 68. Chart. MPI system performance experiment—pedestrian-detection and colorrecognition distances versus MPI system configuration and pedestrian on left or right ........ 91

Figure 69. Chart. MPI system performance experiment—pedestrian-detection and colorrecognition distances versus pedestrian clothing color........................................................... 92

Figure 70. Chart. MPI system performance experiment—pedestrian-detection and colorrecognition distances versus pedestrian clothing color and overhead lighting....................... 93

Figure 71. Chart. MPI system performance experiment—pedestrian-detection and colorrecognition distances versus pedestrian on left or right.......................................................... 94

Figure 72. Chart. MPI system performance experiment—pedestrian-detection and colorrecognition distances versus pedestrian in overhead lighting................................................. 95

Figure 73. Photo. MPI system performance experiment—visual clutter on the Smart Road....... 96

Figure 74. Chart. MPI system performance experiment—percentage of detections of 

pedestrian versus MPI system configuration and whether the pedestrian was 

illuminated by the MPI system ............................................................................................... 97

Figure 75. Chart. MPI system performance experiment—mean fixation duration versus 

MPI system configuration....................................................................................................... 98

Figure 76. Diagram. Overhead-lighting level experiment—target I (low VI) placement .......... 104

Figure 77. Diagram. Overhead-lighting level experiment—target II (high VI) placement........ 105

Figure 78. Graph. Overhead-lighting level experiment—VI on objects by overheadlighting level ......................................................................................................................... 106

Figure 79. Graph. Overhead-lighting level experiment—VI at target, vehicle with 

headlamps on at between 91 and 8 m (300 and 25 ft) from target, luminaires from 0 to 

100 percent............................................................................................................................ 109

Figure 80. Chart. Overhead-lighting level experiment—SNK groupings for pedestriandetection distance with headlamps off by overhead-lighting level....................................... 111

Figure 81. Chart. Overhead-lighting level experiment—SNK groupings for pedestriandetection distance with headlamps on by overhead-lighting level ....................................... 112

Figure 82. Chart. Overhead-lighting level experiment—SNK groupings for target I 

detection distance with headlamps off by overhead-lighting level....................................... 113

Figure 83. Chart. Overhead-lighting level experiment—SNK groupings for target I 

detection distance with headlamps on by overhead-lighting level ....................................... 114

Figure 84. Chart. Overhead-lighting level experiment—SNK groupings for target II 

detection distance with headlamps off by overhead-lighting level....................................... 115

Figure 85. Chart. Overhead-lighting level experiment—SNK groupings for target II 

detection distance with headlamps on by overhead-lighting level ....................................... 116

Figure 86. Chart. Overhead-lighting level experiment—mean detection distance for all 

objects by headlamp on/off................................................................................................... 117

Figure 87. Chart. Overhead-lighting level experiment—mean detection distance for all 

objects by participant age and lighting conditions combined............................................... 118

Figure 88. Chart. Overhead-lighting level experiment—mean detection distances for 

pedestrians by overhead-lighting level ................................................................................. 119
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Figure 89. Chart. Overhead-lighting level experiment—mean detection distances for 

targets by overhead-lighting level......................................................................................... 120

Figure 90. Chart. Overhead-lighting level experiment—mean recognition distance of 

pedestrian by headlamps on and off and overhead-lighting level......................................... 121

Figure 91. Chart. Overhead-lighting level experiment—mean recognition distance of 

target I by headlamps on and off and overhead-lighting level.............................................. 122

Figure 92. Chart. Overhead-lighting level experiment—mean recognition distance of 

target II by headlamps on and off and overhead-lighting level ............................................ 123

Figure 93. Graph. Overhead-lighting level experiment—pedestrian log luminance by 

overhead-lighting level, headlamp condition, and distance.................................................. 124

Figure 94. Graph. Overhead-lighting level experiment—target I luminance by overheadlighting level, headlamp condition, and distance.................................................................. 126

Figure 95. Graph. Overhead-lighting level experiment—target II luminance by overheadlighting level, headlamp condition, and distance.................................................................. 127

Figure 96. Graph. Overhead-lighting level experiment—pedestrian contrast and detection 

distance at 100-percent overhead lighting ............................................................................ 131

Figure 97. Graph. Overhead-lighting level experiment—pedestrian contrast and detection 

distances at 50-percent overhead lighting............................................................................. 133

Figure 98. Graph. Overhead-lighting level experiment—pedestrian contrast and detection 

distances at 10-percent overhead lighting............................................................................. 133

Figure 99. Image Table. Overhead-lighting level experiment—images of target I at 

100-percent overhead lighting for from 15.2 to 123 m (50 to 400 ft) to the target .............. 134

Figure 100. Graph. Overhead-lighting level experiment—target I contrast and detection 

distance at 100-percent overhead lighting ............................................................................ 135

Figure 101. Image Table. Overhead-lighting level experiment—images of target I at 

40-percent overhead lighting for from 15.2 to 123 m (50 to 400 ft) to the target ................ 136

Figure 102. Graph. Overhead-lighting level experiment—target I contrast and detection 

distances versus distance from target at 40-percent overhead lighting................................. 137

Figure 103. Graph. Overhead-lighting level experiment—target II contrast and detection 

distances versus distance from target at 100-percent overhead lighting............................... 138

Figure 104. Graph. Overhead-lighting level experiment—target II contrast and detection 

distances versus distance from target at 40-percent overhead lighting................................. 139

Figure 105. Graph. Overhead-lighting level experiment—pedestrian VL with headlamps 

on and with and without overhead lighting........................................................................... 140

Figure 106. Graph. Overhead-lighting level experiment—target I VL with headlamps on 

and with and without overhead lighting................................................................................ 141

Figure 107. Graph. Overhead-lighting level experiment—target II VL with headlamps on 

and with and without overhead lighting................................................................................ 142

Figure 108. Diagram. Arrangement of target lights for static experiments on the Smart 

Road ...................................................................................................................................... 148

Figure 109. Photo. Mesopic modeling experiment—target, target light, and background......... 150

Figure 110. Graph. Mesopic modeling experiment (static portion)—threshold contrast by 

overhead-lighting type .......................................................................................................... 153

Figure 111. Graph. Mesopic modeling experiment (static portion)—threshold contrast by 

adaptation luminance ............................................................................................................ 154
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Figure 112. Graph. Mesopic modeling experiment (static portion)—threshold contrast by 

eccentricity............................................................................................................................ 155

Figure 113. Equation. Formula for calculating mesopic output ................................................. 155

Figure 114. Equation. Formula for calculating mesopic threshold contrast............................... 156

Figure 115. Graph. Mesopic modeling experiment (static portion)—threshold contrasts 

calculated with photopic and mesopic luminances for the three lighting types and 

three eccentricities ................................................................................................................ 157

Figure 116. Graph. Mesopic modeling experiment (dynamic portion)—detection distance 

by adaptation luminance ....................................................................................................... 160

Figure 117. Graph. Mesopic modeling experiment (dynamic portion)—effect of 

overhead-lighting type and adaptation luminance on detection distance ............................. 161

Figure 118. Chart. Mesopic modeling experiment (dynamic portion)—effect of 

eccentricity and speed on detection distance ........................................................................ 161

Figure 119. Graph. Mesopic modeling experiment (dynamic portion)—effect of 

adaptation luminance and eccentricity on detection distance ............................................... 162

Figure 120. Graph. Mesopic modeling experiment (dynamic portion)—effect of 

overhead-lighting type, adaptation luminance, and eccentricity on detection distance........ 163

Figure 121. Graph. Mesopic modeling experiment (static portion)—threshold contrasts of 

targets under different overhead-lighting sources at different adaptation luminances......... 164

Figure 122. Graph. Mesopic modeling experiment (static portion)—mesopic contrast 

thresholds as predicted by the CIE Mesopic Spectral Sensitivity Model ............................. 166

Figure 123. Equation. Formula for calculating the mesopic luminance difference between 

2,100-K HPS and 6,000-K LED lighting.............................................................................. 167

Figure 124. Graph. Mesopic modeling experiment (static portion)—calculated versus 

predicted deltas (difference in mesopic luminance expressed as a percentage) between 

HPS lighting and each of the LED lighting types................................................................. 168

Figure 125. Graph. Mesopic modeling experiment (dynamic portion)—effect of 

eccentricity and speed on detection distance ........................................................................ 170

Figure 126. Graph. Mesopic modeling experiment—peripheral visual performance in 

both static and dynamic experiments at different adaptation luminances ............................ 171

Figure 127. Graph. Mesopic modeling experiment—peripheral visual performance of 

overhead lighting types at different adaptation luminances ................................................. 172

Figure 128. Diagram. Final performance experiment—pedestrian positions and offsets 

from the roadway .................................................................................................................. 178

Figure 129. Graph. Final performance experiment—detection distance and colorrecognition distance by adaptation luminance...................................................................... 185

Figure 130. Chart. Final performance experiment—detection and color-recognition 

distances by pedestrian clothing color.................................................................................. 187

Figure 131. Chart. Final performance experiment—detection distance and colorrecognition distance versus offset for all pedestrian clothing colors.................................... 188

Figure 132. Chart. Final performance experiment—detection distance by adaptation 

luminance and offset for all clothing colors ......................................................................... 189

Figure 133. Chart. Final performance experiment—detection distance by adaptation 

luminance and offset for gray-clothed pedestrians only....................................................... 189

Figure 134. Chart. Final performance experiment—missed detections by age and offset......... 190
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Figure 135. Chart. Final performance experiment—detection distance by offset and 

overhead-lighting type for gray-clothed pedestrians ............................................................ 191

Figure 136. Chart. Final performance experiment—missed detections by MPI system 

configuration and offset ........................................................................................................ 192

Figure 137. Graph. Final performance experiment—detection distance by speed and 

offset for all clothing colors.................................................................................................. 193

Figure 138. Graph. Final performance experiment—detection distance by speed and 

offset for gray only................................................................................................................ 194

Figure 139. Chart. Final performance experiment—detection distance by adaptation 

luminance and overhead-lighting type with the MPI system off.......................................... 195

Figure 140. Graph. Final performance experiment—detection distance and Weber 

contrast by adaptation luminance.......................................................................................... 197

Figure 141. Graph. Detection distance and Weber contrast by adaptation luminance for 

MPI system on and off.......................................................................................................... 198

Figure 142. Graph. Final performance experiment—detection distance and Weber 

contrast by offset................................................................................................................... 199

Figure 143. Graph. Final performance experiment—VL and Weber contrast by adaptation 

luminance.............................................................................................................................. 201
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LIST OF TABLES

Table 1. Light type, color, and problems (from Shaflik, 1997).....................................................11

Table 2. Overhead-lighting system characteristics........................................................................34

Table 3. Characteristics of headlamp filters ..................................................................................36

Table 4. Integral of pedestrian clothing reflectance.......................................................................39

Table 5. Integral of target reflectance ............................................................................................40

Table 6. Scoping experiment independent variables and values ...................................................48

Table 7. Scoping experiment dependent variables and measurement method ..............................48

Table 8. Scoping experiment participant characteristics ...............................................................52

Table 9. Scoping experiment on-axis pedestrian significant results summary for runs in 

overhead-lighting type and level..............................................................................................53

Table 10. Scoping experiment constant-contrast pedestrian results summary ..............................61

Table 11. Scoping experiment target results summary for overhead lighting ...............................63

Table 12. Scoping experiment pedestrian results summary for no overhead lighting...................66

Table 13. Scoping experiment target results summary for no overhead lighting ..........................67

Table 14. Scoping experiment best and worst colors for target and pedestrian detection 

and color recognition, with and without overhead lighting .....................................................75

Table 15. MPI system performance experiment independent variables and values......................78

Table 16. MPI system performance experiment dependent variables and measurement 

method......................................................................................................................................78

Table 17. MPI system performance experiment participant characteristics..................................84

Table 18. MPI system performance experiment detection distance results per independent 

variable.....................................................................................................................................85

Table 19. MPI system performance experiment color-recognition distance results per 

independent variable ................................................................................................................86

Table 20. Overhead-lighting level experiment independent variables ........................................102

Table 21. Overhead-lighting level experiment dependent variables ...........................................102

Table 22. Overhead-lighting level experiment—VI of targets and pedestrian with 

luminaires and no headlamps.................................................................................................106

Table 23. Overhead-lighting level experiment participant characteristics ..................................107

Table 24. Overhead-lighting level experiment—significant effects on detection distance 

of targets and pedestrian ........................................................................................................110

Table 25. Overhead-lighting level experiment—zones of visibility for pedestrian and 

targets.....................................................................................................................................129

Table 26. Overhead-lighting level experiment—object stopping sight distance and 

corresponding speed...............................................................................................................130

Table 27. Mesopic modeling experiment—independent variables and values............................146

Table 28. Mesopic modeling experiment—covariate and value/measurement method..............146

Table 29. Mesopic modeling experiment—dependent variables and measurement method.......146

Table 30. Mesopic modeling experiment—adaptation luminance by overhead-lighting 

level and pavement type.........................................................................................................147

Table 31. Mesopic modeling experiment participant characteristics...........................................151

Table 32. Mesopic modeling experiment (static portion)—independent variables and their 

effect on threshold contrast....................................................................................................152
xiv

Table 33. Mesopic modeling experiment (dynamic portion)—independent variables and 

their effect on threshold contrast............................................................................................159

Table 34. Final experiment—independent variables and values.................................................176

Table 35. Final experiment—covariate and measurement method .............................................176

Table 36. Final experiment—dependent variables and measurement method ............................176

Table 37. Final performance experiment—adaptation luminance per overhead lighting 

level and pavement type.........................................................................................................177

Table 38. Final performance experiment—VI for all pedestrian positions, overhead 

lighting levels, and visual angles...........................................................................................179

Table 39. Final performance experiment participant characteristics...........................................180

Table 40. Final performance experiment results..........................................................................181

Table 41. Final performance experiment significant main effects, gray only .............................184

Table 42. Final performance experiment results for Weber contrast and detection distance 

for gray pedestrians only........................................................................................................196
xv

ACRONYMS AND ABBREVIATIONS

ANCOVA Analysis of Covariance

ANOVA Analysis of Variance

BAT Brightness Acuity Tester

BSM Binocular Simultaneity Method

CCT Correlated Color Temperature

CIE Commission Internationale d’Eclairage

GPS Global Positioning System

HID High-Intensity Discharge

HPS High-Pressure Sodium

HSD Honest Significant Difference

IES Illuminating Engineering Society

K Kelvin

LED Light-Emitting Diode

MH Metal Halide

MOA Minutes of Arc

MOVE Mesopic Optimization of Visual Efficiency

MPI Momentary Peripheral Illumination

OD Optical Density

RLMMS Roadway Lighting Mobile Measurement System

S/P Scotopic Output/Photopic Output 

SAE Society for Automotive Engineering

SNK Student-Newman-Keuls

SPD Spectral Power Distribution

TOF Time of Flight

UFOV Useful Field of View

VI Vertical Illuminance

VL Visibility Level

VTTI Virginia Tech Transportation Institute

1

EXECUTIVE SUMMARY

Traditional roadway lighting uses high-pressure sodium (HPS) light sources, which provide high 

photometric efficacy. HPS light itself, however, is amber and does not render object color 

correctly. With the advent of light-emitting diode technology in roadway lighting, a new aspect 

of the light source is now being considered—its spectral power distribution (SPD). Broadspectrum sources, with significant spectral output across the entire visible spectrum, potentially 

provide additional benefits to the driver; these light sources can provide better color information 

and can activate all of the photoreceptors in the eye more efficiently. This project investigates 

these effects and considers the potential benefits of a broad-spectrum light source.

This project is a comprehensive review of the applicability of mesopic factors to roadwaylighting applications particularly for higher speed roadways. These factors are based on the 

transition in the human eye from cone sensitivity to rod sensitivity and represent the potential 

benefit of broad-spectrum sources over traditional sources. While the model to determine the 

impact of mesopic adaptation to visual performance is well established and verified, both in the 

laboratory and in some very carefully prescribed experiments, the real-world applicability of the 

model has remained in question. Determining the impact of mesopic lighting on high-speed 

roadways is the focus of this effort. 

In addition, a momentary peripheral illumination (MPI) system for highlighting pedestrians was 

developed and tested. The MPI system’s effect on visibility may have also been affected by the 

overhead lighting source’s SPD and level because pedestrians on the roadside might be detected 

in the periphery of the visual field, where mesopic effects occur. 

This project was developed as a stepwise approach to the problems noted above. The first 

two steps were to develop a scoping experiment that defined the nature of the effect of the SPD 

of overhead lighting on visibility and to provide guidance for development of the subsequent 

experiments. The primary outcomes from this scoping experiment were that both the type and 

level of overhead lighting significantly affected the detection and recognition of objects on the 

roadway. This was also evident for objects that were off of the roadway. One of the primary 

determinants for detection was the color of the pedestrian clothing and the targets in the 

roadway, thus indicating that color contrast is a significant component of object detection. The 

results also indicated that roadway lighting uniformity has an important role in object detection. 

The final aspect was that of headlamp color and intensity. In scenarios when overhead lighting 

was used, headlamp configuration did not affect visibility. 

These results drove the direction of the next two experiments. The first was an investigation of 

conditions when headlamps have an impact on object detection and when they do not. The 

second was the investigation of the applicability of the mesopic model to roadway lighting.

In addition to spectral experiments that were performed, an investigation of the applicability of 

an MPI system was conducted to determine whether a system could be developed to leverage the 

spectral aspects of the visual response. Although the scoping experiment showed a minimal 

spectral effect of headlamp color on detection distance, two headlamp colors were used to further 

explore this relationship or headlamp color and the MPI. A mock-up MPI system was created 

with servo-activated headlamps that either tracked the pedestrians as the vehicle approached or 
2

highlighted them for a short time during which the vehicle approached. The results of this 

experiment were that the MPI system resulted in both shorter detection distances and an increase 

in detection rate. Headlamp color did not seem to have a significant impact on detection. When 

the MPI system highlighted an area across from a pedestrian, participants’ detection rates and 

distances for that pedestrian were lower. This highlights the importance of careful design of a 

full-featured MPI system; participants’ behavior indicated that they expected it to work properly, 

so it must not produce false positives, which could distract drivers from actual roadside hazards. 

The next experiment was an investigation of the interaction of vehicle headlamps and overhead 

lighting on roadway-object detection. Small targets and a pedestrian were located in specific 

positions along the roadway that created high- and low-visibility conditions. The overhead 

lighting was then dimmed, and headlamps were turned off and on while participant drivers tried 

to detect the objects. Results indicated that the impact of the headlamps varied by object size. For 

most lighting levels, the overhead lighting was the dominant force driving object detection, but 

that was not the case when the overhead lighting was at the lowest levels. Headlamps were the 

driving factor for orientation-recognition distances—recognition of the direction the object was 

facing. The applicability of these results is critical for roadway lighting design. Headlamps 

dominate object recognition and also drive adaptation luminance. Therefore, the effect of the 

SPD of the roadway lighting may be overridden by the headlamps’ effect on adaptation level and 

the contribution of headlamp illumination to object luminance.

The other experiment resulting from the scoping experiment considered the mesopic model. Here 

both static and dynamic target-detection experiments allowed the research team to evaluate the 

mesopic model in the field. The static portion of the experiment was performed by determining 

the threshold contrast for small targets, and the dynamic portion examined target detection from 

a moving vehicle. The results indicate that overhead-lighting level significantly affected object 

detection; higher adaptation levels resulted in a lower threshold contrast. The results also showed 

that in the dynamic experiment, higher speeds typically resulted in longer detection distances. In 

terms of the mesopic model, for white overhead-lighting sources, the experimental results 

corresponded well to the model; however, for HPS sources, they did not. One of the important 

aspects of the project was the consideration of the off axis or object eccentric to the line of sight. 

An issue with the mesopic model could be that it does not include a term for eccentricity that 

accounts for different retinal sensitivities at different angles. The main conclusion was that, 

although the mesopic model predicted some of the results at lower lighting levels, it also had

limitations. 

The final experiment performed did not attempt to limit driver eye glances or fix eccentricities at 

detection. This experiment included an MPI system, overhead lighting, two speeds, and 

pedestrian detection in the periphery of the visual field. The results indicate that, for pedestrians 

close to the roadway, there was no impact of overhead lighting’s spectral distribution on 

detection distance. For those pedestrians, adaptation luminance was the most influential factor 

affecting visibility. For pedestrians farther from the roadway, spectral effects were more 

significant, but those results might not be applicable to roadway lighting design because objects 

that far away would have to be moving fairly quickly and on a collision path with the vehicle to 

become a hazard. The MPI performance results were similar to those of the initial MPI 

experiment. The MPI reduced detection distances and increased detection rates for objects in the 

periphery of the visual field. 
3

The results of this experiment show that in a natural driving environment at the speeds tested, 

there is limited applicability of the mesopic model to lighting design. It is likely that drivers scan 

the roadway and detect objects in the fovea, where mesopic effects are not seen. Headlamps 

might also cause a higher adaptation luminance than predicted from the road luminance, further 

limiting the applicability of the mesopic model to lighting design for nighttime driving.

In general, the conclusions from the project are that the spectral component of the light source 

affects driver visual performance but only in certain conditions. The adaptation luminance of the 

driver is by far the dominant component of driver visual performance. The speed of the vehicle

also affects the driver visual performance. In general, for high-speed roadways, it is 

recommended that spectral effects not be included in the design of the lighting systems. For 

lower speed roadways where the lighting system is predominantly for pedestrians, it is believed 

that spectral effects may still apply. 

5

CHAPTER 1. INTRODUCTION

Traditional roadway lighting uses high-pressure sodium (HPS) light sources, which provide high 

photometric efficacy. HPS light itself, however, is amber and does not render object color 

correctly. With the advent of light-emitting diode (LED) technology in roadway lighting, a new 

aspect of the light source is now being considered by roadway lighting designers and road 

agencies: its spectral power distribution (SPD). Broad-spectrum sources, with significant spectral 

output across the entire visible spectrum, potentially provide additional benefits to the driver 

because these light sources can provide better color information and can activate the 

photoreceptors in the eye more efficiently. These benefits from broad-spectrum sources may 

include the ability to reduce lighting levels without sacrificing visibility, improved visual 

performance (speed and accuracy of performing a visual task), better object color recognition, 

and increased visual comfort. A model has been developed that may predict these benefits, but it 

has not been verified in the field.(1) This project investigates these effects and considers the 

potential benefits of a broad-spectrum light source.

In addition to investigating the application of broad-spectrum sources to overhead lighting, this 

project highlights a significant issue in the design and analysis of the roadway visual 

environment. Lighting for motorists is typically provided by two different sources: headlamps 

and, if warranted and installed, a fixed overhead-lighting system. These two systems are 

typically independent. The design of a vehicle headlamp is specified by Society for Automotive 

Engineering (SAE) J1383-2010: Performance Requirements for Motor Vehicle Headlamps and 

Federal Motor Vehicle Safety Standard 108.

(3,4) Requirements for overhead lighting are 

documented in the American National Standards Institute–Illuminating Engineering Society of 

North America RP-8 publication, with more practical specifications in the American Association 

of State Highway and Transportation Officials Roadway Lighting Design Guide.

(5,6) Another 

interesting issue is that jurisdiction governing these two light sources is bifurcated: the National 

Highway Traffic Safety Administration regulates vehicle-based systems, while State and local 

agencies are responsible for roadway lighting infrastructure. Because these two systems are both 

designed and managed separately, their interaction is rarely considered in evaluating the visible 

road environment. Therefore, in addition examining the spectral effects of overhead lighting, the 

integration of vehicle headlamps and overhead lighting and the subsequent effect on visibility 

was also considered in this project.

The final aspect of the project was to take advantage of the potential spectral effectiveness of 

broad-spectrum lighting systems and of video detection and instrumentation systems to develop 

an integrated approach to highlight pedestrians on the side of the roadway. Because many of the 

spectral differences in the eye occur in the periphery of the visual field, a broad-spectrum 

peripheral detection system could potentially benefit visibility to the sides of the road. A 

momentary peripheral illumination (MPI) system was developed and tested for this project to 

determine whether this adaptive lighting system benefited drivers by increasing their detecting 

pedestrians and animals on and along the side of the roadway. The impacts of the MPI system on 

driver behavior were also investigated.

PROJECT OBJECTIVES

The objectives of this project were to evaluate the following:
6

• Impact of the spectra of overhead-lighting systems on driver visual performance for 

higher speed roadways.

• Interaction of vehicle headlamps and overhead lighting in terms of object visibility.

• Applicability of mesopic models and scaling factors in a roadway lighting design.

• Impact of a peripheral illumination system on driver visual performance.

The approach for this project was multiphased. The first phase of the project involved a scoping 

experiment to identify the critical aspects of spectral interactions. The second phase evaluated 

the overall performance of the MPI system. Based on these two experiments, the subsequent 

phase of the project included a study of headlamp and overhead lighting interaction, and a study 

of the models of spectral impact of light sources. Last, an indepth study evaluated all of these 

factors in a single experiment.
7

CHAPTER 2. BACKGROUND

This literature review considers all aspects of the project that were investigated. The first aspect 

is roadway lighting and the overall impact of spectrum on human vision. The second component 

covers the mechanisms for describing visual performance and calculating the impact of lighting 

conditions on the detection of objects in the roadway. The third component considers mesopic 

models. Finally, the fourth component describes the MPI system.

IMPACT OF ROADWAY LIGHTING

Although 25-percent less travel occurs at night compared with daytime, more than 50 percent of 

all fatal crashes occur at night. The fatality rate (number of fatalities per 100 million vehicle-mi 

traveled) for drivers is three times higher at night compared with daylight conditions.(7–9)

Roadway lighting has long been used as a crash countermeasure, and the link between crash 

safety and lighting level has been established through several investigations. (See references 

10–15). In the most recent study, Gibbons et al. show that there is a significant link between 

lighting level and crash safety based on roadway type.(16,17) These investigations consider 

roadway lighting design criteria such as luminance (how much light is reflected from the 

roadway toward the eye of the observer) and uniformity (how evenly the light is distributed on 

the roadway), but none of these investigations have used the color of the light source as a design 

criterion. 

The color of the lighting for the roadway light source has recently become a more critical issue 

in roadway lighting design, but as yet, it is not accounted for in lighting design criteria.(5) The 

human eye responds to electromagnetic waves in the range of about 360 to 800 nm. This is 

known as the visible light spectrum. The light emitted from a light source on a wavelength-bywavelength basis is called spectral power distribution (SPD). The SPD is a result of the physics 

of how the light is produced (i.e., arc tube, emission, phosphor transition, LED, etc.) and can be 

broadband, with emissions across all of the visible light spectrum, or narrowband, with emissions 

in only a small portion of the spectrum. As an example, the SPD of an HPS lamp and three types 

of LED sources are shown in figure 1 and figure 2. Here the HPS lamp would be classified as 

narrowband because it does not have significant output below 500 nm (blue), whereas the LED 

sources would be considered broad spectrum because they have output across the entire 

spectrum. The other curves in the figure, V( ) and V′( ), represent human visual response at 

high (daytime) illuminance levels and low (nighttime) illuminance levels, respectively.

λ λ
8

Figure 1. Graph. Spectral power distribution of HPS light sources.(18)
9

Figure 2. Graph. Spectrum power distribution of three LED light sources.(18)

Light sources have a color component typically measured in correlated color temperature (CCT)

measured in Kelvin (K). The CCT is a measure of how the appearance of a light source relates to 

the temperature of a black body radiator. As the radiator is heated, it typically changes from 

appearing as yellow toward blue as the temperature rises. This color shift is shown in 

figure 3. Typically, light sources that are 3,000 K and higher are categorized as white sources 

because their output is broad spectrum, and they do not have gaps in their color output.
10

Figure 3. Diagram. Change in light source appearance with CCT.(18)

Recent research has shown a benefit of broad-spectrum light in the detection of objects along the 

side of a roadway when compared with traditional narrow-spectrum light sources. (See 

references 19–23.) The potential of this benefit is that a lower light level provides the same 

visual performance under broad spectrum sources as a higher light level under a narrowband 

source. This benefit may be a result of the responsiveness of the human eye to lower levels of 

light (mesopic vision issues), or it may be a result of color information provided by the light 

source in the visual application. The potential for having similar visual performance with 

reduced lighting levels translates directly to reduced energy costs while maintaining driver 

safety. The magnitude of this benefit in an actual driving environment, as well as its relationship 

to the spectral character of the light source, is unknown.

Color

Shaflik detailed differing light types, their colors, and the issues with these light types.

(24) Table 1

is derived from this information. Shaflik notes that blue and white light sources are more 

preferred by the general public, but town planners and astronomers resist them because of light 
11

trespass and light pollution issues. From Shaflik’s review, most blue and white light sources tend 

to be more energy-inefficient than HPS light sources, which lack color-rendering capabilities and 

are less preferred by motorists. It should be noted that Shaflik’s review was in 1997, before the 

advent of high-power LED sources, and he does not consider them in his review.

Table 1. Light type, color, and problems (from Shaflik, 1997).

(24)

Light Type Color Problem

Mercury vapor Blue-white Energy inefficient

Metal halide White Lower efficiency, upward sky glow

High-pressure sodium Amber-pink Poor color rendition

Low pressure sodium Amber-orange Very poor color rendition

Terry and Gibbons compared two LED luminaires of 3,500- and 6,000-K correlated color

temperature to a 4,200-K fluorescent luminaire for the detection and color recognition of small 

targets and pedestrians.(25) Pedestrians were clad in two different colors, and the small targets 

were painted four different colors. Their results were mixed. The 3,500- and 6,000-K sources 

were nearly equal in terms of target color recognition distance; however, the 6,000-K luminaires

outperformed the 3,500-K luminaires in pedestrian clothing color recognition distance by nearly 

30 percent. The 4,200-K fluorescent light outperformed both LEDs for small target color 

recognition, but for pedestrian clothing color recognition, the 4,200-K fluorescent light was 

found to be equal to the 3,500-K LED. When considering both detection distance and color 

recognition distance, the authors suggested that the 6,000-K LED might be the superior light type 

for rendering color. The differences in color recognition between small targets and pedestrians 

were likely a result of the chosen colors for each: pedestrians wore black or denim scrubs, while 

targets were red, blue, gray, or green. 

MESOPIC EFFECTS AT NIGHT FOR DRIVERS

The nature of the lighting conditions for a nighttime driver can define how the spectral output 

from a light source affects the driver’s vision. Eye performance changes based on the ambient 

light level surrounding the driver. This process is typically called dark adaptation and represents 

both changes in overall sensitivity as well as the spectral response of the eye. The dark 

adaptation process represents the transition between the two types of photoreceptors in the retina: 

the rod and cone systems. 

At high light levels, where visual field luminances are in excess of approximately 3.4 cd/m2

(.99 fL) (about early twilight level) conditions are considered to be photopic, and human eye 

response follows a curve known as V( ). When the light level is very low (well below moonlight 

levels, less than 0.001 cd/m2 (0.0003 fL)), the conditions are described as scotopic. Conditions 

between these two levels are referred to as mesopic, which is typically where roadway lighting 

levels fall. In the mesopic range, both rods and cones are active, and visual sensitivity is a 

combination of the two sensitivity functions. 1 cd/m2 = 0.3 fL

Figure 4 illustrates these ranges.(26) This figure also illustrates typical recommended street and 

roadway lighting levels, in terms of averages and minima, for Illuminating Engineering Society 

of North America and Commission Internationale d’Eclairage (CIE) practice.(1,26) Lighting levels 

λ
12

for security are generally in the same range or lower and are usually specified in terms of 

illuminance.

Source: IES

1 cd/m2 = 0.3 fL

Figure 4. Diagram. Ranges of visual sensitivity.(18)

The spectral response of the eye is also a result of the cone/rod transition. The spectral response 

of the cone system, active at high lighting levels, corresponds to the photopic V( ) sensitivity 

curve, which peaks at 555 nm. The spectral response of the rod system, active at low lighting 

levels, corresponds to the scotopic V′( ) sensitivity curve, which peaks at 507 nm. Thus, at the 

transition between photopic and scotopic lighting levels during dark adaptation, the eye’s 

maximum sensitivity changes from green in photopic conditions to blue-green in scotopic 

conditions. This effect—the well-known Purkinje shift, where the eye’s sensitivity shifts from 

longer to shorter wavelengths—has been known for more than a century.

One of the difficulties with this spectral change is that it unevenly affects the retina. It is based 

on the relative densities of the rods and cones and receptor areas across the retina. In the retinal 

periphery, the rod-dominated receptive fields outweigh the cone-dominated fields, which creates 

a stronger mesopic effect in that portion of the visual field. In the fovea centralis, the central 

portion of the retina that provides maximum visual resolution, and at adaptation levels sufficient 

to stimulate the small cone-dominated receptive fields, there is no rod contribution, which means 

there is no mesopic impact in this region. Therefore, because the mesopic effect varies across the 

retina, care must be taken in the design and application of an experiment to provide visual tasks 

that are both fovea based and periphery based.

One of the important aspects of this effect of dark adaptation is that the specifications for light 

outputs of light sources are determined using the photopic V( ) curve. This means that all 

luminaires are characterized photopically, but their implementation in the roadway environment 

is mesopic, where the photopic curve does not accurately indicate their visual effect. However, 

the extent of any deviations between the photopic and mesopic conditions depends on the actual 

luminance levels and viewing conditions in the roadway. 

λ

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13

Models of Mesopic Vision

To evaluate lighting systems in the mesopic adaptation range, a model of the sensitivity of the 

eye during the change from photopic into mesopic lighting conditions was established. 

The luminous efficiency function, V( ), is based on data collected in 1922–23 and was adopted 

in 1924 by the CIE for a central visual angle of 2 degrees.(27) It is the basis of most lighting 

measurements, as well as for manufacturer descriptions of a light source’s output. Other 

luminous efficiency functions are V10( ), the photopic luminous efficiency function for a visual 

angle of 10 degrees, and V′( ), the scotopic luminous efficiency function, derived from data 

acquired at a visual angle of approximately 10 degrees.(27) The ratio of the lamp’s scotopic output 

to its photopic output (S/P) is calculated using V( ) and V′( ), and describes the SPD of a light 

source in mesopic conditions. The models suggest that the higher a lamp’s S/P ratio, the better its 

light is for peripheral detection in the mesopic region.(28,29)

For nighttime driving, in the mesopic vision region, V( ) remains fairly accurate for foveal 

detection but does not describe the eye’s behavior for the kind of off-axis vision commonly used 

in driving. A number of models using various combinations of the photopic and scotopic 

luminous efficiency functions have been developed in an effort to better describe mesopic vision. 

The models discussed here fall into two broad groups, based on how the data to create them were 

collected: brightness-matching models and performance-based models. 

Any mesopic model that is to be implemented as a model of photometry must meet two basic 

requirements. First, the model should obey Abney’s Law of additivity, that the luminance of the

light source is equal to the sum of its comprised wavelengths, which is a fundamental law of 

photometry. The second constraint is that the mesopic spectral sensitivity function should be the 

same as the photopic spectral sensitivity function, V( ), at the upper end of the mesopic region, 

and also should be the same as the scotopic spectral sensitivity function, V′( ), at the lower end 

of the mesopic region. 

Brightness Matching 

To develop a function for mesopic luminous efficiency, a number of researchers had participants 

view two adjacent semicircles of light and match them for brightness. The lower section was 

either white light or 570 nm, and the top section was light of varying wavelengths throughout the 

spectrum.(30–32) Observers identified the point when the two semicircles appeared equally bright; 

researchers then measured their comparative radiances to develop the luminous efficiency 

function. The resulting functions describe the mesopic luminosity of a light source as a linear 

relationship between the log of the mesopic luminous efficiency function and the sum of the 

photopic and scotopic luminous efficiency functions, each weighted by a factor that describes 

where in the mesopic region the test occurred. (See references 30, 32, 33, and 34.) Additional 

brightness-matching functions include contributions of the different types of cones.(35)

Brightness-matching functions accurately describe monochromatic light sources in the mesopic 

region. However, they are inaccurate when describing multichromatic sources such as roadway 

lighting. This is because humans perceive narrow-bandwidth saturated colors as brighter than 

other colors. Thus, two saturated colors together appear less bright than the sum of the 

λ

λ

λ

λ λ

λ

λ

λ
14

constituent colors’ brightness, a phenomenon known as the Helmholtz-Kohlrauch effect.(35)

Brightness matching is performed with narrow-bandwidth stimuli, so the resulting luminous 

efficiency function applies to those stimuli—but not multichromatic sources, which will appear 

dimmer than would be predicted by brightness-matching luminous efficiency functions.

The main problem with using brightness-matching models as a basis for photometry is that they 

tend to violate Abney’s law of additivity. This is because they account for interactions between 

chromatic and achromatic channels of the visual system, which are nonlinear in nature, thereby 

resulting in nonadditive models. 

Performance-Based Models

X-Model:

In an effort to correct for problems with brightness-matching functions and to collect data on 

visual perception in a context more similar to driving, the X-model was developed using data 

collected with the binocular simultaneity method (BSM). The researchers’ goal was to create an 

easy-to-use model of mesopic vision, grounded in human vision, that does not suffer from 

additivity problems, using the familiar luminous efficiency functions V( ) and V′( ).(36)

In the BSM, participants view two stimuli, one for each eye, against two backgrounds set to the 

same adaptation level. Superimposed on the backgrounds are a reference stimulus at 589 nm at a 

reference radiance level for one eye and a test stimulus of varying wavelengths and radiances for 

the other eye. The stimuli are arranged so that, when using binocular vision, they appear 

12 degrees off-axis, one on top of the other. The experimenters flash the stimuli and adjust the 

radiance of the test stimulus until the participant sees the two stimuli appear at the same time. 

Using this method, the experimenters can measure the comparative radiances needed to detect 

the two stimuli simultaneously. From these results, luminous efficiency for the stimuli for a 

number of background adaptation levels can be measured.(27)

The resulting mesopic luminous efficiency function requires an iterative process to calculate the 

mesopic luminous intensity of a source with respect to the V10( ) and V′( ) functions and the 

adaptation level (X) in the mesopic range. Detection time does not suffer from the same bias 

toward saturated colors that brightness matching does; the function is additive and can describe 

multichromatic light sources.(28,36) One major criticism of the X-model is that it was developed 

using data from a very limited number of participants. 

Mesopic Optimization of Visual Efficiency (MOVE) Model:

Like the creators of the X-model, the researchers who developed the MOVE model wanted a 

practical model for mesopic vision designed with realistic nighttime driving in mind and based 

on the existing luminous efficiency functions. They defined a set of common experimental 

parameters and, as a consortium, conducted a large number of experiments based on contrast 

detection threshold, reaction time, and target recognition.(35,37)

Like the X-model, the resulting luminous efficiency function uses V′( ) and x, a factor defining 

the adaption of the eye—the region in the mesopic range where luminous efficiency is 

calculated. Unlike the X-model, it uses V( ) and not V10( ), because the researchers found that 

λ λ

λ λ

λ

λ λ
15

differences between the two were small, and using the more-common V( ) was more 

practical.(37,38) The MOVE model also incorporates the S/P ratio of the light sources, and the 

luminance and spectrum of the background.(38) The MOVE model accurately predicted luminous 

efficiency for both off-axis tasks and tasks with multichromatic light but did not accurately 

predict luminous efficiency for narrow-bandwidth targets or on-axis tasks with monochromatic 

light at 0.01 cd/m2 (0.03 fL). These latter conditions are rarely encountered in nighttime 

driving.(37)

Problems With Models of Mesopic Vision: 

Lighting designers have criticized both the X-model and the MOVE model for incorrectly 

placing the transition point from mesopic to photopic ranges. For the X-model, the transition 

occurs at 0.6 cd/m2 (0.18 fL); in the MOVE model, it falls at 10 cd/m2 (2.9 fL). In practice, many 

lighting experts assume it falls somewhere in between.(39) Therefore, Viikari and Ekrias decided 

to test those models, alongside a modified version of the MOVE model with a transition at 5 

cd/m2 (1.5 fL), using data from three previous experiments.(39) They found the modified MOVE 

model was the most accurate in predicting the experimental results in 9 of 17 test cases. The 

original MOVE model was the most accurate in another seven cases, and the X-model was the 

most accurate in one case. The fact that no single model stood apart as predicting performance 

under all conditions indicates that more research is needed. 

Another example of mixed results using different models of mesopic vision stemmed from 

research on lighting type and level and pedestrian visibility.(40) Results indicated HPS lighting 

outperformed metal halide (MH) lighting for a black-clad pedestrian but not for a blue-clad 

pedestrian. The pedestrians were likely detected foveally (where V( ) accurately predicts 

performance), so mesopic effects, including the benefit of broader-spectrum sources, would not 

be as obvious. Experiments testing off-axis detection are more likely to produce mesopic effects. 

Raphael and Leibenfer used a projector-based simulator to project a roadway and targets at 

2, 6, 10, and 14 degrees off-axis with a range of background luminances and target contrasts.(41)

They found that for targets near the fovea, V( ) more accurately predicted the threshold contrast 

than mesopic models (including the X-model and the MOVE model), but for the 10- and 

14-degree targets, the mesopic models predicted the contrast threshold better than photopic 

luminance. 

CIE Recommended System for Mesopic Photometry Based on Visual Performance:

(1)

Because in some aspects both the X-model and the MOVE-model can be considered to represent 

two extreme ends of a single system, the CIE Recommended System for Mesopic Photometry 

Based on Visual Performance adopted an intermediate system that operates in a region between 

the X-model and the MOVE model.(1) The objective of the recommended system was not only to 

make it widely applicable but also to increase the weight given to achromatic tasks. Two 

intermediate models were considered, with upper and lower luminance limits of 

3 cd/m2 (0.88 fL) – 0.01 cd/m2 (0.003 fL) and 5 cd/m2 (1.5 fL) – 0.005 cd/m2 (0.00015 fL),

respectively. After extensive testing, CIE recommended the intermediate system with an upper 

luminance limit of 5 cd/m2 (1.5 fL) and lower luminance limit of 0.005 cd/m2 (0.00015 fL) as the 

recommended system for mesopic photometry. It takes the form shown in figure 5.

λ

λ

λ
16

Figure 5. Equation. CIE formula for calculating mesopic spectral sensitivity.

Where:

= A normalizing function such that attains a maximum value of 1.

= Value of at 555 nm.

= Mesopic luminance.

= Spectral radiance in W.m-2.

sr-1.nm-1.

if 5.0 cd·m-2

, then .

if 0.0005 cd·m-2

, then .

if 0.005 cd.m-2 5 cd·m-2 then .

It should be noted that the recommended system does not correlate well with visual performance 

when recognition of color is important, when the target has a very narrow SPD, or when 

assessing brightness in the mesopic region. 

Applying Models of Mesopic Vision to Roadway Lighting

Most nighttime driving occurs in the mesopic region, where the eye is more sensitive to blue 

light than photopic luminance, the standard for lighting design, would predict.(28,38) Because they 

are more efficient, broad-spectrum sources can be dimmer—consuming less energy while 

providing the same safety benefit—so those results are promising for energy savings. However, 

models of mesopic vision incorporated into CIE Recommended System for Mesopic Photometry 

Based on Visual Performance, including the MOVE model, were produced using laboratory data 

and must be further validated in roadway conditions before they can be fully applied to roadway 

lighting design.(1) Furthermore, broader-spectrum sources might be more efficient because they 

provide color information, or because they are better suited to mesopic vision. Experiments 

investigating mesopic vision and roadway lighting are described here and are the foundation for 

the work performed in this project. 

X-Model Testing:

As part of the team that created the X-model, Bullough and Rea described a number of studies 

examining roadway lighting.(28) A test-track study found that reaction times with MH lighting 

were shorter than those with HPS lighting for peripheral targets, that headlamps had no effect on 

target detection for targets 15 and 23 degrees off-axis, and that glare reduced reaction time.(42)

Another study, using a roadway lighting simulation with MH lamps (S/P ratio = 1.63) and HPS 

lamps (S/P ratio = 0.64), found that for foveal-target detection, participants had similar reaction 

times with the two lighting types.(43) However, for off-axis detection, reaction times with HPS 

lighting were slower than those for MH lighting, especially at lower light levels. To obtain 

similar reaction times, the HPS lighting had to be about 10 times as bright as the MH lighting, a 

ratio far greater than the ratio of S/P ratios of the lighting types (1.63:0.64 ≈ 2.5:1) would 

predict. Therefore, a comparison of the V( ) and V′( ) functions, on which S/P ratios are based, 

MM(mm)VVmmmmmm (λλ) = mm (λλ) + (1 − mm)VV(λλ) for 0 ≤ mm ≤ 1

and LLmmmmmm = 683

VVmmmmmm (λλ) ∫ VVmmmmmm (λλ)LL (λλ)dd

MM(xx) VVmmmmmm (λλ)

VVmmmmmm (λλ0) VVmmmmmm (λλ)

LLmmmmmm

LL (λλ)

LLmmmmmm ≥ mm = 1

LLmmmmmm ≤ mm = 0

< LLmmmmmm < mm = 0.3334. logLLmmmmmm + 0.767

λ λ
17

does not describe visual performance for off-axis detection in the mesopic range.(43) Two 

simulator studies found that HPS lighting needed to be much brighter than MH lighting, and 

brighter than the ratio of S/P ratios would suggest, to get the same target-detection 

performance.(44,45) Bullough and Rea concluded that, while photopic models accurately predict

foveal detection and driving performance in the mesopic range for multiple lighting types, the 

S/P ratios underestimate MH performance for peripheral object detection—an important task in 

driving—and that the X-model is more accurate than using V( ) and V′( ) alone.(28) They 

recommended that roadway lighting should be designed with peripheral tasks in mind and to take 

a systems approach that accounts for headlamps, roadway lighting, and the cognitive demands of 

nighttime. Research into energy savings has acknowledged the importance of more accurate 

models of mesopic vision and recommends using MH lighting over HPS lighting based on the Xmodel and to use the newer models of mesopic visual performance.(46,47)

MOVE Model Testing:

The MOVE model has also been applied to roadway lighting. Eloholma and Halonen measured 

the photopic luminance of HPS and MH lighting with identical wattages and took background 

luminance measurements for selected areas of the roadway.(38) They found that the photopic 

luminance of MH lighting was lower than its MOVE-calculated mesopic luminance and that the 

photopic luminance of HPS lighting was higher than its MOVE-calculated mesopic luminance. 

The result was similar to that of the X-model testing: MH lighting performed better than HPS

lighting in the mesopic range—more so than predicted by their S/P ratios alone—and MH 

lighting is more efficient than HPS lighting in the mesopic range.(38) Other experiments showed 

that both MH and LED lighting enable better roadside object detection than more common 

narrow-spectrum sources, like HPS lighting. (See references 19, 20, 22, and 23.)

LIGHTING AND THE DRIVING ENVIRONMENT

As discussed previously, roadways are illuminated for nighttime driving with vehicle headlights 

and, on many roads, with overhead roadway lights. These different light sources have a 

combinative effect on roadway visibility. Several components affect this visibility in the 

roadway, as discussed in the following subsections.

Visual Acuity

Driving involves object detection, which in turn relies on detection of motion, luminance 

contrast, and color contrast. Some aspects of driving require higher acuity, such as reading signs 

or differentiating between similar objects. Object detection and categorization affects driver 

behavior; the difference between a tire tread and a pothole, or a pedestrian and a fixed structure, 

or a small animal and highway debris, are all significant in terms of driver reaction. Other 

aspects of driving, such as maintaining lane position, instead rely on peripheral vision and can be 

accomplished with lower acuity. 

For nighttime driving, the lack of understanding of mesopic vision’s effect on visual acuity 

makes it difficult to predict driver’s visual behavior. Predicting this behavior at night is further 

complicated by changing lighting conditions as drivers pass under and away from roadway 

lighting fixtures and by glare from both luminaires and the headlamps of oncoming vehicles. 

λ λ
18

This study examines visual behavior on a road with overhead lighting while driving a vehicle 

with headlamps. 

Age Effects on Vision and Driving

As drivers’ eye age, changes occur in the optical density (OD) of the eye’s crystalline lens. With 

age, the lens becomes more yellow, reducing older persons’ ability to see color and color 

contrast.

(48) Contrast contributes more to visibility than any other factor, so contrast sensitivity 

significantly affects hazard perception. That impact has been measured in driving studies; for 

example, in a study of older drivers, those with cataracts and poor contrast sensitivity had slower 

response times to traffic conflicts.

(49) Another study measured the response time of younger and 

older drivers to roadside objects. Elderly participants were often surprised by the objects 

regardless of the lighting level, but younger participants identified the objects and avoided 

them.

(50) The authors hypothesized that the age-related decrease in target detection was likely a 

result of diminished visual systems in older drivers, including diminished contrast sensitivity.

Aging affects older drivers’ response times and ability to steer, which may also be related to 

visual behavior. In a study by Wood et al., older drivers detected and recognized only 59 percent

of pedestrians, whereas younger drivers recognized 94 percent of pedestrians.(51) Older drivers 

also recognized pedestrians much later than did younger drivers.

(51) Another study found that 

older drivers’ steering accuracy in low-luminance settings was poorer than that of younger 

drivers.

(52) However, Owens and Wood found that older drivers tended to drive more cautiously 

in low light than did younger drivers, showing they might be aware of, and compensating for, 

poorer vision.(50)

Despite the possibility that older drivers compensate for poorer vision, older drivers (ages 55+) 

comprised 16 percent of nighttime crash fatalities and nearly 25 percent of all crash-related 

fatalities, day and night.

(53) Understanding how aging affects visual behavior, especially in 

mesopic night driving conditions, could increase traffic safety and reduce fatalities like those 

above. This study adds to that understanding. 

Visibility and Contrast

Roadway and roadway-lighting design address increasing the visibility of roads and roadway 

objects to drivers. Contrast is a key component of visibility because the amount of contrast an 

object has is often directly related to how well it can be seen. There are two different types of 

contrast: luminance contrast, the comparative brightness of objects irrespective of color; and 

color contrast, the comparative difference in color between objects of the same or different 

luminances. 

Contrast is directly related to visibility in static environments: the higher an object’s contrast, the 

more visible it is. However, because driving is dynamic—with overhead luminaires, moving 

headlamps, and potentially moving roadside objects—factors affecting visibility other than 

luminance contrast might come into play. Two of those factors, roadway and headlamp lighting, 

are investigated in this study and discussed in following sections.

When driving at night and using mesopic vision, a motorist’s ability to discern color varies with

the amount of light available. Because the effect of color-contrast measurements on overall 
19

visibility changes with differing ambient lighting and vision conditions, most researchers 

measure luminance contrast, not color contrast, in night-driving studies. 

Luminance Difference Thresholds

One of the key components of evaluating contrast is a model that quantifies the visibility of 

targets using the luminance difference threshold.(54) The calculation model uses variables that 

include target size, contrast, observer age, exposure time, eccentricity angle, adaptation, and 

distance to determine the visibility level (VL) of the target. A luminance difference ( L) is the 

difference between the luminance of a background and a target. A luminance difference 

threshold is the luminance difference at which the target is just perceived with a probability of 

99.93 percent ( Lth), the formula for which is shown in Figure 6:

Figure 6. Equation. Luminance difference threshold. 

Where:

= Threshold luminance difference.

Lb = Background luminance.

is the target size in minutes of arc FCP is the Contrast Polarity Factor.

t = Observation time.

AF = Age factor.

and L are models of Ricco and Weber’s Law.

VL is a term introduced by Blackwell to better describe the level luminance difference must 

reach for a target to be rendered conspicuous. The formula for calculating VL is given in

figure 7(54,55):

Figure 7. Equation. VL formula.

Where:

= Luminance difference between the target and the background

= Calculated luminance difference from the equation in figure 6.

Luminance contrast is the degree to which the luminance of a target distinguishes itself from the 

background luminance. The Weber contrast formula is commonly used because it accounts for 

contrast polarity. Positive contrast occurs when a light target is against a darker background; 

negative contrast occurs when a dark target is against a lighter background. Higher contrasts or 

high luminance differences do not always correlate with increased visibility. Therefore, by 

∆

∆

∆LL = 2.6�

φφ

1

2

αα

+ LL

1

2�

2

FFCCCC

aa(LLbb , αα) + tt

tt

AA

∆LL

α

φ

VVLL = ΔLLaaaa aaaaaa

ΔLLtthrr hoo

∆LLaaaa aaaaaa

∆LLTThrr hoo
20

incorporating the variables included in the VL calculation, visibility becomes possible to 

quantify. The Weber contrast formula (figure 8) requires target luminance (Lt) and background 

luminance (Lb):

Figure 8. Equation. Formula for calculating Weber contrast. 

VL expresses how much a target’s luminance exceeds the perception threshold and is a function 

of adaptation luminance. As cited by Adrian, VL values of 10 to 20 are considered to be safe 

traffic conditions.(54) The following sections describe the variables used for calculation in the VL 

model and how they are determined. 

Size (Visual Angle)

The visibility calculation considers the size of the target in the viewer’s visual field. A target can 

be as small as the wooden targets used in this project or as large as a pedestrian. Visual angle is 

used to describe object size because relative size in the visual field changes with distance from 

the object; the closer the object, the larger the visual angle. 

The visual angle ( ) can be calculated from the object’s size, S, and its distance from the eye, D, 

from the viewer, as described in figure 9.

Figure 9. Diagram. Variables for calculating visual angle. 

For small visual angles, such as the ones encountered when detecting the presence of small 

wooden targets on the roadside, the formula in figure 10 is used. To convert the visual angle 

from degrees to minutes of arc (MOA), is multiplied by 60.

Figure 10. Equation. Formula for visual angle in degrees.

WW CCoooo = LLtt − LLbb

LLbb

α

α 

D

S

α

αα = tan−1 SS

2DD
21

Contrast Polarity Factor

The contrast polarity factor (FCP), is a phenomenon explained by Aulhorn in 1964 as cited by 

Adrian.(54) Two figures with equal minimum luminance differences but different polarities, 

positive or negative, are not perceived the same way. A dark target on a light background 

(negative contrast) is detected sooner and with more distinguishable detail than is a light target 

on a dark background (positive contrast) with the same absolute value of minimum luminance 

difference. The luminance difference threshold for a target in negative contrast, Lneg, is 

calculated by multiplying the positive contrast, Lpos, by the contrast polarity factor, FCP, as 

described by the equation in figure 11.

Figure 11. Equation. Calculation for negative contrast threshold.

The magnitude of the difference between the minimum positive and negative contrasts, FCP, 

required for object detection depends on the background and target size, as described by the 

equations in figure 12.

Figure 12. Equation. Calculation of the contrast polarity factor.

Exposure Time

The model for visibility incorporates the increment of time that an observer views a target. For 

viewing times shorter than 0.2 s, a greater luminance difference threshold is needed for object 

detection. Typically a glance duration of 0.2 s is most commonly used for design criteria.(54)

Age Factor

Age is a critical factor in the visual perception of objects, especially in low light settings such as 

nighttime driving. As discussed above, the crystalline lens of the eye begins to yellow with 

increased age, limiting the amount and color of light entering the eye.(56–58) Adrian’s model 

accounts for age with two different age-factor calculations for two age groups: one for ages 23 to 

64 and another for ages 64 to 75. The small target visibility model uses the following equations 

for the age groups listed in figure 13 and figure 14.

∆

∆

ΔLL nn = ΔLLpp FFCCCC

FFCCCC(αα, LLbb ) = 1 − mmαα−ββ

2.4ΔLLpp tt=2

For Lb ≥ 0.1 cd/m2:

mm = 10−10−�.0125 �log LLbb +1�

2

+0.0245 �

For all Lb:

ββ = 0.6LL−0.1488
22

Figure 13. Equation. Age factor calculation for younger age group (23 to 64 years). 

Figure 14. Equation. Age factor calculation for older age group (64 to 75 years).

Transient Adaptation

Transient adaptation occurs when the eyes are in the process of adapting and have not 

established a steady state of adaptation. This adaptation process has two speeds: a quick neural 

process related to the photoreceptors and a slower physical process occurring when the pupil 

expands and constricts to adjust to the amount of light in the environment. During nighttime 

driving, light-level changes occur when a vehicle passes under and then beyond overheadlighting fixtures, when the road surface and its reflectivity change, and when oncoming vehicles 

with headlamps approach. Adaptation typically occurs very quickly; however, if drivers’ eyes do 

not sufficiently adapt to the ambient lighting, their ability to detect contrast can be negatively 

affected.(59) Age can also negatively affect transient adaptation. The model for calculating VL 

with luminance threshold difference includes transient adaptation as an element of overall target 

visibility. 

Eccentricity

Eccentricity is defined as the angle, measured in degrees, along the horizontal axis of the visual 

field. Studies have shown that contrast sensitivity depends on several factors, including target 

size, eccentricity of the target, and the background luminance of the visual field. Increases in 

eccentricity are associated with increase in contrast threshold for targets of same size and 

background luminance.(60)

MOMENTARY PERIPHERAL ILLUMINATORS

In 2009, approximately 4,872 non-motor vehicle occupants (pedestrians, bicyclists, etc.) were 

fatally injured, and about 85 percent of those were pedestrians.

(61) About 56 percent of the 

crashes occurred in rural areas, and 83 percent occurred where the speed limit was greater than

72 km/h (45 mi/h). It is likely that the absence of crosswalks, sidewalks, or shoulders on rural 

high-speed roads contributed to those crash fatalities.(61) In addition, approximately 51 percent of

fatal pedestrian crashes occurred at dawn, dusk, or at night,(61) when visibility is more of an 

issue; most pedestrian crashes occur because vehicle drivers fail to see the pedestrians. 

Therefore, technology that helps drivers to see pedestrians on dark rural roads could contribute 

significantly to pedestrian safety. 

An MPI system incorporates vehicle-based sensors that detect pedestrians with servo-actuated 

headlamps that pivot to highlight them, thus directing a driver’s attention to pedestrians. This 

section discusses a theoretical MPI system and its components, and proposes design criteria to 

ensure such a system is effective and successful.

AAFF = (AA + 19)2

2160 + 0.99

AAFF = (AA − 56.6)2

116.3 + 1.43
23

Problems Detecting Pedestrians at Night

In the design of an MPI system, the following must be considered to establish the required 

specifications. Similarly, the experimental evaluation must account for these roadway visibility 

issues.

Peripheral Detection

When driving at night, drivers focus on the center of the roadway. However, pedestrians usually 

walk on the side of the road, where drivers must use peripheral vision to detect them. One of the 

limiting performance factors for the visibility of objects on the side of the road is that the human 

eye has low peripheral sensitivity and visual acuity; the eye’s sensitivity decreases as the object 

is detected farther away from the fovea.(18) Some research has found visual sensitivity plateaus 

between 8 and 30 degrees; other research in peripheral visibility found that target detection 

decreased with increasing angle, dropping off rapidly beyond 15 degrees. (See references 

62–65.) In a road test, more than 80 percent of targets were not detected at a peripheral viewing 

angle of 17.5 degrees.(66) In all cases, peripheral object detection is poor, especially at greater 

angles. Motion of the object may help; however, most objects’ movement in the visual field is 

much slower than the flow of objects due to the motion of the vehicle. In addition, during 

nighttime driving, peripheral vision is mesopic, where the eye’s sensitivity to colors shifts 

toward blue as the environment’s luminance decreases (see Impact of Roadway Lighting section 

above), making accurately modeling visual performance problematic (see Models of Mesopic 

Vision section). 

Headlamps

Headlamps are designed to help drivers see down the road and to minimize glare to oncoming 

traffic. Illuminating the oncoming lane causes glare to oncoming traffic, so headlamps are aimed 

to illuminate the roadway ahead of the vehicle and the right-hand side of the roadway.(18)

Figure 15 shows a headlamp beam’s center, marked by “X,” and the beam pattern resulting from 

aiming the headlamp for the lower right-hand quadrant. Pedestrians usually appear in the lower 

left-hand and the upper right-hand quadrants, where the beam intensity tapers off.(18) If a car is 

far enough away from a pedestrian, the driver might use foveal vision to detect them, but vehicle 

headlamps might not illuminate the roadway to that distance. Therefore, by the time the 

headlamps illuminate a pedestrian, he or she is likely on the periphery of the visual field, making 

him or her difficult to see, and the driver may be too close to avoid a collision. This problem is 

compounded by roadside objects adding visual clutter and/or occluding the view of pedestrians.
24

Figure 15. Graph. Headlamp beam pattern.(18)

One additional problem with headlamp design with respect to pedestrian detection is that drivers 

detect pedestrians using the contrast created when headlamps illuminate pedestrians against the 

background.(18) However, by design, headlamps are aimed low, illuminating both the 

pedestrian’s legs and the ground immediately surrounding them, thus decreasing contrast and 

hindering detection.(18) Increasing headlamp power mitigates that problem but increases glare to 

oncoming vehicles. 

Headlamp Technologies

The headlamp system for the MPI system has two points of consideration: headlamp type and 

headlamp behavior.

Headlamp Type

During low lighting conditions, the headlamp should have an SPD efficient in the mesopic range 

and favor shorter wavelengths. High-intensity discharge (HID) lamps, tuned HID lamps, and 

LED lamps have all provided better off-axis visual performance than traditional headlamp types 

because of their SPDs. (See references 66 and 88–90.)

HID Headlamps:

HID headlamps have higher luminous efficacy, higher color temperature, and longer life 

compared with more traditional halogen headlamps. These lamps are more intense in the beam’s 

periphery. When tested using off-axis targets, participants driving with HID headlamps have 

longer detection distances, fewer missed targets (figure 16), and shorter reaction times (figure 

17) than participants driving with halogen headlamps.(66) HID headlamps enable better peripheral 

detection because of their higher color temperature, brighter area in front of the vehicle, and 

different light spectra. HID lamps have a wider spread of light than halogen lamps. 
25

Reprinted with permission from SAE paper 2001-01-0298 Copyright 

©2001 SAE International Further use and distribution is not permitted 

with permission from SAE.

Figure 16. Chart. Comparison between halogen and HID headlamps.(66)

Reprinted with permission from SAE paper 2001-01-0298 Copyright 

©2001 SAE International Further use and distribution is not permitted 

with permission from SAE.

Figure 17. Graph. Comparison of reaction times between halogen and HID headlamps.

(66)

Spectrally Tuned HID Headlamps: 

Using special filters, HID headlamps can be tuned to emit a higher percentage of shorter 

wavelengths. Participants in a vehicle with tuned HID headlamps detected off-axis targets with 

shorter reaction times and fewer missed detections than those with regular HID headlamps.(88)
26

(See figure 18.) A major limitation of the study was that the test vehicle was stationary; visual 

performance of drivers in moving vehicles with tuned HID headlamps should be further studied.

Source: Van Derlofske, J.F., Bullough, J.D., and Gribbin, C.

Figure 18. Graph. Reaction times with different HID headlamp types.(88)

LED Headlamps:

The use of LEDs is rapidly increasing in automotive headlamps. They have a longer life cycle, 

lower power requirements, and different spectra than HID lamps. In 2008, the Audi® R8 was the 

first car to have full LED headlamps, incorporating 54 high-performance LEDs.

(91)

Researchers at the Lighting Research Center in Troy, NY, investigated the spectral distributions 

of 17 white LEDs used in headlamps. The S/P ratios of three white LEDs were found to be 

similar to or greater than a tuned HID headlamp, indicating that LED headlamps should permit 

drivers to see off-axis targets as effectively as tuned HID headlamps.(64)

Headlamp System Configuration

Typical headlamps are stationary and aimed down the road, where pedestrians are less likely to 

be detected. The headlamp portion of an MPI system would have two sets of headlamps, 

one stationary and the other directed by the MPI system controller to highlight pedestrians and 

roadway objects, increasing the probability of detection.

The MPI headlamps could be installed at a fixed angle with respect to the vehicle and flash 

momentarily as the car approaches a pedestrian. Alternately, the headlamps could be controlled 

by a servo and thus swivel and track the pedestrian as the vehicle moves down the road. These 

options are shown in figure 19. Both technologies require the MPI system to identify and track 

the pedestrian and then calculate the pedestrian’s position with respect to the vehicle. 
27

Figure 19. Diagram. MPI headlamp illuminating pedestrian by flashing on and swiveling.

Potential issues in tracking and illuminating pedestrians include deciding which type of 

headlamp to use, determining how long it is safe to illuminate a pedestrian and how to track and 

illuminate multiple pedestrians, and ensuring pedestrians are comfortable with the system. 

Research on MPI systems should determine which approach is best for pedestrian detection, 

pedestrian comfort, multiple-pedestrian tracking, and avoiding driver distraction.

SPECIFICATION DEVELOPMENT FOR AN MPI SYSTEM

As part of the development of the background for this project, the specifications of a functioning 

MPI system need to be developed.

Conceptual Design of an MPI System

An MPI system would overcome limitations in human vision and vehicle headlamp design by 

automatically detecting pedestrians and using side-aimed headlamps that illuminate the 

pedestrian in sufficient time for the driver to react. MPI headlamp light would be efficient in 

activating the eye’s photoreceptors in the mesopic range and not produce glare for

oncoming drivers. 

Such a system requires two primary components: a detection system and a lighting system. A 

conceptual design for one version of an MPI system is shown in figure 20. That system uses

vision-based detection that enters pedestrian locations into a control system, which activates 

swiveling headlamps. Those and other detection and lighting systems are discussed in 

this section. 
28

Figure 20. Diagram. Conceptual design of an MPI system.

Pedestrian-Detection Systems

An ideal pedestrian collision warning system would detect a pedestrian and the pedestrian’s 

position in time for the headlamp control system and headlamps to illuminate them. The 

detection system would need to have a low false-positive rate because drivers would not continue 

to pay attention to a system with a high false-positive rate. Prior research in pedestrian detection 

points to three commonly used sensor types, described in this section: vision-based sensors,

infrared sensors, and sensors based on time of flight (TOF), such as radar and laser 

scanners.(67–69)

Vision-Based Sensors

Vision-based sensors detect high-resolution information from the environment, extract shapes 

from that information, and compare the shapes with a large sample of example images. If a shape 

matches an image of a pedestrian, a pedestrian is detected. 

Extracting shapes from a complex visual environment can be accomplished in a variety of ways. 

Edge tracking and motion estimating, active frame subtraction, texture property analysis, are 

some possible approaches.(70–72) Other promising approaches analyze image intensity and motion 

information, leg symmetry, motion analysis and parallax, or use stereo matching.(73–75) Systems 

could also be trainable.(72,76) There are a large number of approaches for vision-based systems, 

but all are limited by darkness, where objects and background have no luminance contrast, and 

are also negatively affected by glare. 

Thermal Infrared Sensors

Thermal infrared sensors detect human body heat and are effective when vision-based systems 

are not—at night—when the environment is cooler than human skin. A number of researchers 

have used thermal infrared sensors to detect and track pedestrians. Systems can use a single 

camera or stereo cameras, and detect pedestrians using size and aspect ratio, profile and contrast, 

and pedestrian contour. (See references 77–81.) Other systems use head detection, comparative 

motion of the pedestrian and background and relative motion of the vehicle and pedestrian.(78,82)
29

One system even detected pedestrians and warned drivers.

(82) Although thermal infrared sensors 

work in the dark, they are less effective in poor and hot weather. 

TOF Sensors

TOF systems include radar and laser systems. They scan the environment, collect reflected 

radiation, and estimate the relative velocity of nearby objects based on the differences in time it 

takes for reflected radiation to arrive back at the sensor. TOF systems can be used to select 

objects of interest in the environment for further vision processing, reducing the computational 

load on the vision portion of the system.(83) They could also process data from a TOF system and 

an infrared system in parallel.(84)

Vision-based and infrared sensors provide high-resolution information about a captured scene. 

This high level of information comes at the expense of the amount of processing and 

computation. TOF sensors locate objects accurately, but their resolution is limited. Systems 

using both TOF and vision and/or infrared data would be effective in a variety of conditions but 

can be very expensive.

Pedestrian-Detection Algorithms

Vision-based, infrared-based, and TOF systems all require an algorithm to process the incoming 

data and determine whether a pedestrian is present. Despite the variation in detection technology, 

the detection algorithms are similar, with the basic algorithm structure shown in figure 21.

(67)

Figure 21. Diagram. General structure of a pedestrian-detection system.(67)

This algorithm begins by hypothesizing that an object in the field of view is an object of interest 

and then validates whether or not the object in the field of view is actually an object of interest. 

The system then monitors the input from the sensors to further validate the hypothesis and tracks 

validated objects of interest. Using tracking information, the system calculates whether or not a 

collision is imminent. If a collision is imminent, the tracking information is forwarded to the 

MPI illuminator, which lights up the object of interest. If not, the system continues monitoring 

and tracking objects of interest. 

Pedestrian-Detection System Development

DaimlerChrysler developed and tested the PROTECTOR system in traffic conditions.(85) The 

system detects pedestrians, estimates trajectories, assesses collision risk, and warns drivers. 

Although its performance was promising, PROTECTOR requires more research and 

development before it is implemented. Volvo also developed a pedestrian-detection system for 

the S60, using radar and a camera mounted on the front of the car to detect pedestrians and other 
30

objects.

(86) It can detect pedestrians 0.8 m (32 inches) and taller, and, if a collision is imminent, it 

will alert the driver and automatically brake. Mobileye® technology has been used by 

automakers such as BMW, Volvo, and General Motors, and Mobileye® has developed several 

technologies for detecting pedestrians.

(87) Mobileye® systems use a monocular approach and 

advanced spatiotemporal classification techniques based on machine learning, where the system 

is trained with static and dynamic information. This Mobileye® system does not, however, 

function in the dark.

Pedestrian-Detection System Requirements

Based on previous work, the pedestrian-detection portion of an MPI system should do the 

following: 

• Detect standing and moving pedestrians.

• Detect multiple pedestrians and their direction and speed of movement.

• Detect pedestrians of different sizes and orientations. 

• Categorize and prioritize among pedestrians based on collision risk, if more than 

one pedestrian is present.

• Locate pedestrian positions on the side of the road and calculate the distance between 

the pedestrian and car. 

• Detect pedestrians in the dark and in rain, fog, and snow.

• Have a light source spectrum that maximizes peripheral detection.

• Have minimal latency and issue timely alerts.
31

CHAPTER 3. EXPERIMENTAL APPROACH

Five experiments were undertaken as part of this project. The progression of the experiments is 

shown in figure 22. Later experiments were built from two basic efforts undertaken in the first 

two experiments, a scoping experiment and an MPI experiment. Those two experiments were 

designed to explore the range of the possible experiments’ conditions and to develop the 

boundaries of the future efforts. The two subsequent experiments, the overhead-lighting level 

and mesopic modeling experiments, were performed to investigate other facets of spectral 

interactions. A final performance experiment investigated the results of the first four experiments 

in a full driving environment. Each of the experiments is summarized in the following 

paragraphs.

Figure 22. Flowchart. Relationship among experiments performed.

The scoping experiment compared the detection and recognition distances of pedestrians and 

small targets under two different overhead-lighting sources, two overhead-lighting levels, 

two headlamp colors, and two headlamp levels. The objects detected and recognized were four 

different colors and were located both along the roadway and in the periphery of the roadway. 

The purpose of this experiment was to provide an initial review of the interactions of light source 

spectrum and level and to identify the specific characteristics of the lighting conditions to be 

further investigated.

The MPI system performance experiment investigated the effect of using a momentary 

peripheral illuminator on object visibility. The goal of this experiment was to investigate whether 

the possible spectral improvement in the periphery could be enhanced by the light source type. 

The experiment considered the MPI system under two headlamp light source characteristics, 

two headlamp light levels, three different MPI behaviors, and two different ambient lighting 

conditions. The results of this experiment were used to identify the critical components of the 

Scoping 

Experiment

MPI 

Experiment

Mesopic 

Modelling 

Experiment

Overhead 

Lighting Level 

Experiment

Final 

Performance 

Experiment
32

MPI system, the impact of the light source spectrum on the MPI performance and to identify 

variables to be carried forward to the final performance experiment.

The overhead-lighting level experiment was designed to investigate the interaction between 

overhead lighting and vehicle headlamps on visibility. The spectral effects of a light source 

depend on the adaptation level of the human eye, and headlamps may be the dominant aspect in a 

driver’s field of view; thus, headlamps may be the main contributor to adaptation level. This 

experiment was designed to investigate this aspect of the lighted area by looking at the detection 

and recognition of both small objects and pedestrians in a carefully controlled manner by 

dimming the overhead-lighting system from 100 percent to off, in 10-percent increments. 

The mesopic modeling experiment was designed to provide a real-world validation of the CIE 

Recommended Model for Mesopic Photometry.(1) Because most of the previous work performed 

for mesopic models was done in highly controlled environments, even for the experiment that 

included driving, external validation of the model was required.(42) The experiment implemented 

here used both a static and a dynamic approach to the detection of objects in the roadway. These 

results were then compared with the expected results of the mesopic models.

The final performance experiment combined all of the efforts of the previous experiments to 

perform an overall assessment of the spectral aspects of the overhead lighting. In all, three 

overhead-lighting systems, off-axis pedestrians, five different adaptation luminance levels, 

different vehicle speeds, and three pedestrian clothing colors were used to explore the spectral 

effects of the overhead-lighting system. This experiment was a true driving experiment, used to 

assess the overall applicability of spectral aspects of the light source in a realistic scenario.

There were common elements to each of the experiments undertaken as part of the project. In 

particular, each experiment was performed on the Virginia Smart Road (hereafter referred to as 

the Smart Road) using the same overhead-lighting systems and vehicle headlamp systems. 

Similar objects for detection were also used in all of the experiments. Common components and 

aspects of the experiments are described in this chapter. Experimental methods unique to each 

experiment are described in the chapters on those experiments. 

MATERIALS AND FACILITIES

Virginia Smart Road

The research was conducted at the Virginia Tech Transportation Institute’s (VTTI) Smart Road. 

The Smart Road is a 3.54-km (2.2-mi) restricted-access test track with guard rails, pavement 

markings, and a variety of overhead-lighting installations, as shown in figure 23. The Smart 

Road has two pavement surfaces—asphalt and concrete, and overhead-lighting levels can be 

adjusted to match luminance levels between lighting types, if required.

During the experiments, participant drivers completed a number of laps on the Smart Road, 

turning their vehicles around at the top turn, the bottom turn, or turn three, depending on which 

protocol they were following. 
33

Figure 23. Diagram. Smart Road test track.

Overhead-Lighting Systems

For this project, three lighting systems installed on the Smart Road were used. One was a 

traditional HPS system, and two were solid-state LED systems. All luminaires were dimmable, 

mounted 15 m (49 ft) high, and spaced 80 m (262 ft) apart. The characteristics of the luminaires 

used for this project are listed in table 2, and their SPDs are shown in figure 24.
34

Table 2. Overhead-lighting system characteristics.

Lighting 

System Wattage

Color 

Temperature (K) Luminaire Type Driver

2,100-K 

HPS 150 W 2,100

General Electric model 

M2AC-15S4-A1-GMC2-

1253, Type II M

Greentek 0- to 10-V 

dimming

3,500-K 

LED 234 W 3,500

Cree® LEDway®

BetaLED® model 

STRLWY, Type II M. 3500

Advance 0- to 10-V 

dimming

6,000-K 

LED 234 W 6,000

Cree® LEDway®

BetaLED® model STRLWY, Type II M. 6000

Advance 0- to 10-V 

dimming

Figure 24. Graph. Mesopic modeling experiment—SPDs of overhead-lighting types used in 

the study.

Each of the lighting systems was equipped with a wireless control system to provide dimming 

and on/off control for the luminaires. The control system is a Synapse® Wireless, Inc., SNAP 

DIM-10 0- to10-V system that provides simultaneous 255-step remote-dimming control for all 

3 luminaire systems. 

The overhead-lighting systems were calibrated using the VTTI-developed Roadway Lighting 

Mobile Measurement System (RLMMS).

(23) This allowed each system to be dimmed accurately 

and allowed lighting level characteristics to be matched between lighting systems depending on 

the conditions required for the experiments. 
35

Vehicles and Headlamp Systems 

Two mid-sized sport-utility vehicles with the same body style and internal layout were used in 

the study. Both were instrumented with digital audio and video recorders, luminance cameras 

(described under Luminance-Based Systems in the Procedures section of this chapter), small 

monitors, and keyboards. The data collected included driving distance, vehicle speed, Global 

Positioning System (GPS) location, and the user-input button used to calculate detection and 

color recognition distances. During the experiments, the rear view and side mirrors were covered 

to prevent headlamp glare from the other test vehicle.

HID headlamps were installed on the test vehicles. The headlamps selected were HellaTM 90-mm 

Bi-Xenon projector lamps, rated at 35 W, that use an internal shutter to provide an SAE beam 

pattern with a sharp vertical cutoff. A single 1-F capacitor stabilized headlamp input voltage on 

each vehicle. Low beams only were used. Before each night of experiments, the headlamps were 

aligned according to manufacturer specifications using a Hopkins® Manufacturing Hoppy 

Vision 100 laser aiming system. 

Gels were placed in front of the headlamps and were mounted so they could be easily changed, 

as shown in figure 25. 

Figure 25. Photo. Test vehicle with headlamps and interchangeable gels.

Depending on the experiment, one of five sets of filters was used: white/yellow high intensity, 

white/yellow low intensity, white/blue high intensity, white/blue low intensity, and neutral 

density low intensity. Recommendations for the headlamp configurations were provided by the 

University of Michigan Transportation Research Institute. The headlamp and gel intensity and 

spectral characteristics are listed in table 3, and the SPDs of the headlamps using different filters 

are shown in figure 26 through figure 28. In the first two of these figures, it can be seen that the 

blue light is truncated by the yellow filter and vice versa. 
36

Table 3. Characteristics of headlamp filters. 

Headlamp Color Intensity Transmittance

Correlated Color 

Temperature (K)

White/yellow—high High 0.49 2,926

White/yellow—low Low 0.38 2,910

White/blue—high High 0.44 5,357

White/blue—low Low 0.31 5,120

Neutral density Low 0.34 3,530

Figure 26. Graph. SPD of low-power, color-filtered headlamps.

Figure 27. Graph. SPD of high-power, color-filtered headlamps.
37

Figure 28. Graph. SPD of low-power, neutral-density-filtered headlamps.

Depending on the experiment, yellow filters were used to create an SPD similar to HPS roadway 

lighting, and blue filters were used to create an SPD similar to LED headlamps. When the 

50-percent filters were used, the headlamps were about the same intensity as typical HID 

headlamps installed in vehicles. For the white/yellow headlamp configurations, the CCT was 

slightly different between the high and low settings, because the team used standard, off-theshelf filters (gels) to reduce transmittance, and these off-the-shelf gels did not necessarily have 

precisely the same color properties. The same applied to the white/blue headlamp configurations. 

Visual Objects

For each of the experiments, a visual object was used to measure visual performance. Dependent 

variables characterizing visual performance were detection distance, orientation-recognition 

distance, and color-recognition distance, depending on the experiment. The characteristics, type, 

size, and color of the visual objects were standardized. The objects were either pedestrians or 

small targets and are described in the following subsections.

Pedestrians

Confederates acted as pedestrians. Pedestrians wore four colors of scrubs, depending on the 

experiment—red, black, gray, or blue—as shown in figure 29. Pedestrians were not allowed to 

wear shoes with reflective materials or any other reflective clothing or accessories.
38

Figure 29. Photo. Clothing colors on confederate pedestrian. 

Red was chosen to highlight the amber hue of the HPS overhead lighting and the yellow-filtered 

headlamps. Blue was chosen because LED luminaires have a blue component and because two 

headlamp configurations are white/blue. Gray was chosen as a more neutral color, and black was 

chosen to represent a worst-case, least-visible scenario for a pedestrian at night. The spectral 

reflectance of the four clothing colors, calculated as a percentage relative to a diffuse white 

reflector, is shown in figure 30. The integral of the reflectances for the four clothing colors, 

calculated between 360 and 800 nm using the trapezoid rule and weighted for the eye’s spectral 

sensitivity, are listed in table 4. These integrals give a direct comparison of how visible these 

colors would be in ideal conditions in bright light (photopic) and very low light (scotopic).

Figure 30. Graph. Pedestrian clothing reflectance.
39

Table 4. Integral of pedestrian clothing reflectance.

Clothing Color Red Blue Black Gray

Integral of reflectance (photopic) 11.0 4.3 2.5 26.9

Integral of reflectance (scotopic) 2.7 8.1 2.4 25.3

Targets

Participants also identified targets during the experiment. The targets represented roadway 

obstacles and were 18 by 18 cm (7 by 7 inches)—small and difficult to see but still potentially 

dangerous to drivers. The targets were two-dimensional to remove the effect that viewing angle 

has on a three-dimensional object. Their flat faces facilitated the team’s collection of accurate 

luminance and contrast data, and they were designed to break if a participant ran them over.

The targets were painted one of four colors: red, gray, blue, or green. The red, blue, and green 

were chosen because they are additive primaries; the red, blue, and gray were chosen for the 

same reasons that they were used on the pedestrians’ scrubs. Photos of the targets are shown in 

figure 31, and the spectral reflectance of the targets is shown in figure 32. The integral of the 

reflectances for the four target colors, calculated between 360 and 800 nm using the trapezoid 

rule and weighted for the eye’s spectral sensitivity, are listed in table 5.

Figure 31. Photo. Targets.
40

Figure 32. Graph. Target reflectance relative to diffuse white.

Table 5. Integral of target reflectance.

Target Color Red Blue Green Gray

Integral of reflectance (photopic) 13.0 17.3 15.4 22.3

Integral of reflectance (scotopic) 8.3 27.1 15.6 21.5

DEPENDENT VARIABLES

Three dependent variables measuring visibility were used in the experiments for this project: 

detection distance, color-recognition distance, and orientation-recognition distance. The 

variables used depended on the experimental goals and are described in the following 

subsections. 

Detection Distance

The detection distance was the distance at which a participant was able to detect the presence of 

an on- or off-axis pedestrian or target. To measure detection distance, researchers instructed 

participants to say “person” or “target” when they first saw the pedestrian or target. At that 

moment, an in-vehicle experimenter pressed a button to flag the data. The in-vehicle 

experimenter also pressed a button to flag the data when the vehicle passed the pedestrian or 

target. If a participant failed to see an object, it was counted as a miss. Later analysis calculated 

the distance between these two points to determine the detection distance. 
41

Color-Recognition Distance

The color-recognition distance was the distance at which a participant was able to accurately 

recognize the color of an on- or off-axis pedestrian or target. Participants were instructed to call 

out the color of an on- or off-axis pedestrian’s clothing, or the color of the target, as soon as they 

saw it. The researcher in the vehicle then pressed a button to flag the data. The in-vehicle 

experimenter also pressed a button to flag the data when the vehicle passed the pedestrian or 

target, as stated above, so the distance between where the participant could identify the color and 

when the vehicle passed the pedestrian or target itself was calculated. That distance was the 

color-recognition distance. If participants recognized the wrong color, it was recorded as a miss. 

Figure 33 is a diagram of how detection and color-recognition distances were measured. 

Figure 33. Diagram. Measuring detection and color-recognition distances.
42

Orientation-Recognition Distance

The orientation-recognition distance was the distance at which a participant was able to 

accurately recognize the direction a pedestrian was facing, or the direction the tab on the target 

was pointing. Participants were instructed to call out the direction as soon as they were able to 

discern it. The researcher in the vehicle used button presses to flag the data as was done for the 

other dependent variables, and the orientation-recognition distances were calculated. If a 

participant failed to recognize which way an object was facing, it was recorded as a miss. 

PROCEDURES

Participant Selection

Participants were selected from the VTTI subject database based on their age and gender to form 

two gender-balanced groups, older and younger. 

Screening criteria for participant selection included the following:

• Must hold a valid driver’s license.

• Must not have more than two moving violations in the past 3 years.

• Must have normal (or corrected to normal) vision.

• Must be able to drive an automatic transmission vehicle without assistive devices.

• Must not have caused an injurious accident in the past 3 years.

• Females must not be pregnant.

• Must not have lingering effects of a heart condition, brain damage from stroke, tumor, 

head injury, recent concussion, or infection. Must not have had epileptic seizures 

within 12 months. Must not have current respiratory disorders, motion sickness, inner 

ear problems, dizziness, vertigo, balance problems, diabetes for which insulin is 

required, or chronic migraine or tension headaches.

• Must not currently be taking any substances that may interfere with driving ability, 

cause drowsiness, or impair motor abilities.

• Must be eligible for employment in the United States.

• Must drive at night at least two times per week.

• Must not have had eye surgery.

Participants were compensated $30 for every hour they participated in the study, including time 

spent responding to questionnaires. 
43

Participant Screening 

Telephone Screening

After participants conforming to the age and gender requirements were identified, a research 

assistant called them to ask whether they were interested in participating in the study, to gain 

consent for a screening, and to perform the screening. Those eligible based on the telephone 

screening were scheduled to come to VTTI for the vision screening. 

Vision Screening

When the participant arrived, a researcher obtained consent, had the participant fill out a W9 

form and health questionnaire, and administered vision tests: useful field of view (UFOV), 

brightness acuity, visual acuity, color vision, and contrast sensitivity. Not all vision tests were 

administered for all experiments. Results for participant vision testing are listed in the chapters 

describing the individual experiments. The following describes elements of vision screening:

• Health Questionnaire: Participants filled out a health questionnaire with basic 

demographic information, questions regarding health concerns in the last 24 h, and 

questions regarding their comfort with driving at night. Those with serious health 

concerns or a fear of driving at night were excluded. 

• UFOV: Participants used a computer program that tested their UFOV. Scores ranged 

from 0 to 5; those with a score of 3 and higher were excluded.

• Photopic and Mesopic Visual Acuity: Participants’ binocular visual acuity was 

measured using a Snellen Early Treatment Diabetic Retinopathy Study acuity chart. 

Those with binocular vision worse than 20/40 were excluded. The test was performed 

in both photopic and mesopic lighting conditions. 

• Glare Visual Acuity: Although glare was not a focus of this study, participants’ 

sensitivity to glare was measured using a brightness acuity tester (BAT). The BAT is 

a handheld device with an adjustable light that directs glare toward one eye. While 

holding the BAT over one eye and with the other eye closed, participants read lines 

off of the Snellen eye chart, and their scores were recorded. 

• Color Vision: Participant’s color vision was tested using an Ishihara color vision 

exam. Those responding incorrectly on one or more items were considered colorblind 

and excluded. 

• Contrast Sensitivity: Contrast plays a major role in differentiating between objects 

and their backgrounds. The contrast sensitivity test used an illuminator with a Snellen 

eye chart with 25-percent contrast. Participants’ binocular and monocular 

performance were measured. There was no exclusion criterion for the contrast 

sensitivity evaluation.
44

Experimental Runs

Almost all of the experiments required participants drive on the Smart Road while detecting/

recognizing objects. 

In-Vehicle Experimenter Activities

After participants completed the consent and testing portion of the session, in-vehicle 

experimenters escorted the participants to the test vehicles, where the participants were 

familiarized with the vehicle controls. They then drove the vehicles to the Smart Road and drove

a practice lap, followed by the number of experimental laps required by the experiment. Most 

experiments used two vehicles. During the experiment, the vehicles were driven through the test 

section of the road one at a time, pausing at one of the turnarounds while the other vehicle 

completed its lap, so that the two vehicles would not interfere with each other. Participants 

completing experimental laps would state whether they saw a target or pedestrian, what color 

they saw, and which way the object was facing, and the researcher recorded the detections/

recognitions with button presses, as described in the section on dependent variables. The invehicle researcher ensured that the participant was driving safely and at the correct speed. For 

safety reasons, when using confederate pedestrians, after the participant saw the pedestrian, the 

researcher radioed the pedestrian and asked him or her to move away from the road. 

On-Road Experimenter Activities

On-road experimenters acted as confederate pedestrians and positioned themselves and/or the 

targets alongside the road according to the protocol for that experiment and lap. They were asked 

to clear the road when instructed by the in-vehicle experimenter, or when a vehicle approached 

within about 24 m (80 ft). Experimenters also ensured the overhead lighting was set to the 

correct level and changed headlamp filters as required by the experiment and lap. The test 

vehicles would only pass through the test portion of the road once the on-road experimenters 

confirmed the apparatus was in place. 

PHOTOMETRIC SYSTEMS

For each of the experiments, photometric analyses were performed to determine the lighting 

conditions and the visibility conditions during the experiment. Photometric systems were also 

used to monitor the lighting levels during the experiment and characterize the lighting system’s 

performance. Four different photometric measurement systems were used during the 

experiments, two were luminance-based systems and two were illuminance-based systems, and 

all are described the following subsections. 

Luminance-Based Systems

Both of the two luminance-based photometric systems were imaging systems providing the 

luminance distribution across a scene.

The first system was a Radiant Imaging® ProMetric® 1600 photometer, a stand-alone 

commercial test system providing a 1,024- by 1,024-pixel image of a scene. This system can be 

mounted in the vehicle and provides static luminance measurements from stationary vehicles.
45

The second system was a calibrated luminance camera recording luminance data regarding the 

road in front of the vehicle. The luminance cameras in the two vehicles were calibrated by 

comparing their video to known luminance values.

(92) The luminance camera systems were

mounted to the windshields, linked with the vehicle instrumentation system, and took dynamic 

data from moving vehicles. Every time the participant detected a target or pedestrian, luminance 

data were captured. 

The reduction of the data from the two luminance-measurement system was performed in a 

similar manner. For data from the ProMetric® Radiant Imaging® camera, data were analyzed 

with ProMetric® software. For data taken with the luminance cameras, VTTI developed datareduction software was used.(93) In both cases, the analysis software displayed the captured 

image, allowing the user to select an area of interest, and then calculated the luminance of that 

area of interest. Multiple areas of interest were selected to calculate luminance contrast. A 

screenshot of the VTTI program is shown in figure 34.

Figure 34. Screenshot. Luminance analysis software.

Illuminance-Based Systems

The two illuminance-based systems are a handheld Minolta® T10 system and the VTTI 

RLMMS system. The handheld meter was used to measure the vertical illuminance (VI) and to 

verify the lighting level on the road during the experiment. The RLMMS is a mobile-based 

measurement system that allowed for the illuminance characterization of the entire roadway and 

was used to calibrate the lighting system.
46

DATA REDUCTION PROCEDURES

Detection/Recognition Distances

The detection, color-recognition, and orientation-recognition distances for objects were the 

primary independent variables in almost all of the experiments. These distances were reduced 

from the data stream provided by the instrumentation system in the vehicle. Typically these 

distances were calculated based on the in-vehicle experimenter’s button press at the moment of 

detection or color recognition and a button press at the moment the vehicle passed the visual 

object. The button presses are accurate to within 100 ms and logged to a data file. Using either 

technique, the moment of detection/recognition was identified in the data file containing the 

vehicle’s GPS coordinates, accurate to within 5 cm (2 inches) and recorded using the vehicle 

data acquisition system. Detection/recognition distances were calculated from the GPS 

coordinates at the moment of detection and the moment the vehicle passed the object. 

In the scoping and MPI system performance experiments, button presses were the main method 

for identifying the moment of detection/recognition. If an error occurred and the button presses 

were suspected to be inaccurate, video and audio data, continuously recorded in the vehicles, 

were used to determine the moment when a participant detected/recognized the object. Once the 

exact point of detection was recorded, the time stamp associated with the detection was matched 

to a GPS location recorded by the vehicle’s radar unit. The distance from the point of detection 

to the target or pedestrian was calculated. 

In the other three experiments, the overhead-lighting level, mesopic modeling, and the final 

performance experiments, all moments of detection/recognition were verified by examining 

video data, not by relying on button presses alone. Video verification is more precise because it 

removes any delay caused by an experimenter’s reaction time. This method was used for these 

three experiments because the differences sought were small and accuracy was paramount.

Luminance Data

If the experiment used the luminance camera, the video from the moment when the participant 

detected or recognized the color of a pedestrian’s clothing or a target was correlated with a 

screen capture from the luminance camera. Data reductionists then selected regions of interest in 

the image from which luminance and contrast values were calculated. 
47

CHAPTER 4. SCOPING EXPERIMENT

INTRODUCTION

This experiment was the initial investigation of the spectral impact of overhead lighting and 

vehicle headlamps on driver visual performance. It was a scoping experiment that provided the 

framework for subsequent investigations. 

Research Objectives 

The purpose of the scoping experiment was to accomplish the following tasks: 

• Evaluate the effect of the spectral distribution of overhead-lighting sources on driver

ability to detect pedestrians and targets and recognize colors in the environment.

• Evaluate the effect of the spectral distribution of vehicle headlamp color on driver 

ability to detect pedestrians and targets and recognize colors in the environment.

• Evaluate the impact of the overhead-lighting color and headlamp color in mesopic 

conditions on driver ability to detect pedestrians and targets and recognize colors in 

the environment.

• Evaluate the impact of spectral distribution of overhead lighting and headlamp 

lighting on detection of pedestrians and targets located peripherally.

• Provide a framework upon which to design subsequent experiments.

EXPERIMENTAL DESIGN

A 2 by 4 by 2 by 2 by 4 mixed-factors experiment was designed to investigate the relationship of 

the spectral distribution and level of overhead lighting and headlamps on object detection and 

recognition on a roadway. A partial factorial design was used to ensure participants of both age 

groups were exposed to all combinations of vehicle speed, overhead lighting, and headlamp 

colors and intensities. Experimental variables are listed in table 6 and table 7.
48

Table 6. Scoping experiment independent variables and values.

Independent Variables Levels

Age Younger (25–35), Older (65+)

Overhead-Lighting Type and 

Level

2,100-K HPS High (5 lx (0.46 fc)), 2,100-K HPS Low 

(1.25 lx (0.12 fc)), 

6,000-K LED High (5 lx (0.46 fc)), 6,000-K LED Low 

(1.25 lx (0.12 fc))

Headlamp Type White/Blue 

White/Yellow

Headlamp Intensity High (50-percent transmittance), Low (30-percent 

transmittance)

Pedestrian Clothing Color Red, Black, Gray, Blue

Pedestrian Position Constant VI (On axis), Constant contrast (On axis), Off 

axis

Target Color Red, Gray, Blue, Green

Speed 89 km/h (55 mi/h), 56 km/h (35 mi/h)

lx = lux

fc = foot-candle

Table 7. Scoping experiment dependent variables and measurement method.

Dependent Variables Measurement Method

On-Axis Pedestrian-Detection Distance Participant first sees pedestrian

On-Axis Pedestrian Color-Recognition 

Distance

Participant first correctly identifies pedestrian 

clothing color

Off-Axis Pedestrian-Detection Distance Participant first sees pedestrian

Off-Axis Pedestrian Color-Recognition 

Distance

Participant first correctly identifies pedestrian 

clothing color

Target Detection Distance Participant first sees target

Target Color-Recognition Distance Participant first correctly identifies target color

Independent Variables

Age

Age negatively affects visual perception.(48) There are two components to this visual reduction. 

The first is the yellowing of the lens as a person ages, which acts as a filter across the eye, and 

the second is intraocular scatter, in which light is significantly scattered as it passes through the 

ocular media thus reducing the contrast and the ability of the driver to detect objects. (See 

reference 49, 51, 57, and 94.) Burton, Owsley, and Sloane found that the reduction in retinal 

image quality is almost linear with the age of the observer.(94) However, it is noteworthy that 

driver performance may not follow the same trend as the reduction in the retinal image because

older drivers have more experience driving in general and at night. 

To examine the effects of age on visual performance, two age groups were investigated: younger 

drivers (25–35 years old) and older drivers (65 years old and older). 
49

Overhead-Lighting Type and Level

For this experiment, the 2,100-K HPS and the 6,000-K LED overhead-lighting systems were 

used. Two lighting levels were used: 1.25 lx and 5 lx (0.12 and 0.46 fc).

Headlamp Configuration

Four headlamp configurations were used in this experiment: white/yellow low intensity, 

white/yellow high intensity, white/blue low intensity, and white/blue high intensity. These colors 

were chosen to match the color temperature of the HPS overhead lighting and LED vehicle 

headlamps, respectively. Details regarding the headlamps and filters are included in chapter 3,

Experimental Approach. 

Pedestrian Clothing Color

All four pedestrian clothing colors were used in this experiment.

Pedestrian Position

During the experiment, the confederate pedestrians stood in three sets of positions to satisfy 

three experimental needs. 

The first set of positions was designed to isolate the effect of roadway lighting type and level. 

The VI falling on the pedestrians was controlled. The pedestrians were placed along the length of 

the Smart Road so that the roadway lighting types (HPS and LED) would project the same VI on 

the pedestrian across both lighting types and levels. Depending on the lighting type and level, 

each pedestrian would have to move to one of four possible locations along the road to achieve 

the same VI. In each location, the pedestrian was 0.6 m (2 ft) to the right of the right shoulder 

line, shown in figure 35. This was called the constant-VI position. 

The second set of positions (the constant-contrast positions) was designed to keep the 

pedestrian’s contrast with the background constant. For the constant-contrast position, the 

pedestrian stayed in the same place, 0.6 m (2 ft) from the right shoulder line, for both intensity 

levels of the same lighting type. This means that when the lighting system dimmed, the VI would 

change but the object contrast would not because the background would also be dimmed.
50

Figure 35. Photo. Pedestrian on roadway.

The last set of positions was designed to investigate off-axis visibility and to represent a possible 

collision hazard instead of an immediate collision hazard. This pedestrian was placed beyond the 

right guardrail, approximately 18 m (59 ft) off the roadway. That was called the off-axis position. 

The design was balanced so that all clothing colors and pedestrian positions were viewed under 

all the overhead lighting and vehicle headlamp lighting combinations.

In the two positions close to the road, pedestrians stepped away from the road when the 

participant identified them or when participant vehicle reached a certain point.

Target Color and Position

All four colors of targets were used in this experiment. During the experiment, the targets were 

placed 0.6 m (2 ft) to the right of the shoulder with the flat face aimed toward the oncoming 

participant driver. The target positions controlled for VI, similar to the constant-VI position of 

the pedestrians. Figure 36 is a photo of the target in the roadway.
51

Figure 36. Photo. Target on roadway.

Speed

The experiment was performed at 89 and 56 km/h (55 and 35 mi/h). 

Dependent Variables

Detection and color-recognition distances were measured in this experiment, as described in 

chapter 3. Orientation-recognition distance was not measured, and pedestrians and targets faced 

the same direction for all runs. 

METHODS

Facilities and Equipment

The experiment was performed on the Smart Road using the equipment described in chapter 3,

Experimental Approach. 

Participants 

The participants were recruited and screened, as described in chapter 3. Thirty-two participants 

completed the experiment and were balanced by both age and gender. Mean and standard 

deviation of participant age, visual acuity, mesopic visual acuity, low contrast visual acuity, and 

UFOV are listed in table 8. 
52

Table 8. Scoping experiment participant characteristics.

Participant Characteristic

Older 

Drivers

Mean

Older 

Drivers

Standard 

Deviation

Young 

Drivers

Mean

Younger 

Drivers

Standard 

Deviation

Age 67.2 1.4 26.1 2.3

Visual Acuity 20/20.5 4.7 20/16.4 4.8

Mesopic Visual Acuity 20/35.5 13 20/24.3 6.6

Low Contrast Visual Acuity 20/28.6 7 20/21.3 6.4

UFOV 1.4 0.5 1.3 0.4

Procedure

Experimental Sessions

Those eligible for the experiment, based on the vision and health screening, were asked to come 

to two experimental sessions, with each session testing one overhead-lighting type and speed. 

Two participants completed the experiment during each session. 

Data Collection

Once on the Smart Road, participants drove one practice lap followed by eight experimental laps. 

During the experiment, on-road experimenters altered the overhead-lighting conditions, 

headlamp filters, object types, colors, and positions according to the protocol. In-vehicle 

experimenters directed the participant to drive at the correct speed and recorded detection and 

color-recognition distances using button presses. 

Data Analysis

After data reduction, dependent variables were analyzed with respect to the independent

variables. If the participant failed to detect a pedestrian or target, it was counted as a miss. If the 

participant recognized the incorrect color, it was counted as a miss. 

The detection and color-recognition distances for pedestrians and targets were compared across 

the experimental conditions using analysis of covariance (ANCOVA) in Statistical Analysis 

System® software. Individual and interaction effects were analyzed. Independent variables were 

compared within three groups: on-axis pedestrians, off-axis pedestrians, and targets. There were 

too few detections of off-axis pedestrians to draw meaningful conclusions; however, results are 

described here. Results for on- and off-axis pedestrians were also compared. 

Student-Newman-Keuls (SNK) post hoc tests were performed for all significant effects to 

determine the contribution of the individual factors to the statistical significance. In all figures 

reporting SNK results, mean values sharing a letter label are not significantly different from 

each other.
53

RESULTS

Overhead-Lighting Type and Level

For runs in overhead lighting, independent variables were analyzed with respect to on- and offaxis pedestrian and target detection and color-recognition distances. Results for on- and off-axis 

pedestrians were also compared. 

On-Axis Pedestrian 

ANCOVA results are listed in table 9, with significant main effects described below the table.

Table 9. Scoping experiment on-axis pedestrian significant results summary for runs in 

overhead-lighting type and level.

Factor(s)

Detection 

Distance, F

Detection 

Distance, p

ColorRecognition 

Distance, F

ColorRecognition 

Distance, p

Age 7.78 0.0092a 7.42 0.0108a

Overhead-Lighting Type and 

Level

5.55 0.0023a 1.62 0.1963

Clothing Color 4.27 0.0073a 28.67 < 0.0001a

Clothing Color by OverheadLighting Type and Level2.1 0.0327a 2.06 0.0379a

Headlamp Color and Intensity 0.57 0.5674 1.22 0.3016

Age By Headlamp Color and 

Intensity

0.64 0.5329 0.68 0.5131

Age by Clothing Color 0.71 0.5494 2.02 0.1172

Headlamp Color and Intensity by

Clothing Color

0.44 0.8513 1.62 0.1464

Age by Overhead-Lighting Type 

and Level

1.13 0.3461 1.13 0.3453

Headlamp Color and Intensity by

Overhead-Lighting Type and 

Level

0.42 0.8648 0.83 0.5532

a

Significant at p < 0.05.

Age: 

Driver age significantly affected pedestrian-detection distance. Younger drivers detected 

pedestrians from significantly farther away (the mean M = 118 m (386 ft)) than older drivers 

(M = 88.5 m (290 ft), p = 0.0092). Age significantly affected color-recognition distance for 

pedestrian clothing. Younger participants recognized the clothing color from significantly farther

away (M = 69.3 m (227 ft)) than older participants (M = 49.1 m (161 ft), p = 0.0108). Both 

effects were expected because previous research has shown these types of effects. These may be 

a result of intraocular scatter or the natural yellowing of the lens as a person ages.
54

Overhead Lighting:

Overhead-lighting type and level significantly affected detection distances and color-recognition 

distances. Higher lighting levels of both types corresponded to longer detection distances, as 

seen in figure 37, where bars sharing a letter are not significantly different from each other. What 

is important to note is the difference within overhead-lighting type. The difference in detection 

distances between the high and low levels for the 6,000-K LED luminaires was statistically 

significantly greater than the difference between the high and low levels for 2,100-K HPS 

(p = 0.0023) sources. This indicates an impact that can be attributed to the spectral distribution of 

the light source.

Overhead-lighting type and level affected pedestrian color-recognition distance similarly to how 

they affected pedestrian-detection distance—detection distances were significantly longer for 

higher level lighting. Again, the difference between the high and low levels was more 

pronounced for the LED lighting than for the HPS lighting, again showing an impact owing to 

the overhead lighting’s spectral distribution. The color-recognition distance results are shown in

figure 38, where bars sharing a letter are not significantly different from each other.

1 m = 3.3 ft

Figure 37. Chart. Scoping experiment—mean pedestrian-detection distance by overheadlighting type and level, with SNK results.
55

1 m = 3.3 ft

Figure 38. Chart. Scoping experiment—mean pedestrian color-recognition distance by 

overhead-lighting type and level, with SNK results. 

Pedestrian Clothing Color: 

Pedestrian clothing color significantly affected detection distances. Figure 39 shows that when 

the four lighting conditions were compared, gray clothing was detected from significantly farther

away than any other color, and red was detected from significantly closer than any other color.

Bars sharing a letter are not statistically significantly different from each other.

Pedestrian clothing color also significantly affected color-recognition distance but in a different 

manner than detection distance. The pedestrian with blue clothing was detected, on average, at 

significantly shorter distances than the pedestrians wearing the other colors see (figure 40). Bars 

sharing a letter are not significantly different from each other.
56

1 m = 3.3 ft

Figure 39. Chart. Scoping experiment—mean pedestrian-detection distance by clothing 

color.
57

1 m = 3.3 ft

Figure 40. Chart. Scoping experiment—mean pedestrian clothing color-recognition 

distance by clothing color.

Off-Axis Pedestrian

Participants detected 23 percent of off-axis pedestrians. Participants had only five opportunities 

to detect off-axis pedestrians during the course of the experiment, too few to draw strong 

conclusions, and no effects were found to be significant. However, those results provided 

direction to the research team for future experiments and are discussed. 

Overhead-Lighting Type and Level: 

Off-axis pedestrians were detected from farther away, on average, in LED overhead lighting 

(M = 55.7 m (183 ft)) than in HPS overhead lighting (M = 44.7 m (147 ft)). The difference was 

not statistically significant, but it is in the expected direction, and overhead lighting is capable of 

off-axis illumination. In addition, when overhead lighting was broken down by color and level, 

off-axis pedestrians were detected from farthest away in high-level LED lighting, followed by 

low-level LED lighting. Those results show promise that LED lighting, efficient in the mesopic 

range, allowed objects on the road’s periphery to be detected from farther away, more so than 

HPS lighting at higher levels. A comparison between on- and off-axis pedestrian-detection 

distances in the two lighting types is shown in figure 41.
58

1 m = 3.3 ft

Figure 41. Chart. Scoping experiment—mean off-axis pedestrian-detection distance by 

overhead-lighting type and level.

When overhead-lighting type and level were combined, they had a significant effect on off-axis 

pedestrian color-recognition distance. Participants recognized the color of off-axis pedestrian 

clothing color from farther away in LED lighting (M = 24.2 m (79.4 ft)), than in HPS lighting 

(M = 19.8 m (64.9 ft), p > 0.05. The results, shown in figure 42, were in the expected direction 

given off-axis spectral effects in the mesopic range but were not significant.
59

1 m = 3.3 ft

Figure 42. Chart. Scoping experiment—mean off-axis pedestrian color-recognition distance 

by overhead-lighting type and pedestrian position.

On- Versus Off-Axis Pedestrian 

Overhead-Lighting Type:

Overhead-lighting type did not significantly affect color-recognition distance for either on- and 

off-axis pedestrians, but in both cases, the mean color-recognition distances for LED lighting 

were greater than for HPS. The effect was more so for the off-axis pedestrian, visible in

figure 43. This could indicate a spectral effect in which bluer LED lighting, which is more 

efficient for off-axis mesopic vision than HPS lighting, led to better pedestrian detection. 

Subsequent experiments in this project examined off-axis object detection in detail. 

Clothing Color: 

For on-axis pedestrians, blue clothing had a much shorter color-recognition distance than 

clothing of the other colors, but the same trend was not seen for off-axis pedestrians, nor for the 

detection distance of this blue-clothed pedestrian (figure 44). Theoretical explanations regarding 

human vision do not explain those findings. One environmental factor could be that the on-axis 

pedestrians were seen against the road, and the off-axis pedestrians were seen against the hills 

and brush on the roadside. It could be that the differently contrasting environments affected color 

recognition differently and unpredictably. 
60

1 m = 3.3 ft

Figure 43. Chart. Scoping experiment—pedestrian-detection distance by position and 

clothing color.

1 m = 3.3 ft

Figure 44. Chart. Scoping experiment—pedestrian color-recognition distance by position 

and clothing color.

Speed and Overhead-Lighting Type: 

Overhead-lighting levels were pooled for this analysis. For on-axis pedestrians, mean detection 

distances at 89 km/h (55 mi/h) were shorter than at 56 km/h (35 mi/h), but the opposite trend, 
61

visible in figure 45, was seen for off-axis pedestrians. The detection distance for 56 km/h (35 

mi/h) was particularly short because detection distances for HPS were short. However, with so 

few detections of off-axis pedestrians, it is difficult to draw a firm conclusion that the shorter 

distance is due to the overhead-lighting type. 

1 m = 3.3 ft

Figure 45. Chart. Scoping experiment—mean pedestrian-detection distance by pedestrian 

position, speed, and overhead-lighting type.

Constant Contrast Pedestrian

An analysis of the constant-contrast pedestrian found a significant effect of participant age on 

detection and color-recognition distances. Results for all factors are listed in table 10.

Table 10. Scoping experiment constant-contrast pedestrian results summary.

Factor(s)

Detection 

Distance F

Detection 

Distance p

ColorRecognition 

Distance F

ColorRecognition 

Distance p

Age 7.33 0.0113a 1.43 0.2407

Headlamp Color 1.97 0.1712 0.24 0.6292

Age By Headlamp Color 2.82 0.1036 3.13 0.0874

Overhead-Lighting Type 3.66 0.1043 4.57 0.0763

Headlamp Color by OverheadLighting Type7.46 0.0719 11.03 0.0800

Overhead-Lighting Level 0.84 0.3678 2.20 0.1489 a

Significant at p < 0.05.

Overhead-lighting level and type did not significantly affect detection distance for the constantcontrast pedestrians. The constant-contrast pedestrian was in the same location for each 
62

detection, with the same background, regardless of overhead-lighting level. Lower lighting levels 

had slightly longer detection distances for both overhead-lighting types, as shown in figure 46, 

but the effect was not statistically significant. 

1 m = 3.3 ft

Figure 46. Chart. Scoping experiment—detection distances by overhead-lighting type and 

level for constant-contrast pedestrians.

Targets 

The significant effects of the independant variables on detection and color-recognition distances 

of the targets for detections performed in overhead lighting are summarized in table 11.
63

Table 11. Scoping experiment target results summary for overhead lighting.

Factor(s)

Detection 

Distance F

Detection 

Distance p

ColorRecognition

Distance F

ColorRecognition 

Distance p

Target color 12.35 < 0.0001a 3.6 0.0169a

Target color by overhead-lighting 

type and level

1.32 0.2321 3.25 0.0018a

Age 1.46 0.2372 0.39 0.5369

Headlamp color and intensity 1.51 0.2287 0.33 0.7186

Age by headlamp color and intensity 0.88 0.4196 0.59 0.5581

Age by target color 0.91 0.4406 0.45 0.7145

Headlamp color and intensity by

target color

0.92 0.4833 0.83 0.5474

Overhead-lighting color and level 2.19 0.1013 0.67 0.5717

Age by overhead-lighting color and 

level

0.26 0.8525 0.07 0.9763

Headlamp color and intensity by

overhead-lighting color and level

0.71 0.642 0.66 0.682

a

Significant at p < 0.05.

Target Color:

Only target color significantly affected target detection distances in all overhead-lighting 

conditions. When the four overhead-lighting conditions were combined, the blue target was 

detected from significantly farther away than the other targets, and the gray target was detected 

from significantly closer (figure 47), where bars sharing a letter do not statistically differ from 

each other. The road targets were detected on-axis, and color would not necessarily be prone to 

mesopic effects. More experiments were performed during this project to examine spectral 

effects in the mesopic range. 
64

1 m = 3.3 ft

Figure 47. Chart. Scoping experiment—mean target-detection distance by target color.

Target color significantly affected target color-recognition distance. Red was detected from the 

farthest away, followed by green, blue, and gray (figure 48). All color-recognition distances 

differed significantly from each other. The color-recognitions distances (red, green, blue, and 

gray) are in a different order than the detection distances (blue, red, green, and gray). That could 

be because of confusion between blue and green. Color contrast between the target and the 

background could have an effect, but the luminance camera did not take color-contrast data, so 

color contrast could not be analyzed.

1 m = 3.3 ft

Figure 48. Chart. Scoping experiment—mean target color-recognition distance by target 

color.
65

Target Color and Overhead-Lighting Type: 

Target color and overhead-lighting type combined to affect target color-recognition distance. 

Overhead-lighting levels were pooled for this analysis. In LED lighting, the red and green targets 

were visible from a greater distance than in HPS lighting. That effect was not seen for the blue 

and gray targets, as shown in figure 49. 

1 m = 3.3 ft

Figure 49. Chart. Scoping experiment—target color-recognition distance by target color 

and overhead-lighting type.

No Overhead Lighting

For experiments without overhead lighting, independent variables were analyzed with respect to 

pedestrian and target detection and color-recognition distances, and results are shown in table 12

for pedestrians and table 13 for targets. Results for off-axis pedestrians were not analyzed for 

runs without overhead lighting because there were too few detections for results to be 

meaningful.
66

Pedestrian

Table 12 summarizes scoping experiment results for pedestrians with no overhead lighting.

Table 12. Scoping experiment pedestrian results summary for no overhead lighting.

Factor(s)

Detection 

Distance F

Detection 

Distance p

ColorRecognition 

Distance F

ColorRecognition 

Distance p

Clothing Color 16.15 < 0.0001a 21.04 < 0.0001a

Age 1.25 0.2731 1.35 0.2539

Headlamp Intensity 0.77 0.3887 1.29 0.2659

Age by Headlamp Intensity 1.81 0.1895 4.3 0.077

Headlamp Color 0.04 0.8503 0.6 0.4446

Age by Headlamp Color 0.68 0.4151 0.06 0.8006

Headlamp Intensity by Headlamp 

Color

0.07 0.7876 0.02 0.9668

Age by Clothing Color 1.26 0.2943 1.75 0.163

Headlamp Intensity by Clothing 

Color

0.82 0.4886 0.16 0.9225

Headlamp Color by Clothing Color 0.46 0.7129 0.75 0.5266 a

Signficant at p < 0.05.

Pedestrian Clothing Color: Pedestrian clothing color significantly affected detection distance, 

with gray clothing being detected from significantly farther away than the other clothing colors 

(p < 0.0001) (figure 50). As shown in figure 50, the red and gray clothing colors had higher 

reflectances than the blue and black. When illuminated with vehicle headlamps alone, the higherreflectance pedestrians were more positively contrasted and more visible. 

1 m = 3.3 ft

Figure 50. Chart. Scoping experiment—mean pedestrian-detection distance by clothing 

color for no overhead lighting.
67

Pedestrian clothing color significantly affected pedestrian clothing color-recognition distance 

(p < 0.0001) (figure 51). Pedestrians with red and gray clothing were recognized from farther

away than with black and blue clothing. The similar and shorter color-recognition distances for 

black and blue might be because participants found it difficult to differentiate between the 

two dark colors. Target detection followed the same pattern. 

1 m = 3.3 ft

Figure 51. Chart. Scoping experiment—mean pedestrian color-recognition distance by 

clothing color for no overhead lighting.

Targets

Table 13 summarizes scoping experiment results for targets with no overhead lighting.

Table 13. Scoping experiment target results summary for no overhead lighting.

Factor(s)

Detection 

Distance F

Detection 

Distance p

ColorRecognition 

Distance F

ColorRecognition 

Distance p

Target Color 3.3 0.0242a 3.1 0.0312a

Age 0.71 0.4059 0.23 0.6335

Headlamp Intensity 4.29 0.0673 0.98 0.3303

Age by Headlamp Intensity 0.76 0.3919 0.04 0.8467

Headlamp Color 0.34 0.565 0.11 0.7469

Age by Headlamp Color 0.08 0.7856 0.37 0.5462

Headlamp Intensity by Headlamp 

Color

3.3 0.0801 1.75 0.1975

Age by Target Color 0.5 0.6818 0.3 0.827

Headlamp Intensity by Target 

Color

1.03 0.3854 0.87 0.4614

Headlamp Color by Target Color 1.11 0.3492 2.65 0.0572 a

Signficant at p < 0.05.
68

Target Color: 

Target color significantly affected target detection distance (p = 0.0242), with gray targets 

detected from significantly farther away than green targets, but no other pairwise comparisons 

were significant (figure 52). 

1 m = 3.3 ft

Figure 52. Chart. Scoping experiment—mean target-detection distance by target color for 

no overhead lighting.

Target color significantly affected color-recognition distance, with red and green target colors 

recognized from significantly farther away (red M = 37.3 m (122 ft); green M = 35.6 m (17 ft)) 

than blue targets (M = 28.8 m (94.4 ft), p = 0.0312) (figure 53). 
69

1 m = 3.3 ft

Figure 53. Chart. Scoping experiment—mean target color-recognition distance by target 

color for no overhead lighting.

Luminance Analysis

Luminance camera images taken at the moment a participant detected a pedestrian or target were 

analyzed. They were also analyzed at color recognition, but those results largely mirrored the 

detection results and are not discussed. Luminance analyses were only performed for runs with 

overhead lighting. 

Pedestrian-Detection Distance 

Luminance: 

At the moment participants detected pedestrians under HPS lighting, the average pedestrian 

luminance was 0.327 cd/m2 (0.095 fL), less than that in LED lighting, 0.464 cd/m2 (0.14 fL). 

Figure 54 shows a threshold at a luminance of approximately 0.2 cd/m2 (0.06 fL) where on-axis 

pedestrians, regardless of clothing color, were able to be detected. 
70

1 cd/m2 = 0.3 fL

1 m = 3.3 ft

Figure 54. Scatter Plot. Scoping experiment—pedestrian-detection distance, luminance, 

and clothing color.

Contrast:

The average contrast when pedestrians were detected was also different between the lighting 

types, with the contrast in HPS lighting at -0.086 and LED lighting at -0.032. A lower negative 

contrast, as seen in HPS lighting, means a darker pedestrian with respect to the background was 

needed than in LED lighting.

When further broken down by clothing color, a combined effect of clothing color and lighting 

type on contrast at time of detection is seen in figure 55. For all clothing colors except red, 

pedestrians were more highly contrasted in LED lighting than in HPS lighting, as measured by 

detection distance. In HPS lighting, red clothing requires the lowest contrast for detection, 

meaning it is particularly visible in that lighting type. The opposite trend is seen in LED lighting, 

where red requires the greatest contrast for detection, meaning it is less visible in that lighting 

type. That is because the spectrum of HPS lighting is redder than that of LED lighting, making 

red clothing visible, while the opposite is true in LED lighting. 
71

Figure 55. Chart. Scoping experiment—pedestrian mean Weber contrast at time of 

detection by overhead-lighting color and clothing color.

Uniformity:

LEDs have a more uniform horizontal light distribution than HPS sources, as shown in figure 56, 

where the photo of HPS lighting has bright streaks across the road, and the photo of LED 

lighting does not. Even though VI on the pedestrians was matched between HPS and LED

lighting, less negative contrast was needed in LED lighting for pedestrian detection. That is 

because HPS lighting created bright lines across the roadway that increased background 

luminance values and therefore increasing the absolute contrast.
72

Figure 56. Photo. Uniformity of HPS and LED lighting.

Target Detection Distance

Luminance: 

At the moment participants detected targets under HPS lighting, the average target luminance 

was 0.916 cd/m2 (0.267 fL), greater than that in LED lighting, 0.683 cd/m2 (0.199 fL). There 

appears to be a threshold at a luminance of approximately 0.2 cd/m2 (0.06 fL) where targets were 

detected, shown in figure 57. 
73

1 cd/m2 = 0.3 fL

1 m = 3.3 ft

Figure 57. Scatter Plot. Scoping experiment—target-detection distance and luminance.

Contrast: 

The average contrast when targets were detected was also different between the lighting types, 

with the contrast in HPS lighting at 0.788 and LED lighting at 0.734. Unlike the pedestrians, the 

targets were in positive contrast at detection. Similar to the pedestrians, a higher absolute 

contrast was needed in HPS than LED lighting to detect the targets. 

DISCUSSION

The study considered many factors, including overhead-lighting type (2,100-K HPS and 6,000-K 

LED) and intensity, and headlamp color and intensity. 

A project objective was to measure the combined effect of vehicle headlamps and overhead 

lighting on visibility. Results of this experiment indicated that headlamp color and intensity did 

not significantly affect detection and color-recognition distances. While that result may be 

expected in situations where the overhead lighting is the predominant lighting source, when there 

was no overhead lighting present and the driver relied solely on headlamp light, there was still no 

statistically significant difference in visibility between the white/blue or white/yellow headlamps 

or between high- and low-powered headlamps. That result held true for detection and colorrecognition for both targets and pedestrians. Interestingly, results were closest to approaching 

significance for targets positioned low to the ground in the dark section of the road. A possible 

explanation for this could be that the beam pattern of the headlamps is critical for highlighting 

low targets on the roadway. 

The project also aimed to measure the impact of spectra of overhead-lighting systems on 

visibility, another project objective. The results regarding the effect of overhead-lighting type, 
74

(HPS with a correlated color temperature of 2,100 K and LED with a correlated color 

temperature of 6,000 K) and overhead-lighting intensity on pedestrian detection were mixed. For 

the constant-contrast pedestrian, there was no significant difference between the effects of 

overhead-lighting type and effects of level of the overhead lighting on detection distance. 

However, results combining all pedestrian locations indicated a very strong impact of overheadlighting type and level on pedestrian-detection distance. This is an interesting result because it 

highlights the importance of contrast for detection. A few aspects of the experiment could have 

produced this result. First, constant-contrast pedestrians only wore gray clothing, so interactions 

between the spectral distribution of the overhead lighting and clothing color contrast would not 

be present. Second, this task was likely foveal, so mesopic effects would be minimal. The 

constant-contrast result shows that contrast is by far the more dominant impact on detection 

compared with other factors such as overhead-lighting SPD and intensity. It also demonstrates 

that the spectral effects are by far more evident in the periphery, as shown by the off-axis results 

and by the fact that changing the light source spectrum did not improve performance when all 

other factors were controlled.

Despite informing participants that pedestrians could be located both along the roadway and off 

to the side of the roadway, there were very few detections of off-axis pedestrians. The low 

detection rate for the off-axis pedestrians did not produce enough data to draw firm conclusions. 

However, for off-axis pedestrians, high-level LED overhead lighting resulted in a greater 

detection distances than high-level HPS overhead lighting; the same was not true for low-level 

lighting. The results were not statistically significant but are worth considering because they 

might indicate an effect of the overhead lighting’s uniformity. One possible reason for the 

greater detection distances in high-level LED lighting could be the distribution of the light source 

and how it illuminates the environment outside the roadway. The off-axis pedestrians stood on a 

grassy and rocky area, much less uniform than a typical roadway surface. The LED overhead 

lighting was more uniformly distributed than the HPS lighting. Thus, the more-uniform LED 

overhead lighting combined with the less-uniform background for off-axis pedestrians could 

have allowed participants to detect those pedestrians from farther away. 

CONCLUSIONS

A research objective of this experiment was to evaluate the effect of the spectral distribution of 

overhead-lighting sources on drivers’ ability to detect pedestrians and targets and recognize 

colors in the environment. Results found that overhead-lighting type and level significantly 

affected detection and color-recognition distances for pedestrians, with brighter lighting 

corresponding to longer detection and color-recognition distances. The effect of a change in 

overhead-lighting level for a particular type of lighting was significant. For example, lighting 

level significantly affected detection and color-recognition distances for LED lighting. For HPS 

lighting, lower levels of lighting also resulted in shorter detection and color-recognition 

distances, but the effect was not significant. The different results for LED and HPS lighting 

indicate a spectral effect occurred with the LED lighting that did not occur with HPS lighting. 

Another research objective for this experiment was to evaluate the impact of spectral distribution 

of overhead lighting and headlamp lighting on detection of pedestrians and targets located 

peripherally. Results indicate that, for off-axis pedestrians, LED overhead lighting had greater 
75

(though not statistically significantly greater) color-recognition distances than HPS lighting, 

possibly indicating a spectral effect of overhead-lighting type. 

Evaluating the effect of the spectral distribution of vehicle headlamp color on drivers’ ability to 

detect pedestrians and targets and recognize colors in the environment was an additional research 

objective. Results found that headlamp color and intensity did not significantly affect detection 

and color-recognition distances when overhead lighting was used. Because overhead lighting had 

a greater effect on visibility than headlamps, overhead lighting was the focus of subsequent 

experiments.

Other noteworthy results include that age significantly affected detection distances when there 

was overhead lighting but not without overhead lighting. Age-related response times might have 

been a factor, but responses were not corrected for this factor. Also, age-related response times 

would not explain why age significantly affected detection distances in overhead lighting only. 

Age effects were not a main research objective, and these results were not further analyzed in 

this experiment.

Pedestrian clothing color and target color both significantly affected detection and colorrecognition distances, whether or not overhead lighting was used. The best and worst colors for 

detection and color recognition varied between pedestrians and targets, and between whether or 

not there was overhead lighting, as listed in table 14. However, clothing and target color were 

not the focus of this project on light-source spectra.

Table 14. Scoping experiment best and worst colors for target and pedestrian detection and 

color recognition, with and without overhead lighting. 

Condition Dependent Variable Color

On-Axis 

Pedestrian

Off-Axis 

Pedestrian Target

Overhead lighting Detection distance Best Gray Red Blue

Overhead lighting Detection distance Worst Red Black Gray

Overhead lighting Color-rec. distance Best Gray Red Red

Overhead lighting Color-rec. distance Worst Blue Black Gray

No overhead lighting Detection distance Best Gray N/A Gray

No overhead lighting Detection distance Worst Black N/A Green

No overhead lighting Color-rec. distance Best Gray N/A Red

No overhead lighting Color-rec. distance Worst Blue N/A Blue

rec. = Recognition.

N/A = Not applicable.

The uniformity of the overhead lighting appeared to affect the results. At the time of detection, 

pedestrians contrasted more against the background under HPS lighting than under LED lighting, 

likely because HPS lighting was less uniform and created bright lines across the roadway. Future 

experiments included HPS and LED lighting to further investigate the effect of the overhead 

lighting’s uniformity on object detection. 
76

Design of Further Experiments

The results of the scoping experiment were used to inform the design of the subsequent phase of 

the project. This project’s subsequent investigation of the spectral impact of the overheadlighting sources included the following efforts: 

• Evaluate the full range of the spectral impact of the light source on the visibility of 

both on-axis and off-axis targets because the scoping experiment showed that a wider 

range of off-axis detection objects was required to more fully describe the impact of 

overhead lighting’s spectral effect.

• Test an MPI peripheral highlighting system’s effectiveness in increasing off-axis 

pedestrian visibility, possibly increasing detection rates from the very low 23-percent 

rate found in the scoping experiment. 

• Evaluate further the interaction of overhead lighting and headlamps on visibility

because results from the scoping experiment regarding this interaction were not 

statistically significant.

• Evaluate the effectiveness of the CIE-developed mesopic model for describing 

nighttime driving visibility conducted both in a static and a dynamic environment 

because the scoping experiment was not specifically designed to address this project 

objective.

LIMITATIONS

Participant fatigue and learning effects might have affected the results. There were too few offaxis detections to draw strong conclusions regarding off-axis detection. 
77

CHAPTER 5. MPI SYSTEM PERFORMANCE EXPERIMENT

INTRODUCTION

As discussed earlier, one promising technology for increasing pedestrian and object visibility on 

nighttime roadways is an MPI system that would detect and highlight crash hazards. With this 

technology, objects located in the areas of a driver’s peripheral vision could be illuminated more 

effectively than with a traditional headlamp design. With illuminating objects in a driver’s 

peripheral vision comes the consideration of how a driver’s eyes process headlamp color. It is 

also worth exploring driver reactions to that technology. For example, it is unknown whether 

moving headlamps would distract drivers to the extent that visibility would be decreased instead 

of increased. Therefore, the goal of this experiment was to test the visual performance and extent 

of distraction of participant drivers using an MPI system to relate such a system to detection and 

recognition of color located in the periphery of driver’s visual field.

Designing an actual MPI system was outside the scope of this project. However, a mockup MPI 

system that could not detect pedestrians but instead moved the headlamps to illuminate 

pedestrians at predetermined locations, was designed, built, and used. The MPI system behaved 

in two ways, either turning on the headlamp at a set distance and spot-lighting the pedestrian for 

a short period of time, or swiveling the headlamp to keep the pedestrian in its beam while the 

vehicle approached. Two headlamp colors were used—the white/yellow and white/blue used in 

the scoping experiment—but only the high-intensity filters were used. Because small target 

detection is difficult using machine vision software, MPI systems would likely be designed to 

illuminate only pedestrians. Thus, this experiment used pedestrians only to test visibility. 

Pedestrians were placed on both sides of the road and wore two clothing colors. 

Detection distance, detection rate, and color-recognition distance quantified visual performance. 

A major concern with using MPI systems is the extent to which they would distract the driver. 

Detection distances cannot entirely capture driver distraction, so an eye tracker was used in this 

experiment to measure the driver’s fixation duration. 

Research Objectives

One of this project’s objectives was to evaluate the impact of a peripheral illumination system on 

driver visual performance. To achieve that objective, the purpose of the MPI system performance 

experiment was to evaluate the following: 

• How different highlighting behaviors of the MPI system affect driver visual 

performance.

• How the MPI system and overhead lighting interact to affect driver visual 

performance.

• How different headlamp colors on an MPI system affect driver visual performance.

• How an MPI system affects driver eye-glance behavior. 
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EXPERIMENTAL DESIGN

A mixed-factors experiment was conducted investigating the effects on participant detection 

distances and detection rates and on participant eye-glance behavior. Factors considered were

participant age, headlamp color, overhead-lighting level, MPI system configuration, pedestrian 

position, pedestrian clothing color, and whether the pedestrian was illuminated. Variables are 

listed in table 15 and table 16.

Table 15. MPI system performance experiment independent variables and values.

Independent Variable Levels

Age Younger (25–35), Older (65+)

Headlamp Color White/Yellow, White/Blue

Overhead Roadway Lighting Level On, Off

MPI System Configuration Highlighting, Tracking, Off

Pedestrian Position on Road Left, Right

Pedestrian Clothing Color Red, Blue

Pedestrian Illumination by MPI System Yes, No

Table 16. MPI system performance experiment dependent variables and measurement 

method.

Dependent Variables Measurement Method

Pedestrian-Detection Distance Participant first sees pedestrian

Pedestrian Color-Recognition Distance Participant first correctly identifies pedestrian 

clothing color

Fixation Duration Measured with eye tracker 

Independent Variables

Age

For the same reason as in the scoping experiment, this experiment included two age groups: 

younger (25–35) and older (65+). 

Headlamp Color

Two headlamp configurations were used in this experiment: white/yellow high intensity and 

white/blue high intensity. The high-intensity filters were used because they more closely 

matched the intensity of headlamps on the market. 

MPI System Configuration

The purpose of this experiment was to measure driver reactions to a system with moving 

headlamps that illuminate pedestrians, rather than to develop a full-featured working MPI 

system. Therefore, the team designed and built a mockup MPI system with headlamps swiveled 

and aimed according to a predetermined program, rather than by detecting the pedestrians in real 

time. The headlamps are shown in figure 58.
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Figure 58. Photo. MPI system with straight (left) and swiveling (right) headlamp. 

Confederate pedestrians stood on the roadway in the positions the headlamps were programmed 

to illuminate. The mockup MPI system had three configurations. The first was highlighting, in 

which the system would aim a headlamp at a pedestrian and turn it on momentarily, illuminating 

the pedestrian for a short period of time. Highlighting occurred when the vehicle was about 

46.7 m (150 ft) from the pedestrian, and only the headlamp closest to the pedestrian would 

highlight him or her. The second MPI system configuration was tracking, in which the MPI 

system turned on the headlamp closest to the pedestrian when the vehicle was about 183 m 

(600 ft) from him or her and then swiveled the headlamp to keep the pedestrian in the beam as 

the vehicle approached. The last configuration was when the MPI system was off, with the 

headlamps aimed down the roadway as in a normal vehicle. The three MPI system 

configurations are illustrated in figure 59. The MPI system illuminated pedestrians on both sides 

of the road. 
80

Figure 59. Diagram. MPI system performance experiment—MPI system configurations.

Pedestrian Clothing Color

Pedestrians wore blue or red scrubs. The blue color was chosen because both the 6,000-K LED 

overhead lighting and white/blue headlamps have strong blue components. The red was chosen 

because the white/yellow headlamps have a strong amber component. Both red and blue are 

common clothing colors. 

Pedestrian Stations

Pedestrians were placed at various locations along the road to create the independent variables

described in the following sections. All pedestrian stations were placed away from curves in the 

road, so line-of-sight would not be shorter than the detection distance. Stations were placed far 

enough apart so that participants would not detect two pedestrians at two different distances at 

the same time. VI on the pedestrians’ faces was kept constant at 0.40 lx (.037 fc) for all 

pedestrian stations. Although the pedestrians’ faces were illuminated at 0.40 lx (0.037 fc), the 

background behind the pedestrians was necessarily different, creating different contrasts. 
81

Pedestrian on Left or Right

Pedestrians were placed on the left-hand or right-hand side of the roadway with respect to the 

oncoming vehicle, approximately 12 m (40 ft) away from the shoulder. 

Pedestrian in Overhead Lighting

Pedestrians stood at five different stations. Two of those stations were illuminated by overhead 

lighting, and three were not. The areas with overhead lighting used 6,000-K LED luminaires 

dimmed to 20 percent. At that dim level, the horizontal illuminance was 1.97 lx (0.183 fc) when 

measured on the ground, halfway between two luminaires, on the shoulder of the road opposite 

the luminaires. That dim level also resulted in a VI of 0.40 lx (0.037 fc) on the pedestrian’s face

and enabled researchers to consistently illuminate the pedestrians in the sections of the road with 

overhead lighting. 

Pedestrian Illuminated by MPI System

To test whether the MPI system would distract drivers from seeing pedestrians or objects outside 

of its beam path, some runs were performed with a pedestrian on the side of the road opposite 

from where the MPI system illuminated, illustrated in figure 60.

Figure 60. Diagram. MPI system performance experiment—pedestrian opposite from area 

illuminated by MPI system.

Dependent Variables

Detection and color-recognition distances were measured in this experiment, as described in 

chapter 3. Orientation-recognition distance was not measured, and pedestrians faced the same 

direction for all runs. Fixation duration was also measured and is described the following 

subsections. 

Fixation Duration

Fixation duration was measured using an Arrington Research ViewPoint EyeTracker® and its 

accompanying software. The eye tracker was mounted on a pair of clear goggles with 

two cameras, one under each eye, to determine the direction the pupil was aimed. A third camera 

mounted on the bridge of the goggles was aimed forward to record the scene the user was seeing. 
82

After calibration, the software overlays a dot corresponding to gaze direction onto the video from 

the forward-facing camera, and researchers then use the dot to determine where the participant 

was looking at any point in time. The eye-tracker goggles are shown in figure 61, and 

screenshots from the eye tracker software showing pupil identification and dot overlaid on a 

scene are shown in figure 62. 

Figure 61. Photo. MPI system performance experiment—eye-tracker goggles.
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Figure 62. Screenshot. MPI system performance experiment—pupil location (left) and dot 

representing gaze direction overlaid on scene (right). 

Facilities and Equipment

The experiment was performed on the Smart Road using one of the same test vehicles as in the 

scoping experiment. The vehicle headlamps were aligned each evening before experiments were 

conducted. 

Participants

Participants were recruited and screened as described in chapter 3. A total of 13 participants 

performed the experiment. Seven were older, and six were younger. Seven were female, and six 

were male. Mean and standard deviation of participant age, visual acuity, mesopic visual acuity, 

low contrast visual acuity, and UFOV are listed in table 17. 
84

Table 17. MPI system performance experiment participant characteristics.

Participant Characteristic

Older 

Drivers

Mean

Older 

Drivers

Standard 

Deviation

Young 

Drivers

Mean

Younger 

Drivers

Standard 

Deviation

Age 66.8 1.7 28.4 2.4

Visual Acuity 20/26.9 9.8 20/19.1 3.9

Mesopic Visual Acuity 20/39 15.7 20/28.1 5.8

Low Contrast Visual Acuity 20/30.8 17.5 20/20 5

UFOV 1.6 0.5 1 0

Procedure

One participant completed the testing protocol during each session. Upon arrival, participants 

followed the procedure outlined in chapter 3 but were also fitted for the eye tracker, which was 

then calibrated. They next proceeded to the Smart Road, where they drove one practice lap 

followed by eight experimental laps at a maximum speed of 56 km/h (35 mi/h). During the 

experiment, the confederate pedestrians positioned themselves at the stations and changed the 

headlamp filters according to the protocol. The in-vehicle researchers configured the MPI system 

according to protocol and recorded detection and color-recognition distances. 

Data Analysis

After reduction, analysis of variance (ANOVA) tests were performed comparing detection and 

color-recognition distances across the independent variables. ANOVAs show whether the mean 

values of multiple variables differ significantly from each other. They do not report which means 

differ significantly from other means. Therefore, Tukey’s honest significant difference (HSD) 

tests were also performed to determine which individual variables significantly differed from 

other individual variables. When Tukey HSD results are shown in charts, bars sharing a letter are 

not significantly different from each other. 

Detection Rates

Detection rate is the percentage of instances a pedestrian, either on the same side of the road 

illuminated by the MPI system or opposite the side illuminated by the MPI system, was detected. 

Detection rates were only calculated with respect to the pedestrian-illuminated-by-MPI-system 

variable. 

Mean Fixation Duration

Video clips of participants detecting pedestrians were isolated from the video files using button 

presses as flags. A custom MATLAB® program allowed data reductionists to calculate the 

fixation duration, i.e., the length of time the participant gazed at a pedestrian. Mean fixation 

durations were calculated for the three MPI configurations (off, tracking, and highlighting). 
85

RESULTS 

Detection and Color-Recognition Distances

For detection distance, three independent variables resulted in significantly different means: age, 

headlamp color, and pedestrian clothing color. There was also a significant effect of the 

interaction between MPI system performance and headlamp color. Detection-distance results are 

shown in table 18.

For color-recognition distance, two independent variables resulted in significantly different 

means: age and pedestrian clothing color. There was also a significant effect of the interaction 

between MPI system configuration and headlamp color. Color-recognition results are shown in 

table 19.

Table 18. MPI system performance experiment detection distance results per independent 

variable.

Independent Variable(s) F-value Pr > F

Age 57.51 < 0.0001a

Headlamp Color 5.90 0.0355a

Headlamp Color and Pedestrian in Overhead Lighting 0.09 0.7650

MPI System Configuration 2.50 0.1076

Pedestrian Clothing Color 17.49 0.0015a

Pedestrian Clothing Color and Pedestrian in Overhead Lighting 1.28 0.2825

Pedestrian on Left or Right 3.81 0.0767

Pedestrian in Overhead Lighting 1.52 0.2430

MPI System Configuration by Pedestrian in Overhead Lighting 0.99 0.3917

MPI System Configuration by Headlamp Color 3.90 0.0405a

MPI System Configuration by Pedestrian on Left or Right 1.79 0.1967 a

Signficant at p < 0.05.

Pr = Probability.
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Table 19. MPI system performance experiment color-recognition distance results per 

independent variable.

Independent Variable(s) F-value Pr > F

Age 36.08 < 0.0001a

Headlamp Color 3.02 0.1131

Headlamp Color and Pedestrian in Overhead Lighting 0.85 0.3792

MPI System Configuration 0.52 0.6037

Pedestrian Clothing Color 31.79 0.0002a

Pedestrian Clothing Color and Pedestrian in Overhead Lighting 0.08 0.7811

Pedestrian on Left or Right 4.80 0.0562

Pedestrian in Overhead Lighting 0.67 0.4322

MPI System Configuration by Pedestrian in Overhead Lighting 0.93 0.4161

MPI System Configuration by Headlamp Color 6.56 0.0090a

MPI System Configuration by Pedestrian on Left or Right 2.95 0.0832 a

Signficant at p < 0.05.

Pr = Probability.

Results are discussed in detail in following subsections. 

Age

Age significantly affected detection distance because younger participants (M = 70.2 m (230 ft)) 

detected pedestrians from significantly farther away than older participants (M = 49.2 m 

(161 ft)), p < 0.0001).

Age also significantly affected color-recognition distance because younger participants 

(M = 57.2 m (188 ft)) recognized clothing color from significantly farther away than older 

participants (M = 42.1 m (138 ft)), p < 0.0001).

Headlamp Color

Headlamp color significantly affected detection distances. When using white/yellow headlamps, 

participants detected pedestrians from significantly farther away (M = 64.5 m (212 ft)) than when 

using white/blue headlamps (M = 56.7 m (186 ft), F=5.90, p<0.05) (figure 63). Headlamp color 

did not significantly affect color-recognition distances. When using white/yellow headlamps, 

participants recognized the clothing color from a mean of 53.0 m (174 ft) away, and when using 

white/blue headlamps, participants recognized the clothing color from 48.2 m (158 ft) away. The 

results are shown in figure 63.
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1 m = 3.3 ft

Figure 63. Chart. MPI system performance experiment—pedestrian-detection and colorrecognition distances versus headlamp color.

Headlamp Color and Pedestrian in Overhead Lighting

Headlamp color significantly affected detection distances when the pedestrian was in sections of 

the road with and without overhead lighting. In both cases, detection distances were greater with 

the white/yellow headlamps than with white/blue headlamps, as illustrated in figure 64. 

1 m = 3.3 ft

Figure 64. Chart. MPI system performance experiment—pedestrian-detection and colorrecognition distances versus headlamp color and overhead lighting.
88

The effects of headlamp color on detection and color recognition were robust enough to be 

replicated in different ambient-light scenarios. One would expect white/blue to have greater 

detection and color-recognition distances than white/yellow, because mesopic vision is more 

sensitive to blue. However, pedestrian detection was performed with participants free to move 

their heads and eyes. Thus, detection probably occurred in the fovea, less sensitive to mesopic 

effects than peripheral vision. The transmittance of the white/yellow filters was 49 percent, and 

the white/blue filters was 44 percent, making the white/yellow headlamps slightly brighter, 

perhaps causing the greater detection distances. 

MPI System Configuration

The MPI system was configured three ways: set to off, set to appear to track the pedestrian, or set 

to appear to highlight the pedestrian. 

The ANOVA revealed that the MPI system configuration’s effect on detection distance 

approached significance at the 0.10 level (F = 2.5, p = 0.1076). A Tukey HSD test found that 

pedestrians were detected from significantly farther away when the MPI system was off 

(M = 67.4 m (221 ft)) than when it highlighted (M = 56.7 m (186 ft)) or tracked pedestrians 

(M = 61.1 m (200 ft)), as shown in figure 65. That could be because the MPI system’s actions 

distracted the participant, making it more difficult to detect pedestrians.

MPI system configuration did not approach significance with respect to color-recognition 

distance (F = 0.52, p = 0.6037). 

1 m = 3.3 ft

Figure 65. Chart. MPI system performance experiment—pedestrian-detection and colorrecognition distance versus MPI system configuration.

MPI System Configuration and Pedestrian in Overhead Lighting

There was no significant effect on detection distance from the interaction between MPI system 

configuration and whether the pedestrian was in overhead lighting. In the area with no overhead 
89

lighting, pedestrian-detection distances were similar to the overhead-lighting condition for the 

three MPI system configurations. In the area with overhead lighting, the pedestrian-detection 

distance for the highlighting MPI system configuration (M = 51.4 m (169 ft)) was shorter than 

for the other two configurations (tracking (M = 61.2 m (201 ft)) and MPI off (M = 65.0 m 

(213 ft)), visible in figure 66, but not to a statistically significant degree.

There was also no significant effect on color-recognition distance from the interaction between 

MPI system configuration and whether the pedestrian was in overhead lighting. 

1 m = 3.3 ft

Figure 66. Chart. MPI system performance experiment—pedestrian-detection and colorrecognition distances versus MPI system configuration and overhead lighting.

MPI System Configuration and Headlamp Color

There was a statistically significant interaction between the headlamp color and MPI system 

configuration (F = 3.90, p < 0.05). When the MPI system was tracking the pedestrians, headlamp 

color had almost no effect on detection distance, which was about 61.0 m (200 ft). When the 

MPI system was off, mean detection distance for the white/yellow headlamps (M = 70.5 m 

(231 ft)) was slightly greater than for the white/blue headlamps (M = 63.6 m (209 ft)). The 

greatest effect was when the MPI system was highlighting the pedestrian. With the white/yellow 

headlamp, the mean detection distance (M = 63.7 m (209 ft)) was significantly greater than with 

the white/blue headlamp (M = 49.2 m (161 ft)). The results are shown in figure 67.
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1 m = 3.3 ft

Figure 67. Chart. MPI system performance experiment—pedestrian-detection and colorrecognition distances versus MPI system configuration and headlamp color.

MPI System Configuration and Pedestrian on Left or Right

There was no statistically significant effect on detection distance from the interaction between 

MPI system configuration and pedestrian on the left or right of the road. The shorter detection 

distances for the highlighting MPI system configuration were apparent whether the pedestrian 

was on the left of right side of the road. 

There was no statistically significant effect on color-recognition distance from the interaction 

between MPI system configuration and pedestrian on the left or right of the road. 

The mean detection and color-recognition distance for pedestrians on the left of the road with the 

MPI system off was visibly greater than the other means (figure 68). This could possibly be 

because of a combination of the vehicle’s blind spot on the left and the distracting headlamp 

action. Those two factors could have shortened detection and color-recognition distances when 

the pedestrian was on the left and the MPI system was on. 
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1 m = 3.3 ft

Figure 68. Chart. MPI system performance experiment—pedestrian-detection and colorrecognition distances versus MPI system configuration and pedestrian on left or right.

Pedestrian Clothing Color

Clothing color significantly affected detection distance. Pedestrians wearing red (M = 66.2 m 

(217 ft)) were detected significantly farther away than pedestrians wearing blue (M = 55.6 m 

(182 ft), p = 0.0015), as shown in Figure 69.

Clothing color also significantly affected color-recognition distance. Participants recognized the 

clothing color of pedestrians wearing red (M = 57.9 m (190 ft)) from significantly farther away 

than pedestrians wearing blue (M = 43.0 (141 ft), p = 0.0002), also shown in figure 69.
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1 m = 3.3 ft

Figure 69. Chart. MPI system performance experiment—pedestrian-detection and colorrecognition distances versus pedestrian clothing color.

Pedestrian Clothing Color and Pedestrian in Overhead Lighting

When detection distances were broken down by whether the pedestrian was in an area with 

overhead lighting, clothing color did not have a significant effect on detection distance for either 

overhead-lighting condition. With no overhead lighting, red-clothed pedestrians (M = 70.6 m 

(232 ft)) were detected from farther away than blue-clad pedestrians (M = 56.3 m (185 ft)). With 

overhead lighting, red-clothed pedestrians (M = 61.4 m (201 ft)) were also detected from farther

away than blue-clad pedestrians (M = 54.5 m (179 ft)), but this interaction of presence/absence 

of overhead lighting and pedestrian clothing color was not found to be statistically significant. 

The same trend held for color-recognition distances. With no overhead lighting, participants 

recognized the color of red-clothed pedestrians (M = 61.3 m (201 ft) from farther away than 

blue-clad pedestrians (M = 44.6 m (146 ft)). With overhead lighting, participants recognized the 

color of red-clothed pedestrians (M = 54.1 m (177 ft)) from farther away than blue-clad 

pedestrians (M = 41.0 m (134 ft)), but again, this was not statistically significant. Both detection 

and color-recognition distance results by pedestrian clothing color and overhead lighting are 

shown in figure 70.
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1 m = 3.3 ft

Figure 70. Chart. MPI system performance experiment—pedestrian-detection and colorrecognition distances versus pedestrian clothing color and overhead lighting.

Pedestrian on Left or Right

Pedestrians on the right side of the road (M = 62.6 m (205 ft)) were detected from farther away 

than pedestrians on the left side of the road (M = 58.7 m (193 ft)) but not to a statistically 

significant degree. 

Participants recognized the clothing color of pedestrians on the right side of the road 

(M = 53.2 m (175 ft)) from farther away than clothing color of pedestrians on the left side or the 

road (M = 47.7 m (157 ft)); this effect was also not statistically significant. Both effects are 

illustrated in figure 71.
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1 m = 3.3 ft

Figure 71. Chart. MPI system performance experiment—pedestrian-detection and colorrecognition distances versus pedestrian on left or right.

Pedestrians could be detected and their clothing colors recognized from farther away when they 

stood on the right-hand side of the road because of the headlamp beam pattern: the headlamp 

cutoffs that prevent light from creating glare for oncoming vehicles also prevent light from 

shining on the left-hand shoulder of the road and illuminating pedestrians there. 

Pedestrian in Overhead Lighting

The mean detection distance for pedestrians in areas with no overhead lighting (M = 63.1 m 

(207 ft)) was greater than the mean detection distance for pedestrians in areas with overhead 

lighting (M = 58.2 m (191 ft), p = .2430). An SNK test indicates that while these results are not 

statistically significant in terms of an analysis of variance, these mean detection distances were 

meaningfully different from each other, as shown in figure 72. Results of the mean colorrecognition distances were similar in terms of significance for with (M = 48.1 m (158 ft)) and 

without (M = 53.0 m (174 ft)) overhead lighting.
95

1 m = 3.3 ft

Figure 72. Chart. MPI system performance experiment—pedestrian-detection and colorrecognition distances versus pedestrian in overhead lighting.

It should be noted that for this comparison and the clothing color and overhead-lighting 

conditions, Stations with overhead lighting had more visual clutter than the stations without 

overhead lighting, and that clutter could have masked the pedestrians, as illustrated in figure 73. 

Therefore, the above results must be interpreted with caution. 
96

Figure 73. Photo. MPI system performance experiment—visual clutter on the Smart Road.

Detection Rate by Pedestrian Illuminated by MPI System

Some trials were performed with a pedestrian on the side of the road opposite the location where 

the MPI beam tracked to determine whether the MPI system interrupted the participants’ normal 

scanning behavior. 

When the MPI system illuminated the pedestrian, participants detected the pedestrian about 70 to 

80 percent of the time. When the MPI system illuminated the roadway opposite the pedestrian, 

participants detected the pedestrian about 50 to 60 percent of the time. The results, illustrated in 

figure 74, show that participant drivers either trusted the MPI system or expected the 

experimental configuration to be predictable. 
97

Figure 74. Chart. MPI system performance experiment—percentage of detections of 

pedestrian versus MPI system configuration and whether the pedestrian was illuminated 

by the MPI system.

Mean Fixation Duration by MPI System Configuration

When the MPI system was configured to track the pedestrian, it began illuminating the 

pedestrian when the vehicle was 183 m (600 ft) away and swiveled to keep the pedestrian 

illuminated as the vehicle approached. When the MPI system was configured to highlight the 

pedestrian, it shined on the pedestrian when the vehicle was 46.7 m (150 ft) away. 

Mean fixation duration per MPI system configuration was calculated, but the fixation duration 

data was not normally distributed, so comparing means to determine statistical significance was 

not valid. Therefore, a non-parametric statistical analysis, the Kruskal-Wallis test, was used to 

compare the groups of fixation durations between MPI system configurations. There was no 

statistically significant difference in fixation duration between the three MPI system 

configurations (p = 0.8040). The mean fixation durations by MPI system configuration are 

shown in figure 75.
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Figure 75. Chart. MPI system performance experiment—mean fixation duration versus

MPI system configuration. 

DISCUSSION

An objective of this project was to evaluate the effect of a peripheral illumination system on 

driver visibility. In the absence of overhead lighting, headlamps were the only source of light the 

driver had available to aid in object detection, and the effects of the MPI system were isolated. 

Results were mixed, with the MPI system increasing participant detection rates for pedestrians 

but decreasing detection distances. This result indicates that in a vehicle with an active MPI 

system, drivers rely on the MPI system to help them detect pedestrians. An MPI system that can 

detect and highlight pedestrians in the periphery better than human scanning alone would likely 

increase detection rates for those pedestrians. However, the MPI system may distract the driver’s 

attention from the roadway, because the driver would follow the moving headlamp beam. So 

while the system may distract in one case, it focuses the driver’s attention in another. Refocusing 

a driver’s attention may not always lead to better driving, as seen in the cases where the MPI 

headlamp beam was directed to a position opposite the pedestrian, reducing detection distances 

and rates for those pedestrians. 

In instances where pedestrians were located on the right side of the vehicle, the MPI highlighting 

and tracking configurations resulted in detection distances similar to when the MPI system was 

inactive. However, when pedestrians were located on the left side of the vehicle, the inactive 

MPI scenarios resulted in the longest detection distances. A possible explanation for this would 

be the design of the vehicle, where the A pillar may have impeded the driver’s view of the lefthand side of the roadway. With a moving headlamp, drivers would have a higher cognitive load 

trying to search for pedestrians around the A pillar than trying to search for them on the right 

side of the road with no obstruction,. 

A significant interaction took place between headlamp color and the behavior of the MPI system, 

resulting in findings that are more telling than those found in the scoping experiment, which had 

too few off-axis detections to draw firm conclusions. The findings from this experiment showed 
99

that the white/yellow headlamps might have allowed drivers to detect peripheral pedestrians 

from farther away, possibly indicating an effect of the light source’s SPD. 

CONCLUSIONS

The first objective of this experiment was to evaluate how MPI system configuration affected 

driver visual performance. Results showed that participants detected pedestrians from the 

greatest distances when the MPI system was off. That was the case for sections of the road both 

with and without overhead lighting. This could be because the MPI system’s moving beam 

distracted the participants and prevented them from scanning normally. Additionally, participants 

seemed to assume the MPI system would accurately illuminate pedestrians; they detected the 

pedestrian between about 70 to 80 percent of the time with the MPI system illuminating the 

pedestrian, compared with about 50 to 60 percent of the time with the MPI system illuminating 

the roadway opposite the pedestrian. This could have serious implications for an MPI system that 

failed to detect all pedestrians or malfunctioned to illuminate a “false-positive” pedestrian; 

drivers might assume the system was working and be less vigilant, causing more pedestrian 

crashes and casualties. 

Other research objectives of the experiment was to evaluate how headlamp color of the MPI 

system affected driver visual performance and how the MPI system and overhead lighting 

interacted to affect driver visual performance. The white/yellow headlamps had greater detection 

distances than white/blue headlamps in sections of the road with and without overhead lighting. 

That could be partially because the white/yellow gels had slightly higher transmittance than the 

white/blue gels (see table 3). Although mesopic vision is more efficient for blue than for yellow 

light, drivers were allowed to turn their heads and move their eyes as they scanned the road, and 

pedestrian detection occurred foveally where mesopic effects are minimal.

An eye tracker was used to determine how the MPI system affected driver eye-glance behavior, 

the last research objective of the experiment. There were no statistically significant differences 

among the different MPI system configurations for glance duration. 

FUTURE RESEARCH

The experiment described in this chapter was performed with a single overhead-lighting color 

with pedestrians placed at the same distance from the shoulder for each run. To better 

characterize mesopic vision and its interaction with an MPI system, future experiments should

use other overhead-lighting colors and add off-axis angles for object detection. The final 

experiment in this project takes these concerns into account. 

Drivers typically glance around the roadway as they drive. Future analyses should characterize 

exactly how an MPI system affects glance behavior. 

Participants apparently trusted the MPI system to the extent that they missed important visual 

information. Therefore, any MPI system would have to be accurate enough that it would seldom, 

if ever, miss detecting a pedestrian on the side of the road. 

101

CHAPTER 6. OVERHEAD-LIGHTING LEVEL EXPERIMENT

INTRODUCTION

The goal of the overhead-lighting level experiment was to investigate the interaction of 

overhead-lighting level and vehicle headlamps. The performance of a lighting system is based on 

the adaptation level of the driver, so it is critical to identify whether the dominant component of a 

driver’s visual field is the vehicle headlamps or the overhead-lighting system. This experiment 

was designed to investigate the relative dominance of overhead lighting and headlamps with 

regard to visibility.

Only the 6,000-K LED lighting was used in this experiment because, of the three lighting types 

used in this project, its spectrum was the most efficient in the mesopic region. Consequently, it 

provided the best visibility at the lowest illuminance. Eleven lighting levels were tested, ranging 

from off to the 100 percent. 

Pavement type, color, and reflectance all affect object contrast and visibility. Therefore, 

experiments were performed on two types of pavement: lighter-colored concrete and darker 

asphalt. The position of the target with respect to the overhead-lighting masts also affects 

visibility. By combining target placement with respect to the luminaire and pavement type, two 

worst-case scenarios for target visibility were created to test minimum VLs. In the first case, a 

target was placed on dark asphalt in a position with low VI. In the second case, a target was 

placed on light concrete in a positon with high VI. Pedestrians were also used to test visibility,

representing a real-life dangerous driving scenario. 

To get a detailed picture regarding visibility at the various lighting levels, both targets and 

pedestrians were used to measure detection distance. Only gray clothing and targets were used, 

because this experiment focused on overhead-lighting level and mesopic vision, not object color. 

Participants detected the targets and pedestrians. To add a level of detail, participants were also 

asked to recognize which direction the targets and pedestrians were facing. 

Tests were performed with headlamps on to simulate real-life driving scenarios and with 

headlamps off to examine the effect of overhead lighting alone. 

Research Objectives

The research objectives of the overhead-lighting level experiment were to evaluate the following: 

• Impact of headlamps driver visual performance.

• Impact of overhead-lighting level on driver visual performance.

• How headlamps and overhead lighting interact to affect driver visual performance.

• Minimum overhead-lighting level where driver visual performance is not 

compromised.
102

In addition to the stated research objectives, the results of this experiment informed the 

remaining experiments regarding adaptation luminance in mesopic conditions. This information 

was the basis for light levels and object locations used in subsequent experiments in this project.

EXPERIMENTAL DESIGN

An experiment was designed to measure the relationship of overhead-lighting level and 

headlamps on nighttime driving visual performance in mesopic conditions. Variables used in the 

experiment are listed in table 20 and table 21.

Table 20. Overhead-lighting level experiment independent variables.

Variable Type Variable Options

Age Younger (25–35), Older (65+)

Vehicle Headlamps On, Off

Overhead-Lighting Level (percent) Off, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100

Target Type Target I (low VI), target II (high VI), pedestrians

Table 21. Overhead-lighting level experiment dependent variables.

Dependent Variable

Maximum Times 

Measured Per Participant

Target I Detection Distance 21

Target II Detection Distance 21

Pedestrian-Detection Distance 21

Target I Orientation-Recognition Distance 21

Target II Orientation-Recognition Distance 21

Pedestrian Orientation-Recognition Distance 21

Object Luminance and Contrast N/A

N/A = Not applicable.

All variable combinations were tested except for the headlamp-off and the overhead-lighting-off 

conditions together. In that combination, no lighting would be present, which would present a 

significant safety hazard.

Independent Variables

Age

Participants were divided into the same age groups as in previous experiments: drivers 

(25–35 years old) and older drivers (65 years old and older). These ages were selected on the 

basis of visual ability and driving experience. Younger drivers typically have better vision for 

driving at night, while the older population has more experience with driving in general.

Vehicle Headlamps

Vehicle headlamps were either turned on or off; when on, they were used with the neutraldensity low-intensity filters described in chapter 3. 
103

Overhead-Lighting Level

The overhead roadway lighting used was the 6,000-K LED system. Dim levels varied in 

10-percent intervals from 0-percent illuminance to 100-percent illuminance. The horizontal 

illuminances were measured on the roadway surface at the target II location. (Target positioning 

will be described in the next section.) The 100-percent level was 10 lx (0.93 fc). The 10-percent 

level was actually 1.37 lx (0.13 fc) because the control system and luminaire characteristics do 

not allow for lower dimming levels. The headlamps used were the white/yellow filters with the 

30-percent neutral density filters. Vehicle headlamps were either turned on or off. 

Target Type

This experiment used gray-clad pedestrians and gray targets. The pedestrians were positioned on

the shoulder, 0.03 m (1 ft) away from the road, on the same side of the road as the overheadlighting system. The targets were placed on the shoulder, also 0.03 m (1 ft) away from the road 

but on the opposite side of the road from the overhead lighting.

The target placement longitudinally along the roadway was selected to produce worst-case 

scenarios in terms of target contrast—one with high VI and one with low VI. The pedestrians 

were located so that the VI at head level matched that of the high VI targets. Target and 

pedestrian placement is described in detail in the following paragraphs. 

Target I Placement:

The placement of target I attempted to create a low VI condition. It was placed on a dark asphalt 

surface and positioned directly across from a luminaire (figure 76), so very little light from the 

luminaire shone directly on the face visible to the oncoming vehicle.
104

Figure 76. Diagram. Overhead-lighting level experiment—target I (low VI) placement.

Target II Placement: 

The placement of target II attempted to create a high VI condition. It was placed on a light 

asphalt surface halfway between two luminaires (figure 77), the area of maximum VI with 

respect to the luminaire, and as much light as possible shone on the face visible to the oncoming 

vehicle. 
105

Figure 77. Diagram. Overhead-lighting level experiment—target II (high VI) placement.

Pedestrian Placement:

The placement of the pedestrian with respect to the luminaires was chosen so that the VI at 

approximately face height, about 1.5 to 1.8 m (5 to 6 ft), matched the VI at target II, the high-VI

target. 

To do so, first the VIs at target II’s position were measured. Measurements were repeated with 

the overhead lighting varying in 10-percent intervals from 0 to 100 percent. Then, VI

measurements were taken at various locations on the same side of the road as the luminaires and 

at 1.5 to 1.8 m (5 to 6 ft) (head height). The pedestrian was positioned where these VI

measurements were approximately equal to those at target II for the same illumination condition. 

Table 22 lists illuminance measurements at the target I, target II, and the best-match pedestrian 

position. Figure 78 graphs those illuminances.
106

Table 22. Overhead-lighting level experiment—VI of targets and pedestrian with 

luminaires and no headlamps.

Dim Level of 

Luminaire Target I VI (lx (fc)) Target II VI (lx (fc)) Pedestrian VI (lx (fc))

100 percent 1.6 (0.15) 5.73 (0.53) 6.75 (0.63)

90 percent 1.5 (0.14) 5.68 (0.53) 5.92 (0.55)

80 percent 1.3 (0.12) 4.79 (0.44) 5.45 (0.51)

70 percent 1.07 (0.099) 4.06 (0.38) 4.53 (0.42)

60 percent 0.98 (0.091) 3.44 (0.32) 3.8 (0.35)

50 percent 0.83 (0.077) 2.85 (0.26) 3.21 (0.30)

40 percent 0.67 (0.062) 2.29 (0.21) 2.59 (0.24)

30 percent 0.5 (0.046) 1.67 (0.16) 1.96 (0.18)

20 percent 0.34 (0.032) 1.11 (0.103) 1.3 (0.12)

10 percent 0.27 (0.025) 0.79 (0.073) 0.88 (0.082)

Figure 78. Graph. Overhead-lighting level experiment—VI on objects by overhead-lighting 

level. 

Dependent Variables 

Detection and orientation-recognition distances were measured in this experiment. Colorrecognition distance was not measured; all objects were gray. 
107

Object Luminance and Contrast

Photometric images of the pedestrians and targets were taken post hoc with a calibrated 

ProMetric® Radiant Imaging® camera mounted inside the test vehicle. Images were taken with 

headlamps on and off. For the targets, photometric images were recorded every 15 m (50 ft) out 

to 122 m (400 ft) from the target. For pedestrians, images were recorded every 30 m (100 ft) out 

to 244 m (800 ft) from the pedestrian. 

The images taken were analyzed using Radiant Imaging® software. A data reductionist traced 

the contour of the pedestrian or target, and the software calculated the average luminance within 

that polygon. The luminances of the targets, pedestrians, and their backgrounds were measured 

to calculate contrast.

METHODS

Participants

Twenty-four participants took part in the study. However, data for two of the participants were 

unusable because an anomaly during data collection rendered that data unreliable. Of the 

22 participants whose data were kept, 12 were in the older group, and 10 were in the younger 

group. There were 11 males and 11 females. Mean and standard deviation of participant age, 

visual acuity, mesopic visual acuity, and low contrast visual acuity are listed in table 23. 

Table 23. Overhead-lighting level experiment participant characteristics.

Participant Characteristic

Older 

Drivers

Mean

Older 

Drivers

Standard 

Deviation

Young 

Drivers

Mean

Younger 

Drivers

Standard 

Deviation

Age 67.4 1.3 25.6 1

Visual Acuity 20/21.2 4.9 20/16.2 5.8

Mesopic Visual Acuity 20/36.3 14.1 20/23.5 6.3

Low Contrast Visual Acuity 20/29.8 7.2 20/21.2 6.8

UFOV 1.4 0.5 1.2 0.4

Procedure

Data Collection

Upon arrival for an experimental session, participants were directed to the test vehicles, 

familiarized with their operation and the experiment, and then asked to proceed to the Smart 

Road for the experiment. As in previous experiments, two participants in two vehicles completed 

the experiment at one time. Participants drove 22 laps on the Smart Road, including one practice 

lap. One lap was from turn 2 to turn 3, as shown in figure 23. In the remaining 21 laps, the same 

objects (targets and pedestrians) were in the same location for each lap. Participants were told the 

objects would be in the same position for each lap and were reminded to say when they could see 

the objects and object orientations, not when they expected to see the objects. On-road 

researchers changed the overhead-lighting configuration and object orientations, and in-vehicle 
108

researchers turned the headlamps on and off and recorded detection and orientation-recognition. 

There were no laps without headlamps and without overhead lighting to avoid creating 

hazardous test conditions.

Data Analysis

After video reduction of the data, an ANOVA was conducted for analyses related to detection 

and orientation-recognition distances. An ANCOVA was conducted for analyses related to 

contrast.

The photometric results were also analyzed in terms of contrast and average detection distance 

for the objects.

RESULTS

Lighting Characteristics

The distance at which headlamps contribute illuminance to a small target was determined by 

measuring VI using a light meter placed in front of the target at ground level with the vehicle 

headlamps on. Luminaire dim levels did not noticeably affect the VI at the target while 

headlamps were on. When the vehicle was within 91.4 m (300 ft) of the target, there was only 

about a 10-lx (0.93 fc) difference in VI between no overhead lighting and 100-percent overhead 

lighting at each distance. VI measurements by distance from target are graphed in figure 79.
109

1 m = 3.3 ft

Figure 79. Graph. Overhead-lighting level experiment—VI at target, vehicle with 

headlamps on at between 91 and 8 m (300 and 25 ft) from target, luminaires from 0 to 

100 percent.

Detection Distance Versus Lighting Conditions

The data were analyzed using a mixed-models analysis to determine significant effects of 

headlamp lighting condition and overhead-lighting level on detection and orientation-recognition 

distances. 

Significant Results Summary

Separate analyses were run for the targets and the pedestrian to isolate the effects of lighting. 

Overhead-lighting level significantly affected pedestrian-detection distances. Overhead-lighting 

level significantly affected detection distance for both targets, as did headlamp condition, but 

there was no effect from interaction between the two (table 24).
110

Table 24. Overhead-lighting level experiment—significant effects on detection distance of 

targets and pedestrian. 

Object Effect F-value Pr > F

Pedestrian Headlamps on/off 0.39 0.5315

Pedestrian Overhead-lighting level 18.65 < 0.0001a

Pedestrian Headlamps by overhead-lighting level 1.34 0.2153

Target I Headlamps on/off 27.52 < 0.0001a

Target I Overhead-lighting level 4.41 < 0.0001a

Target I Headlamps by overhead-lighting level 0.14 0.9985

Target II Headlamps on/off 13.93 0.0002a

Target II Overhead-lighting level 3.7 < 0.0001a

Target II Headlamps by overhead-lighting level 0.34 0.9603 a

Significant at p < 0.05.

Pr = Probability.

Detection Distance Versus Overhead-Lighting Level

An SNK test was conducted to isolate the extent that overhead-lighting level affected detection 

distance, with headlamps off and with headlamps on, for the targets and pedestrians. In the 

figures reporting results of SNK tests, bars sharing a letter do not significantly differ from each 

other. 

Pedestrian, Headlamps Off:

For pedestrian detection with headlamps off, 100-percent overhead-lighting level provided the 

greatest mean detection distance. However, although lighting levels of 50 through 90 percent

were not significantly different from 100 percent, they had mean detection distances significantly 

greater than for the 10- to 30-percent overhead-lighting levels. The same was true for the 

dimmest levels; 10 percent had the shortest mean detection distance, but this was not 

significantly different from 20- and 30-percent overhead lighting. The general trend was for 

increasingly intense overhead lighting to correspond with greater mean detection distances, 

except for 30 percent, which had an unexpectedly short mean detection distance given the 

general trend. Those results are shown in figure 80.
111

1 m = 3.3 ft

Figure 80. Chart. Overhead-lighting level experiment—SNK groupings for pedestriandetection distance with headlamps off by overhead-lighting level.
112

Pedestrian, Headlamps On: 

For pedestrian detection with headlamps on, the SNK results show a sharp decrease in mean 

detection distance when the overhead-lighting level drops from 30 to 20 percent (figure 81).

1 m = 3.3 ft

Figure 81. Chart. Overhead-lighting level experiment—SNK groupings for pedestriandetection distance with headlamps on by overhead-lighting level.
113

Target I, Low VI, Headlamps Off: 

There were no significant differences in target I mean detection distances for the different 

overhead-lighting levels, but a trend of greater mean detection distances with greater overhead 

lighting is apparent (figure 82).

1 m = 3.3 ft

Figure 82. Chart. Overhead-lighting level experiment—SNK groupings for target I

detection distance with headlamps off by overhead-lighting level.
114

Target I, Low VI, Headlamps On:

The target I mean detection distances were significantly different for the different overheadlighting levels when headlamps were on. There was a significant difference in mean detection 

distances between 100 and 10 percent and between 0 and levels 80 percent and higher, as 

illustrated in figure 83.

1 m = 3.3 ft

Figure 83. Chart. Overhead-lighting level experiment—SNK groupings for target I

detection distance with headlamps on by overhead-lighting level.
115

Target II, High VI, Headlamps Off:

The mean detection distances for target II with headlamps off did not show a strong trend of 

greater detection distance with more intense overhead lighting. The 80-percent overhead-lighting 

level had a significantly greater mean detection distance than the 30-percent level, as shown in

figure 84, but neither differed significantly from any of the other lighting levels. 

1 m = 3.3 ft

Figure 84. Chart. Overhead-lighting level experiment—SNK groupings for target II

detection distance with headlamps off by overhead-lighting level.
116

Target II, High VI, Headlamps On:

The mean detection distances for target II with no headlamps are similar to those with 

headlamps; the 80-percent level had a significantly greater mean detection distance than the 30-

percent level, but neither differed significantly from any of the other overhead-lighting levels 

(figure 85).

1 m = 3.3 ft

Figure 85. Chart. Overhead-lighting level experiment—SNK groupings for target II

detection distance with headlamps on by overhead-lighting level.
117

Detection Distances Versus Headlamps On or Off

Mean detection distances were greater when the vehicle’s headlamps were off for all objects—

target I, target II, and the pedestrian—although the difference was not significant for the 

pedestrian. Those differences are shown in figure 86.

1 m = 3.3 ft

Figure 86. Chart. Overhead-lighting level experiment—mean detection distance for all 

objects by headlamp on/off.
118

Detection Distance Versus Participant Age

Young subjects detected the pedestrians and both targets at significantly greater mean distances 

than older participants, when all overhead-lighting levels and headlamp condition were 

combined. These results, illustrated in figure 87, do not differentiate between the different 

lighting levels.

1 m = 3.3 ft

Figure 87. Chart. Overhead-lighting level experiment—mean detection distance for all 

objects by participant age and lighting conditions combined.

Comparison Among Pedestrian, Target I, and Target II Detection Distances

Most pedestrian-detection distances were from more than 122 m (400 ft), but the test vehicle 

headlamps only contributed to object illumination at between 76.2 and 107 m (250 and 350 ft) 

away, so overhead lighting, more than headlamps, drove pedestrian visibility. Pedestriandetection distances were also significantly greater with headlamps off. In addition, detection 

distances for the pedestrians were not significantly different from each other for overheadlighting intensities between 60 and 100 percent, as shown in figure 81, figure 82, and figure 88. 

This shows there might be an opportunity to conserve energy without reducing pedestrian safety. 
119

The target I (low VI) mean detection distances tended to be greater than those for target II (high 

VI), with headlamps both on and off, because target I had greater contrast with the background 

than did target II (figure 89). Target detection distances tended to increase with increasing 

overhead-lighting levels in both headlamp conditions, but the increase was more erratic with 

target II because of its low contrast and poor general visibility to the participants. Targets were 

consistently detected from farther away with headlamps off, likely because the headlamps 

washed out the background behind the target, reducing contrast. 

1 m = 3.3 ft

Figure 88. Chart. Overhead-lighting level experiment—mean detection distances for 

pedestrians by overhead-lighting level.
120

1 m = 3.3 ft

Figure 89. Chart. Overhead-lighting level experiment—mean detection distances for 

targets by overhead-lighting level.

Orientation-Recognition Distance

Object orientation was randomized, unlike detection distance, because the targets were kept at 

the same place on the roadway. Because it was randomized, orientation-recognition distance is 

less likely to have participant-created error. 

Pedestrian

Orientation-recognition distances varied more widely with overhead-lighting level with 

headlamps off than with headlamps on, because without headlamps, overhead-lighting level 

alone drove visibility (figure 90). At overhead-lighting levels between 0 and 30 percent and no 

headlamps, the pedestrians were less visible, resulting in shorter detection distances. At those 

same overhead-lighting levels, however, and with headlamps on, pedestrian orientation was 

recognized from farther away—about 91.4 m (300 ft)—the limit of the headlamp’s range. 

Therefore, headlamps create a boundary at about 91.4 m (300 ft) where objects will most likely 

be visible. Adding overhead lighting increased orientation-recognition distances beyond that 

300 ft (91.4 m).
121

Orientation-recognition distances for the pedestrian varied widely, and that variance was even 

wider at higher overhead-lighting levels, whether or not the headlamps were on.

1 m = 3.3 ft

Figure 90. Chart. Overhead-lighting level experiment—mean recognition distance of 

pedestrian by headlamps on and off and overhead-lighting level.
122

Target I

Figure 91 shows that for target I, the span of orientation-recognition distances with headlamps 

off and 10-percent overhead lighting was from 15 to 125 m (50 to 500 ft). With headlamps on, 

that span was from 24 to 61 m (80 to 200 ft). 

With headlamps off and high overhead-lighting levels, participants recognized target orientation 

from distances beyond where headlamps normally illuminate. With headlamps on and overheadlighting levels at 30 percent and below, participants recognized targets from farther away.

Therefore, headlamps helped drivers see targets at those lower lighting levels. 

1 m = 3.3 ft

Figure 91. Chart. Overhead-lighting level experiment—mean recognition distance of 

target I by headlamps on and off and overhead-lighting level.
123

Target II

The orientation-recognition distances for target II vary widely with headlamps on and off at 

between 100- and 0-percent overhead lighting (figure 92). One trend is that with headlamps on 

and with between 20- and 50-percent overhead lighting, participants recognized target II 

orientation from farther away than with headlamps off. 

1 m = 3.3 ft

Figure 92. Chart. Overhead-lighting level experiment—mean recognition distance of 

target II by headlamps on and off and overhead-lighting level.

Luminance

The luminance and contrast measures were measured to determine whether detection distance 

correlated with changes in contrast. If so, photometric measurements could be used to predict 

driver visual behavior.

Pedestrian 

Pedestrian-detection distances differed significantly with the 100-, 50-, and 1- percent overheadlighting levels, so those lighting levels were chosen for the luminance analysis. Luminance for 

those overhead-lighting levels was measured from 30.4 to 244 m (100 to 800 ft) in 15.2-m 
124

(100-ft) intervals. Results are shown in figure 93 and described following the figure. Log 

luminance is used to better show the differences in the conditions without headlamps.

1 cd/m2 = 0.3 fL

1 m = 3.3 ft

Figure 93. Graph. Overhead-lighting level experiment—pedestrian log luminance by 

overhead-lighting level, headlamp condition, and distance.

Overhead Lighting at 100 Percent: 

The luminance between 213 and 244 m (700 and 800 ft) was 0.096 cd/m2 (0.028 fL) higher with 

headlamps on, showing some headlamp illumination was reaching the pedestrian even at 244 m 

(800 ft), probably via reflection off the guard rails and roadway surface. Headlamps illuminated

the pedestrian more directly beginning at 122 m (500 ft), and then luminance increased sharply at 

91.4 m (300 ft) from the pedestrian, where the headlamps began to directly illuminate them. 

Overhead Lighting at 50 Percent:

Dimming the overhead lighting from 100 to 50 percent affected the pedestrian luminance; at 

244 m (800 ft) and without headlamps, the pedestrian luminance at 50-percent overhead lighting 

(0.145 cd/m2 (0.042 fL)) was half that at 100-percent overhead lighting (0.288 cd/m2 (0.084 fL)). 

With headlamps, the luminance at 50-percent overhead lighting was 58 percent (0.226 cd/m2
125

(0.066 fL)) of that at 100-percent overhead lighting (0.384 cd/m2 (0.112 fL)), showing the 

percent contribution of headlamp light to luminance was different for the various overhead 

lighting intensities. This could be because at 100-percent overhead lighting level, the overhead 

lighting drowned out the headlamps’ effect. Also, slightly different vehicle and pedestrian 

positions between measurements could affect luminance measurements. 

Overhead Lighting at 10 Percent:

Dimming the overhead lighting from 100 to 10 percent affected the pedestrian luminance; at 

244 m (800 ft) and without headlamps, the pedestrian luminance at 10-percent overhead lighting 

(0.045 cd/m2 (0.013 fL)) was 15 percent of that at 100-percent overhead lighting (0.288 cd/m2

(0.084 fL)) with the headlamps off. With headlamps, the luminance at 10-percent overhead 

lighting was 35 percent (0.1375 cd/m2 (0.040 fL)) of that at 100-percent overhead lighting 

(0.384 cd/m2 (0.112 fL)). This shows that headlamp illumination affected pedestrian luminance 

at that distance.

Another important aspect of this comparison is that the headlamp seemed to dominate the target 

light level at 152 m (499 ft), where the target luminances from each of the dim levels began to 

converge.

Target I (Low VI)

The 40-percent overhead-lighting level had considerably shorter detection distances than more

intense levels and appears to be the level where overhead lighting and headlamp light contribute 

similarly to luminance. Therefore, the 100- and 40-percent levels were selected for luminance 

analyses. Luminance measurements were taken from 15.2 to 122 m (50 to 400 ft) in 15.2-m 

(50-ft) intervals, and the results are shown in figure 94.
126

1 cd/m2 = 0.3 fL

1 m = 3.3 ft

Figure 94. Graph. Overhead-lighting level experiment—target I luminance by overheadlighting level, headlamp condition, and distance.

Results for 100- and 40-percent overhead lighting were similar, and headlamp condition and 

vehicle distance to target both affected target I’s luminance. For headlamps on and off, the 

targets had nearly equal luminance between 91.4 and 122 m (300 and 400 ft). At 76.2 m (250 ft) 

and closer, the luminances diverged, with target I in the headlamp-on condition brighter than in 

the headlamp-off condition. Luminances were slightly greater with headlamps on at 100-percent 

overhead lighting than those with headlamps on at 40-percent overhead lighting at distances 

closer than 60 m (200 ft).
127

Target II (High VI)

Like target I, the luminance and contrast of target II were analyzed at 100- and 40-percent 

overhead-lighting levels. Results are illustrated in figure 95. 

1 cd/m2 = 0.3 fL

1 m = 3.3 ft

Figure 95. Graph. Overhead-lighting level experiment—target II luminance by overheadlighting level, headlamp condition, and distance.

Results for 100- and 40-percent overhead lighting were similar, and headlamp condition and 

vehicle distance to target both affected the target II’s luminance. In both overhead-lighting 

conditions, target luminance with headlamps on was greater than with headlamps off, even at 

longer distances. Like target I, luminance increased rapidly as measurements were taken from 

60 m (200 ft) and closer with the headlamps on because headlamp light contributed to 

luminance. At greater distances, headlamp contribution to luminance was smaller for target II, 

but the headlamps-on and headlamps-off curves never converged, showing that even at great 

distances, headlamps contributed slightly to target luminance.
128

DISCUSSION

Although this experiment did not specifically evaluate models of mesopic vision—a project 

objective—its results provide insight on human visual behavior while driving in mesopic 

conditions as a method to determine the adaptation level of the driver, which is an input to the 

mesopic model. Pedestrians and small roadside targets were largely visible to participants even 

in low contrast conditions and without headlamps. Dimming the roadway lighting did not affect 

luminance ratio and contrast of objects, but object visibility, as measured by detection and 

object-recognition distances, did change depending on roadway lighting dim level. At 40-percent

overhead lighting and below, detection distances were much shorter than at higher lighting 

levels. At 30-percent overhead lighting and below, orientation-recognition distances were much 

shorter. The range of orientation-recognition distances narrowed considerably at 30-percent

overhead lighting and below. Therefore, participants were able to detect and recognize lowcontrast objects, but that ability dropped off at reduced overhead lighting. 

Viewing angle affected object visibility, as did the angle of light on the object. Object

illuminance combined with contrast affected visibility in terms of quantity, in accordance with

Adrian’s model.

(54)

Contrast polarity also affected object visibility. The negatively contrasting target—target I—had 

greater detection distances than the positively contrasting target—target II. Adrian’s model 

accounts for the tendency for negatively contrasting objects to have higher visibility.(54)

However, when the vehicle approached the negatively contrasted target, the headlamps 

illuminated it, increasing its luminance with respect to the background. As the vehicle 

approached, the target contrast was close to zero for a period of time, rendering it invisible. 

When the vehicle was close enough, the headlamps illuminated the target enough to produce 

positive contrast. Thus, negatively contrasted objects are initially more visible than positively

contrasted objects, but as a vehicle with headlamps approaches them, there is a span of distance 

where the object has low contrast and very poor visibility. Positively contrasted objects only 

increase in contrast as a vehicle approaches. 

Zones of Visibility

Headlamps and roadway lighting combine to form three zones of visibility for object detection. 

These zones, introduced by Boyce, include near, intermediate, and far categories.(95) The near 

zone is where visibility is dominated by headlamp light. The intermediate zone is where 

headlamp and overhead lighting combine to affect visibility. The far zone is where overhead 

lighting alone contributes to visibility. 

The zones of visibility identified in this experiment for pedestrians and targets are listed in

table 25. The near zone for target visibility was aligned with previous research that found 

headlamps were the dominant light source up to approximately 40 m (130 ft).(95) The differences 

in visibility zones between the pedestrian and the targets are because they are different sizes and 

are different distances from the ground. The boundaries for the intermediate zone for pedestrians 

were not as clear from the data because the pedestrian contrast polarity shift was more gradual 

than that of the targets.
129

Table 25. Overhead-lighting level experiment—zones of visibility for pedestrian and 

targets.

Zone Pedestrian Targets

Near Zone 0 to 91.4 m 0 to 30 m

Intermediate Zone 91.4 to 152 m 30 to 76.2 m

Far Zone 152 m and beyond 76.2 m and beyond

1 m = 3.3 ft

Stopping Sight Distance

Lighting design should consider stopping sight distance, because illuminated objects must be 

visible from far enough away for drivers to stop. The detection distances were assumed to be 

stopping sight distances, and vehicle speeds corresponding to those distances were calculated 

using the stopping sight distance formula. The following calculations take into account both 

headlamp and overhead light, meeting a project objective of evaluating influence of the 

interaction of vehicle headlamps and overhead lighting on driver visual performance. The results 

are listed in table 26.
130

Table 26. Overhead-lighting level experiment—object stopping sight distance and 

corresponding speed.

OverheadLighting 

Level

(percent)

Headlamp 

Condition

Pedestrian

Stopping 

Sight 

Distance

(m)

Pedestrian

Rec. Speed

(km/h)

Target I 

Stopping 

Sight 

Distance

(m)

Target 

I Rec. 

Speed

(km/h)

Target II 

Stopping 

Sight 

Distance

(m)

Target 

II Rec. 

Speed

(km/h)

0 ON 76 56 24 24 24 24

10 OFF 24 24 24 24 35 32

10 ON 76 56 24 24 24 24

20 OFF 35 32 24 24 — < 24

20 ON 76 56 24 24 24 24

30 OFF — < 24 — < 24 — < 24

30 ON 76 56 — < 24 — < 24

40 OFF 61 48 24 24 — < 24

40 ON 93 64 — < 24 — < 24

50 OFF 130 80 — < 24 — < 24

50 ON 93 64 — < 24 — < 24

60 OFF 130 80 — < 24 — < 24

60 ON 130 80 24 24 — < 24

70 OFF 110 72 24 24 — < 24

70 ON 76 56 — < 24 — < 24

80 OFF 151 88 24 24 24 24

80 ON 151 88 — < 24 — < 24

90 OFF 93 64 24 24 — < 24

90 ON 110 72 24 24 24 24

100 OFF 110 72 35 32 — < 24

100 ON 110 72 — < 24 — < 24

Rec. = Recognition.

— Indicates data were not adequate to provide a meaningful result.

1 km/h = 0.62 mi/h

1 m = 3.3 ft

There are many instances in which the minimum detection distance was shorter than suggested 

by stopping sight distance at a typical speed limit of about 24 km/h (15 mi/h). The implication is 

that, for those situations, drivers typically cannot see objects in time to stop. That was the case 

for many conditions using the targets but not so with the pedestrian, where drivers could travel as 

fast as 88 km/h (55 mi/h) and still see a pedestrian in time to stop. (Most roadways with 

pedestrians have speed limits of 72 km/h (45 mi/h) or lower.) The results for target detection 

suggest that headlamp and overhead-lighting design might be inadequate for small-object 

detection. 
131

Weber Contrast and Detection Distance

Pedestrian

Weber contrasts for the pedestrians and targets were calculated using the same procedure 

outlined in the scoping experiment. Weber contrast results were related to detection distances 

and are examined in the discussion. 

Overhead Lighting at 100 Percent: The contrasts calculated from the images of the pedestrian 

are shown in figure 96. Overlaid on the contrast curves are points at which the participant 

detected the pedestrian. Between 152 and 213 m (500 and 700 ft), the curves slightly diverge, 

with pedestrians and headlamps on having more contrast. At those greater distances, the

headlamps illuminate the roadway in front of the vehicle, causing the eye to adapt to the brighter 

foreground and causing the background and pedestrian to appear darker. 

1 m = 3.3 ft

Figure 96. Graph. Overhead-lighting level experiment—pedestrian contrast and detection 

distance at 100-percent overhead lighting. 

The contrast curves diverge more sharply at distances closer than 122 m (500 ft), where the 

headlamps begin to indirectly illuminate the pedestrian via reflections from the pavement and 

guardrails, and very sharply at 91.4 m (300 ft) from the pedestrian, where the headlamps begin to 

directly illuminate the pedestrian. 
132

Changing contrast as the vehicle approaches the pedestrian did not appear to affect detection 

distance because the average detection distance with headlamps off was 285 m (925 ft) and with 

headlamps on was 250 m (819 ft), distances with very low measured luminance contrast. This 

level can be determined to be the threshold contrast for the object. (Note 100-percent detection 

threshold, not 50-percent detection as typically referenced.) The participants could be detecting 

pedestrians from farther away without headlamps because headlamps raise the ambient light 

level, causing the eye to adapt to a higher light level and be less sensitive. It would also be 

because participants tend to not look beyond the area their headlamps illuminate and do not scan 

as far ahead with headlamps on. Both detection distances are sufficient for a driver to stop a 

vehicle traveling at 121 km/h (75 mi/h) before arriving at the pedestrian; however, while it is 

unlikely a road with that speed limit would have pedestrians, other hazards, such as wildlife, 

might be present. 

Overhead Lighting at 50 Percent: The Weber contrasts of the images were similar to those 

taken at 100-percent overhead-lighting level, because dimming the overhead lighting dims the 

entire scene and changes the luminance difference, while the luminance ratio is unchanged. 

Average detection distances were more than 244 m (800 ft) with 100-percent overhead lighting 

but are just over 213 m (700 ft) with 50-percent overhead lighting with headlamps on or off. A

50-percent reduction in overhead-lighting output shortened the detection distances by 21 percent

without headlamps and 14 percent with headlamps. Results are shown in figure 97.

1 m = 3.3 ft
133

Figure 97. Graph. Overhead-lighting level experiment—pedestrian contrast and detection 

distances at 50-percent overhead lighting.

Overhead Lighting at 10 Percent:

The Weber contrast curves for 10-percent overhead lighting are similar to those for the other 

lighting levels, but the average detection distances are different (figure 98). 

1 m = 3.3 ft

Figure 98. Graph. Overhead-lighting level experiment—pedestrian contrast and detection 

distances at 10-percent overhead lighting.

Target I (Low VI)

Weber contrast calculations were performed for the targets at 100-percent and 40-percent 

overhead lighting levels. The 40-percent overhead lighting level had considerably shorter 

detection distances than more intense levels and appears to be the level at which overhead 

lighting and headlamp light contribute similarly to luminance. Therefore, those lighting levels 

were selected for contrast analyses. 
134

Overhead Lighting at 100 Percent:

Images of target I with 100-percent overhead lighting taken from various distances are shown in 

figure 99 to illustrate that, between 45.7 and 76.2 m (150 and 250 ft), the contrast changes from 

positive to negative. At 61.0 m (200 ft), the 100-percent overhead lighting creates negative 

contrast by illuminating the road behind the target, and the headlamps create positive contrast by 

illuminating the target. The target goes through a stage where it is invisible, and the contrast 

shifts from negative to positive and crosses the point of zero contrast.

Figure 99. Image Table. Overhead-lighting level experiment—images of target I at 

100-percent overhead lighting for from 15.2 to 123 m (50 to 400 ft) to the target.

The Weber contrasts for target I at 100-percent overhead lighting, with and without headlamps, 

are shown in figure 100. With the headlamps on (solid line), the shift from negative to positive 

contrast can be seen as the line passes through zero contrast. Detection distances, indicated by 

squares and diamonds on the two lines, are widely spread. When the vehicle was 76.2 to 122 m 

(250 to 400 ft) away, the contrasts, for headlamps off and on, like the luminances, were very 

similar. 
135

1 m = 3.3 ft

Figure 100. Graph. Overhead-lighting level experiment—target I contrast and detection 

distance at 100-percent overhead lighting.

Overhead Lighting at 40 Percent:

Images of target I at various distances with 40-percent overhead lighting with and without 

headlamps are shown in figure 101. The ambient luminance is darker than at 100-percent 

overhead lighting. The same change from positive to negative contrast between 45.7 and 76.2 m 

(150 and 250 ft) is apparent in these images, as it was in the images with 100-percent overhead 

lighting. 
136

Figure 101. Image Table. Overhead-lighting level experiment—images of target I at 

40-percent overhead lighting for from 15.2 to 123 m (50 to 400 ft) to the target.

Weber contrast and detection distances for target I in 40-percent overhead lighting are shown in

figure 102. The average detection distance with headlamps on is shorter than with headlamps off, 

which could be because the headlamps cause the participant’s eye to adapt to higher luminance, 

reducing contrast sensitivity. Also, at 40-percent overhead lighting, because headlamp light 

dominates the visual environment, participants might be less likely to look beyond the area 

illuminated by them. 
137

1 m = 3.3 ft

Figure 102. Graph. Overhead-lighting level experiment—target I contrast and detection 

distances versus distance from target at 40-percent overhead lighting.

Target II (High VI)

As with target I, the contrast of target II was analyzed at 100-percent and 40-percent overheadlighting levels. 

Overhead Lighting at 100 Percent:

The Weber contrasts for target II are shown in figure 103. With headlamps at distances closer 

than 76.2 m (250 ft), the headlamps create positive contrast on the target. Beyond that and 

without headlamps, contrasts are very close to zero but fluctuate owing to differences in the 

pavement behind the target and the changing visual angle. 

Most participants detected the target more than 61.0 m (200 ft) away, showing that luminance 

contrast was not the only factor driving target visibility. The average detection distance without 

headlamps decreased by approximately 15 m (50 ft) (19.4 percent) when the lighting level was 

reduced from 100 percent to 40 percent. With headlamps, the detection distance decreased by 

approximately 12 m (40 ft), or 17.8 percent. Detection distances are nearly normally distributed 
138

when the headlamps are off. When the headlamps are on, the distribution is skewed toward 

longer distances.

1 m = 3.3 ft

Figure 103. Graph. Overhead-lighting level experiment—target II contrast and detection 

distances versus distance from target at 100-percent overhead lighting.

Overhead Lighting at 40 Percent:

Weber contrast at 40-percent overhead lighting, shown in figure 104, is similar to that at 100-

percent overhead lighting. Without headlamps, the target detection distance is about 15.2 m 

(50 ft) farther away than with headlamps, even though the contrast with headlamps is higher. 

Again, this could be because headlamps increase ambient luminance causing the eye to adapt to 

brighter conditions and become less sensitive to contrast. 
139

1 m = 3.3 ft

Figure 104. Graph. Overhead-lighting level experiment—target II contrast and detection 

distances versus distance from target at 40-percent overhead lighting.

Visibility Level

VL is a calculation describing the extent to which a target can be seen. It accounts for the 

object’s size, its contrast, the age of the observer, and the threshold luminance required for an 

object to be visible to 99 percent of viewers. The higher the VL, the more visible the target 

should be.(96)

Pedestrian 

The VL calculations for the pedestrian with headlamps on are shown in figure 105. Overheadlighting levels from 10 to 100 percent were averaged (dashed line) because there was very little 

difference in VL between them. They are compared with VL for no overhead lighting (solid 

line). The pedestrian VL was higher for no overhead lighting at all distances up to 244 m 

(800 ft). That roughly corresponds with experimental observations, where pedestrians were 

visible from slightly greater distances with no overhead lighting. 
140

1 m = 3.3 ft

Figure 105. Graph. Overhead-lighting level experiment—pedestrian VL with headlamps on 

and with and without overhead lighting.
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Target I (Low VI)

Figure 106 shows the VL calculations for target I with headlamps on, grouped by whether or not 

there was overhead lighting. The VLs are very similar at 30 m (100 ft) from the target and 

beyond. The VL rapidly increases at closer than 30 m (100 ft). That point is closer than the 

distance at which headlamp light falls on the target, about 76.2 m (250), showing that the VL 

calculation does not correspond with experimental observations. 

1 m = 3.3 ft

Figure 106. Graph. Overhead-lighting level experiment—target I VL with headlamps on 

and with and without overhead lighting.
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Target II (High VI)

The VL calculations do not align with experimental observations for target II. Target II’s

contrast transitioned from negative to positive when the vehicle with headlamps on got close to 

it, but the VL calculations do not show that change (figure 107). At about 46 m (150 ft), the VL 

calculations also show a spike in visibility for no overhead lighting and a dip in visibility for 

overhead lighting. Those effects were not supported by the experimental data. 

1 m = 3.3 ft

Figure 107. Graph. Overhead-lighting level experiment—target II VL with headlamps on 

and with and without overhead lighting.

CONCLUSIONS

A research objective for this experiment was to evaluate the impact of headlamps on driver 

visual performance. Results found that distant objects were more visible without headlamps than 

with headlamps, likely because the headlamp light caused the eye to adapt to the brighter 

environment, reducing contrast sensitivity. In addition, headlamps, which produced measurable 

amounts of VI up to 91.4 m (300 ft) away from a vehicle, affected object visibility at 213 m 

(700 ft) away, likely owing to light scatter on the roadway and environment. The impact of this 

indirect light on visibility was negligible. 
143

Evaluating how overhead-lighting level affected driver visual performance was the next research 

objective. High ambient luminance made a positively contrasting object more difficult to see, 

and low ambient luminance made a negatively contrasting object easier to see. An object with 

very low contrast would be difficult to see, regardless of object size and adaptation level; 

however, that poor visibility can be mitigated by adjusting object size and adaptation luminance.

In addition, a target with negative contrast becomes briefly invisible as it is illuminated by 

headlamps and transitions to positive contrast. The effects of higher adaptation levels to brighter 

environments are particularly noticeable at night with low-contrast objects.

Other project objectives were to evaluate how headlamps and overhead lighting interacted to 

affect driver visual performance and to evaluate the minimum overhead-lighting level where 

target and pedestrian visibility was not compromised. This experiment found that headlamps 

combined with overhead lighting resulted in better object recognition and detail visibility than 

with overhead lighting alone, especially when overhead lighting was dim. The combination of 

headlamps and overhead lighting appears to have its greatest impact when the two sources 

contribute a nearly equal amount of lighting. This partial contribution can be achieved in two 

ways and was found primarily with respect to target visibility. The first way was when overhead 

lighting was dimmed to 30 percent (3 lx (0.28 fc), approximately 0.3 cd/m2 (0.09 fL)) or below. 

Above 30 percent, the overhead lighting was typically the dominating light source, allowing for 

distant detections, especially for targets. Headlamps competed with overhead lighting when the 

level was below 30 percent, resulting in effects on the properties of the target—contrast, 

adaptation luminance, and light angle—that affect visibility. The second way the partial 

combination was achieved was via proximity, depending on contrast polarity. In the intermediate

zone (30.4 to 76.2 m (100 to 250 ft)), a negative contrast target must transition to positive 

contrast when headlamps become the primary source of lighting, causing it to cross a point of

zero contrast. In this same zone, a positive contrast target experiences changes in VL and 

contrast that can affect visibility. In this situation, whether an object seen in positive contrast

remains visible as a vehicle approaches depends on adaptation luminance. The impact of 

headlamps and overhead lighting was less pronounced in the case of pedestrians, primarily 

because of their angular size and nonuniform shape.

Additional results found that object size, ambient lighting level, and contrast affected the 

visibility of gray pedestrians and targets in this experiment. First, the larger an object, the more 

visible it was, as shown by the VL calculations; pedestrians had higher VLs than targets. Second, 

the ambient lighting level affected the eye’s adaptation, which in turn affected contrast 

sensitivity. Last, longer pedestrian-detection distances were measured without headlamps (and 

with overhead lighting), because this condition had a lower adaptation level, making a lowcontrast pedestrian discernible beyond 422 m (800 ft). Orientation recognition, however, 

typically occurred only within 30 m (100 ft) of the object, requiring the driver to drive slower 

than 24 km/h (15 mi/h) to be able to stop in time to avoid colliding with the object. 

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CHAPTER 7. MESOPIC MODELING EXPERIMENT

INTRODUCTION

This experiment was designed to investigate the applicability of the CIE Recommended System 

for Mesopic Photometry, described in chapter 2, to nighttime roadway conditions.(1) It also 

investigated the relationship between peripheral visual performance and the SPD of overheadlighting sources in the mesopic range, in both static (parked) and dynamic (driving) conditions. 

To that end, three types of overhead lighting with different spectral distributions were used: 

2,100-K HPS, 3,500-K LED, and 6,000-K LED. The level of the overhead lighting was chosen 

based on the adaptation luminance at the roadway. All adaptation luminance levels fell in the 

mesopic range, and all were the same among the different lighting types. The overhead lighting 

was used in combination with two pavement types—darker asphalt and lighter concrete—so the 

same level for an overhead-lighting type produced two different adaptation luminance levels, 

depending on pavement type. The experiment used five adaptation luminances for each lighting 

type, one of which was the same between asphalt and concrete. Three different off-axis target 

locations were used because research indicated that overhead-lighting sources whose spectral 

distributions favored the sensitivity of the rods had better off-axis performance.(20,21)

This experiment included two portions, one static and one dynamic. The static portion of the 

experiment was similar to laboratory and simulator studies on mesopic vision but was performed 

in a stationary vehicle on the roadway. Participants sat behind the steering wheel of the test 

vehicle and fixated on the center of the roadway while researchers adjusted the contrast of offaxis targets on the shoulder. The dependent variable was the threshold luminance contrast or 

contrast threshold at which the participant first saw the target. In the dynamic portion of the 

experiment, participants drove down the road and were instructed to fixate on the roadway and 

use peripheral vision to detect the targets. Targets at different eccentricities and contrasts were 

presented, and participant detection distances were measured. The static experiment used a fixed 

geometry setup, whereas the dynamic experiment used a nonfixed geometry setup. 

Target color was not included in this experiment, because the experiment focused on the effects 

of overhead-lighting configuration, eccentricity, and adaptation luminance rather than effects of 

object color on mesopic visibility. For the same reason, vehicle headlamps were not used for this 

experiment, isolating the effects of overhead lighting on mesopic vision. 

Research Objectives

The purpose of the mesopic modeling experiment was to evaluate the following: 

• Applicability of the CIE Recommended System for Mesopic Photometry in 

naturalistic nighttime roadway situations.(1)

• Effect of the spectrum of an overhead-lighting source on peripheral visual 

performance of drivers in both static and dynamic conditions.
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EXPERIMENTAL DESIGN

For the static portion, a 2 by 3 by 5 by 3 mixed-factors experiment was designed to investigate 

the effects of age, overhead-lighting type, adaptation luminance, and eccentricity on the 

threshold contrast where participants first detected a target. 

For the dynamic portion, a 2 by 3 by 5 by 3 by 2 mixed-factors experiment was designed to 

investigate the effects of age, overhead-lighting type, adaptation luminance, eccentricity, target 

light intensity, and vehicle speed on target detection distance.

Participants of two age groups were exposed to all combinations of factors for the static 

experiment. The dynamic experiment was a partial factorial experimental design with all secondorder interactions and one third-order interaction. Experimental variables are listed in table 27

through table 29.

Table 27. Mesopic modeling experiment—independent variables and values.

Independent Variable

Used In 

Static

Used In 

Dynamic Values

Age Yes Yes Younger (25–35), Older (65+)

Overhead-Lighting Type Yes Yes 6,000-K LED (S/P 1.82), 3,500-K LED 

(S/P 1.31), 2100-K HPS (S/P 0.64)

Adaptation Luminance Yes Yes 0.07 cd/m2 (0.020 fL), 0.1 cd/m2

(0.03 fL), 0.2 cd/m2 (0.06 fL), 

0.3 cd/m2 (0.09 fL), 0.5 cd/m2 (0.15 fL)

Eccentricity Yes Yes 6 degrees, 10 degrees, and 14 degrees

Speed No Yes 56 km/h (35 mi/h), 80 km/h (50 mi/h)

Table 28. Mesopic modeling experiment—covariate and value/measurement method.

Covariate

Used in

Static

Used in

Dynamic Value/Measurement Method

Target Contrast No Yes High and Low Contrast (Weber)

Table 29. Mesopic modeling experiment—dependent variables and measurement method.

Dependent Variables

Used in

Static

Used in

Dynamic Measurement Method

Threshold Contrast Yes No Weber contrast at which participant 

can first detect a target

Detection Distance No Yes Distance at which participant can first 

detect a target

Independent Variables

Age

Participants were divided into the same age groups as previous experiments: younger 

(25–35 years old) (M = 28.4, SD = 3.2) and older (65 years old and older) (M = 67, SD = 1.5).
147

Overhead-Lighting Type 

All three of the overhead-lighting systems were used. 

Adaptation Luminance

Adaptation luminance was the photopic luminance viewed from inside the vehicle for a given 

combination of overhead-lighting level and pavement type. It was controlled for by adjusting the 

overhead-lighting levels for the three lighting types and two pavement types. 

Overhead-Lighting Level: 

Each lighting type was used at three intensity levels: low, medium, and high. Those lighting 

levels were adjusted so that the low, medium, and high intensity levels produced the same 

adaptation luminance inside the vehicle across the three lighting types, and so they were all in 

the mesopic range.

Pavement Type: 

With the two pavement surfaces on the Smart Road, the pavement color and reflectance differed 

meaning that the same overhead-lighting level resulted in different adaptation luminances in the 

two sections. For this experiment, the overhead-lighting levels were adjusted so that the high 

overhead-lighting level on asphalt produced the same adaptation luminance as the medium 

overhead-lighting level on concrete. The result was five distinct adaptation luminances, shown in 

table 30.

Table 30. Mesopic modeling experiment—adaptation luminance by overhead-lighting level 

and pavement type. 

Overhead-Lighting Level

Luminance on Concrete

(cd/m2 (fL))

Luminance on Asphalt

(cd/m2 (fL))

Low 0.1113 (0.032) 0.0716 (0.021)

Medium 0.3535a (0.103) 0.1987 (0.056)

High 0.5385 (0.157) 0.3535a (0.103) a

Same luminance.

Eccentricity

Targets were placed at three angles with respect to the roadway: 6, 10, and 14 degrees, as shown 

in figure 108. These angles were measured assuming the observer was at a vertical distance of 

83 m (272 ft) from the target location.
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Figure 108. Diagram. Arrangement of target lights for static experiments on the Smart 

Road.

Speed

For the static portion of the experiment, the vehicle was parked in a specified location with 

respect to the targets, and speed was not an independent variable. 

For the dynamic portion of the experiment, the vehicle traveled at 56 and 80 km/h (35 and 

50 mi/h). 

Dependent Variables

Threshold Contrast

For the static portion of the experiment, the target’s light intensity was increased until the 

participant could just discern the target. At that point, threshold contrast of the target was 

calculated by first measuring the luminance of the target and background with a ProMetric®

luminance camera inside the test vehicle and then by calculating the target’s Weber contrast from 

those measurements using the formula in figure 8. The threshold-detection measurements in this 

experiment measured supra-threshold contrast, where the participant definitely detected the 
149

target, whereas threshold contrast typically refers to the contrast at 50-percent detection 

probability.

Detection Distance

For the dynamic portion of the experiment, the detection distance was the dependent variable. 

METHODS

Facilities and Equipment

This experiment was conducted on the Smart Road using the same test vehicles as in previous

experiments. 

Targets and Backgrounds

The target setup for this experiment was significantly different than those used in the other 

experiments. While the targets were the same gray targets as used in the previous experiments in 

this project, they were mounted on vertical posts, and their height was adjustable. Spotlights used 

to illuminate the targets were mounted on the same apparatus as the targets. The target 

illuminators were about 2.4 m (8 ft) from the targets and were also adjustable, so the 

experimenters could ensure the spotlight was centered on the target. The target lights were 

enclosed in light-tight cases, reducing scatter, so that the only light emitted was directed toward 

the target. The target stands and target lights were painted in a neutral camouflage pattern so they 

would not distract the participant drivers. The targets were illuminated using CREE® LED BR30 

reflector spotlights rated at 650 lumens. A warm bulb (2,100 K) was used with the 3,500-K LED 

and 2,100-K HPS lighting, and a cool (5,000 K) bulb was used with the 6,000-K LED lighting. 

The backgrounds were 0.91 by 1.2 m (3 by 4 ft) sheets of gray-painted plywood on gray-painted 

stands. The target-lighting apparatus were placed 4.6 m (15 ft) from the background. A target, a 

target light, and its background are illustrated in figure 109.
150

Figure 109. Photo. Mesopic modeling experiment—target, target light, and background.

Four sets of three targets, target lights, and backgrounds were set up alongside the Smart Road to 

create the three desired viewing angles at those four stations. The two stations in the asphalt 

section were named A1 and A2, and the two in the concrete section were named C1 and C2. All 

four stations were used in the dynamic portion of the experiment, but only stations A1 and C2 

were used in the static portion of the experiment. 

Participants

Participants were recruited and screened as described in chapter 3. A total of 18 participants 

performed the experiment. Nine were older, and nine were younger. Nine were female, and nine 

were male. Mean and standard deviation of participant age, visual acuity, mesopic visual acuity, 

low contrast visual acuity, and UFOV are listed in table 31. 
151

Table 31. Mesopic modeling experiment participant characteristics.

Participant Characteristic

Older 

Drivers

Mean

Older 

Drivers

Standard 

Deviation

Young 

Drivers

Mean

Younger 

Drivers

Standard 

Deviation

Age 70.4 6.3 30.5 3.2

Visual Acuity 20/24.2 8.8 20/19.4 8.8

Mesopic Visual Acuity 20/38.8 10.6 20/27.2 13.0

Low Contrast Visual Acuity 20/26.3 10.6 20/18.3 6.4

UFOV 1.55 0.73 1 0

Procedure

Each participant attended three experimental sessions, one for each overhead-lighting type. Each 

session consisted of a static and dynamic portion, and two participants completed the experiment 

simultaneously.

Static

One participant was directed to park at the vehicle stopping point for station A1, and the other 

was directed to park at the vehicle stopping point for station C2. An in-vehicle experimenter 

asked the participant to focus on a cone placed on the roadway, so that the targets were visible in 

the participant’s peripheral vision. The researcher then slowly increased the intensity of the 

target light. When the participant was first able to see a target, he or she announced “target” and 

the researcher recorded the target light intensity. The procedure was repeated for targets at 

different viewing angles for a total of 24 times. At the end of the static portion of the experiment, 

the in-vehicle researchers notified other team members that they were finished, and the on-road 

researchers adjusted the equipment for the dynamic portion of the experiment. 

Dynamic 

Each participant performed 1 practice run and 12 experimental runs down and up the Smart Road

for each overhead-lighting type during an experimental session. For the dynamic portion of the 

experiment, the on-road experimenters set the target light levels, target positions, and overheadlighting level for each experimental run. The in-vehicle experimenters directed the participants to 

focus on the roadway while driving to encourage target detection in peripheral vision, ensured 

they drove at the correct speed, and recorded detections. 

Data Analysis

Static

A mixed-factors ANOVA was performed to determine how the independent variables affected 

threshold contrast. If results were significant, a Tukey’s HSD test was performed to determine 

which independent variables differed significantly from each other. In the graphs reporting 

Tukey’s HSD results, points sharing a letter are not significantly different from each other. 
152

Dynamic

A partial mixed-factorial ANOVA was performed to determine how the independent variables 

affected detection distance. 

RESULTS 

Static

In the static portion of the experiment, researchers varied the contrast of the off-axis targets, and 

participants told researchers when they could first see them. The threshold contrast, when the 

participant would first see the target, was the dependent variable.

The effect of the independent variables on threshold contrast is listed in table 32, with results 

described in detail after the table. 

Table 32. Mesopic modeling experiment (static portion)—independent variables and their 

effect on threshold contrast.

Independent Variable(s)

Degrees of 

Freedom F-value Pr > F

Age 1 3.34 0.0832

Overhead-Lighting Type 2 18.55 < .0001a

Overhead-Lighting Type by Age 2 3.18 0.0549

Adaptation Luminance 4 48.11 < .0001a

Adaptation Luminance by Age 4 1.68 0.1765

Eccentricity 2 32.81 < .0001a

Eccentricity by Age 2 3.04 0.0596

Overhead-Lighting Type by Overhead-Lighting Level 8 6.06 < .0001a

Overhead-Lighting Type by Eccentricity 4 5.77 0.0005a

Adaptation Luminance by Eccentricity 8 14.86 < .0001a

Overhead-Lighting Type by Adaptation Luminance 

by Eccentricity

16 10.96 < .0001a

a

Signficant at p < 0.05.

Pr = Probability

Overhead-Lighting Type

Overhead-lighting type significantly affected threshold contrast, illustrated in figure 110, where 

points sharing a letter are not significantly different from each other. The 6,000-K LED lighting 

provided the lowest threshold contrast and the 2,100-K HPS lighting the highest threshold 

contrast. A Tukey’s HSD test found no statistical difference between the two LED lighting types, 

but HPS light differed significantly from both LED types.
153

Figure 110. Graph. Mesopic modeling experiment (static portion)—threshold contrast by 

overhead-lighting type.

Adaptation Luminance

Adaptation luminance significantly affected threshold contrast, with, in general, lower adaptation 

levels requiring higher contrasts before the target was visible (see figure 111, where points 

sharing a letter are not significantly different from each other). A Tukey’s HSD test found all 

pairwise comparisons between adaptation luminances were significant, except the comparisons 

between 0.2 and 0.3 cd/m2 (0.06 and 0.09 fL) and 0.2 and 0.5 cd/m2 (0.06 and 0.15 fL). This 

could be because of a threshold effect where, if the luminance is above a certain level, increasing 

luminance no longer negatively affects contrast sensitivity. Adaptation luminance was also 

involved in statistically significant higher order interactions. This main effect could also be 

significantly influenced by the statistical significance of higher order interactions involving 

adaptation luminance. 
154

1 cd/m2 = 0.3 fL

Figure 111. Graph. Mesopic modeling experiment (static portion)—threshold contrast by 

adaptation luminance.

Eccentricity 

Eccentricity significantly affected threshold contrast, with targets farther off axis requiring 

greater contrast before they were visible, as shown in figure 112. Pairwise comparisons found 

that the threshold contrasts at all three eccentricities were significantly different from one 

another. That increase in threshold contrast cannot be attributed to loss due to probability 

summation because of the target locations (especially at 10 and 14 degrees) falling in the blind 

spot of the eyes. Binocular probability summation states that if the probability of detection by 

each eye is 50 percent (or 0.5), the probability that two eyes detect the signal when the target 

falls in the blind spot on one eye is now 0.5 + 0.5 – (0.5 x 0.5) = 0.75 or 75 percent (a 25-percent 

decrease compared with when the target is not in the blind spot of one eye). The threshold 

contrast at 10 degrees was 70-percent greater than that at 6 degrees, and at 14 degrees, it was 

140-percent greater than that at 6 degrees. That is greater than the increase in threshold contrast 

predicted by binocular probability summation alone—25 percent. 
155

Figure 112. Graph. Mesopic modeling experiment (static portion)—threshold contrast by 

eccentricity.

Effect of Overhead-Lighting Type, Adaptation Luminance, and Eccentricity on Mesopic and 

Photopic Threshold Contrast 

Luminance, as determined by the luminance meter used in this experiment, assumed 

measurements were taken in the photopic range. In the mesopic range, luminance calculations 

are different and account for the eye’s different mesopic spectral sensitivity. The above-threshold 

contrasts were calculated using luminance measurements as determined by the luminance meter, 

with no mesopic correction. 

The mesopic correction factor is calculated by multiplying the photopic luminance by a 

correction factor based on the S/P ratio of the light source. The S/P ratio is the ratio of the lamp’s 

scotopic output to its photopic output, calculated using V( ) and V’( ) and describes the spectral 

sensitivity of the eye to the power distribution of a light source in mesopic conditions. The 

higher a lamp’s S/P ratio, the better its light for peripheral detection in the mesopic region.(28,29)

The mesopic output is calculated using the formula in figure 113 and figure 114, taken from the 

CIE Recommended System for Mesopic Photometry Based on Visual Performance.

(1)

Figure 113. Equation. Formula for calculating mesopic output. 

Where:

Lmesopic = Mesopic luminance.

MM = Mesopic multiplier from the CIE Recommended System for Mesopic Photometry Based on 

Visual Performance.

(1)

LPhotopic = Photopic luminance. 

λ λ

LLMM MM = MMMM × LL hoo oo
156

Mesopic threshold contrasts were calculated by first calculating the mesopic luminance of the 

target and the mesopic luminance of the target’s background using the equation in figure 114:

Figure 114. Equation. Formula for calculating mesopic threshold contrast. 

Where:

LTarget-Mesopic = Mesopic luminance of the target.

LBackground-Mesopic = Mesopic luminance of the background.

Threshold contrasts using photopic luminance values were compared with those using calculated 

mesopic luminance values to determine where the results diverge and which luminance 

calculation best predicts the results. Calculations were repeated for each adaptation luminance 

and are displayed by lighting type and eccentricity in figure 115.

TThrr hoo CCoooo MM MM = LLTT −MM MM − LLBB BB −MM MM

LLBB BB −MM MM
157

1 cd/m2 = 0.3 fL

Figure 115. Graph. Mesopic modeling experiment (static portion)—threshold contrasts 

calculated with photopic and mesopic luminances for the three lighting types and three 

eccentricities.
158

From figure 115, it is clear that broad spectrum overhead-lighting types with higher S/P ratios 

(3,500-K LED and 6,000-K LED) had considerably lower off-axis threshold contrast values 

compared with narrow spectrum 2,100-K HPS lighting. The threshold contrasts were lower for 

LED lighting at all eccentricities and at four of the five adaptation luminances, showing that 

greater contrast was needed in HPS than LED lighting to see the same object.

Surprisingly, both threshold contrasts for HPS lighting were highest at 0.1 cd/m2 (0.03 fL) and 

not the dimmer 0.07 cd/m2 (0.020 fL) one would expect. 

Photopic threshold contrasts for HPS were lower than the mesopic threshold contrasts. For LED 

lighting, the opposite was true. 

Dynamic 

In the dynamic portion of the experiment, participants drove with their eyes focused on the 

roadway, detecting the off-axis targets in their peripheral vision. Age, overhead-lighting type, 

adaptation luminance, eccentricity, speed, and target contrast were independent variables. A 

partial mixed-factors ANOVA was performed to determine how the independent variables 

affected detection distance. 

The effect of the independent variables on detection distance is listed in table 33, with results 

described in detail after the table. 
159

Table 33. Mesopic modeling experiment (dynamic portion)—independent variables and 

their effect on threshold contrast.

Factor(s)

Degrees of 

Freedom F-value Pr > F

Age 1 1.2 0.2898

Overhead-Lighting Type 2 0.18 0.8325

Adaptation Luminance 4 2.98 0.0263a

Eccentricity 2 1.69 0.2014

Speed 1 0.23 0.6349

Contrast 1 0.87 0.3654

Overhead-Lighting Type by Adaptation Luminance 8 3.25 0.0027

Overhead-Lighting Type by Eccentricity 4 0.74 0.572

Adaptation Luminance by Eccentricity 8 2.73 0.0091a

Overhead-Lighting Type by Speed 2 0.34 0.7174

Adaptation Luminance by Speed 4 2.29 0.0731a

Eccentricity by Speed 2 3.55 0.0433a

Contrast by Overhead-Lighting Type 2 4.09 0.0303a

Contrast by Adaptation Luminance 4 1.35 0.2636

Contrast by Eccentricity 2 0.98 0.3904

Contrast by Speed 1 0.55 0.4711

Overhead-Lighting Type by Adaptation Luminance by

Eccentricity

16 2.08 0.0164a

a

Signficant at p < 0.05 or marginally significant at p < 0.1.

Pr = Probability.

Only the main effect of adaptation luminance was significant. Four two-way interactions and a

three-way interaction among overhead-lighting type, adaptation luminance, and eccentricity were 

also significant.

Adaptation Luminance

The effect of adaptation luminance on detection distance, plotted in figure 116, was significant. 

Points sharing a letter in the figure do not differ significantly from one another. Targets in the 

areas with the highest adaptation luminances were detected from farther away than those in areas 

with the lower adaptation luminances. 
160

1 cd/m2 = 0.3 fL

1 m = 3.3 ft

Figure 116. Graph. Mesopic modeling experiment (dynamic portion)—detection distance 

by adaptation luminance.

Overhead-Lighting Type and Adaptation Luminance

The effect on detection distance of the interaction between overhead-lighting type and adaptation 

luminance was significant when all eccentricities were combined. There was no difference in the 

mean detection distance among the three overhead-lighting types at the highest and lowest 

adaptation luminance levels (0.5, 0.3, and 0.01 cd/m2 (0.15, 0.09, and 0.03 fL)). However, at the 

intermediate adaptation luminance levels (0.1 and 0.2 cd/m2 (0.03 and 0.06 fL)), detection 

distances were different across the different overhead-light types, as illustrated in figure 117. 

More information was extracted from these trends by including the eccentricity in these data, as 

described in the section Overhead Lighting Type, Adaptation Luminance, and Eccentricity.

1 cd/m2 = 0.3 fL
161

1 m = 3.3 ft

Figure 117. Graph. Mesopic modeling experiment (dynamic portion)—effect of overheadlighting type and adaptation luminance on detection distance.

Eccentricity and Speed

The effect on detection distance of the interaction between eccentricity and speed was 

significant. At both speeds, more eccentric targets were detected from farther away, as illustrated 

in figure 118. Also, objects were detected from farther away at the lower speed compared with 

the higher speed. 

1 m = 3.3 ft

1 km/h = .62 mi/h

Figure 118. Chart. Mesopic modeling experiment (dynamic portion)—effect of eccentricity 

and speed on detection distance.

Adaptation Luminance and Eccentricity

The interaction between adaptation luminance and eccentricity was also significant. The 

detection distance was longer for higher adaptation luminances and targets at greater 

eccentricities. The 6-degree eccentricity target had the lowest mean detection distance across all 

the adaptation luminances. The 10- and 14-degree targets had higher detection distances at every 

adaptation luminance. An increase in adaptation luminance was also associated with an increase 

in the detection distance. The results, illustrated in figure 119, also show that an increase in the 

adaptation luminance significantly affected the detection distances of objects at higher 

eccentricities. The 14-degree target at 0.07 cd/m2 (0.020 fL) had a higher detection distance than 

those at the other two angles, 6 and 10 degrees. 
162

1 cd/m2 = 0.3 fL

1 m = 3.3 ft

Figure 119. Graph. Mesopic modeling experiment (dynamic portion)—effect of adaptation 

luminance and eccentricity on detection distance.

Overhead-Lighting Type, Adaptation Luminance, and Eccentricity

Mean detection distances increased as adaptation luminance increased, and different light 

sources had longer detection distances at different adaptation luminances, as shown in

figure 120. An increase in eccentricity corresponded to both an increase in detection distance and 

a decrease in the difference between detection distances among the overhead-lighting types. At a 

6-degree eccentricity, the 2,100-K HPS lighting resulted in had longer detection distances at 

adaptation luminances of 0.5 and 0.2 cd/m2 (0.15 and 0.06 fL), but the three lighting types had 

similar detection distances at adaptation luminances of 0.1 and 0.3 cd/m2 (0.03 and 0.09 fL). The 

6,000-K LED overhead lighting had shorter detection distances than the other lighting types at 

the 6-degree eccentricity at all adaptation luminance levels. At the 10-degree eccentricity, both

of the LED overhead-lighting types had longer detection distances than HPS lighting at every 

adaptation luminance. However, that trend did not occur at the 14-degree eccentricity, where no 

lighting type had longer detection distances at all adaptation luminances. 
163

1 cd/m2 = 0.3 fL

1 m = 3.3 ft

Figure 120. Graph. Mesopic modeling experiment (dynamic portion)—effect of overheadlighting type, adaptation luminance, and eccentricity on detection distance.

DISCUSSION

This research effort focused on application of the CIE Recommended System for Mesopic 

Photometry Based on Visual Performance to realistic nighttime roadway conditions and directly 

addressed the project objective to evaluate the effectiveness of mesopic models in a driving 

environment.(1) A two-pronged approach was used. First, a static experiment was conducted to 

replicate fixed geometry laboratory tests to understand the effects of overhead-lighting type, 

adaptation luminance, and eccentricity on peripheral threshold contrasts of participants of 

different age groups. Second, a dynamic experiment was performed in which the same 

participants drove at two different speeds to detect targets under the same conditions of overhead 

lighting, adaptation luminance, and eccentricity. The dynamic experiment helps answer 

questions regarding how nonfixed geometry affects peripheral visual performance; as in any 

driving task, the objects on the periphery of the road subtend different angles at different 

instances. The analysis of the static and dynamic experiments provided insights into not only the 

applicability of the CIE recommended mesopic system but also how overhead-lighting sources, 

adaptation luminance, and eccentricities affected peripheral visual performance. 
164

Static Experiment

The results showed that both LED overhead-lighting sources showed significantly lower 

threshold contrasts compared with the HPS source. Participants found it more difficult to detect 

the targets in HPS lighting and required higher contrast to do so, likely because HPS spectral 

distribution does not align with the eye’s sensitivity in the mesopic range. A light source with 

higher S/P ratios, such as the LED light sources tested, produces more light in the wavelengths 

where the rod photoreceptors are more sensitive and lowers the peripheral contrast threshold of 

the targets. These results align with previous research, which showed that light sources with 

higher S/P ratios had better peripheral visual performance. (See references 20, 21, 28, 42,

and 44.)

An increase in adaptation luminance is also associated with a decrease in the threshold contrast. 

These results are in agreement with other studies that indicated that an increase in adaptation 

luminance reduces threshold contrast.(41) Both the LED light sources required lower contrasts to 

detect peripheral targets compared with the HPS source, as shown in figure 121. Again, this can 

be attributed to LED light sources producing light in the wavelengths that make the rods more 

sensitive. An increase in the eccentricity of the target was associated with an increase in the 

threshold contrast. These results align with previous research that found visual acuity outside the 

fovea decreases with eccentricity.(60)

1 cd/m2 = 0.3 fL

Figure 121. Graph. Mesopic modeling experiment (static portion)—threshold contrasts of 

targets under different overhead-lighting sources at different adaptation luminances.

Comparison of Mesopic and Photopic Threshold Contrasts

The mesopic threshold contrasts were calculated from the mesopic luminances of the targets and 

their backgrounds under different overhead-lighting types and adaptation luminances and at 

three different eccentricities. These mesopic threshold contrasts were compared with the 
165

photopic contrasts to see how they varied. The difference between photopic and mesopic 

threshold contrasts for target detection for the 2,100-K HPS lighting was largest at 0.1 cd/m2

(0.03 fL) and higher eccentricities. For HPS lighting, the difference in photopic and mesopic 

threshold contrasts decreased as eccentricity decreased and decreased as adaptation luminance 

increased. For LED lighting, the differences in photopic and mesopic threshold contrasts were 

smaller than those for HPS lighting but also became slightly greater for lower luminance levels, 

mirroring the trend in HPS lighting. This shows that for HPS lighting, photopic luminance 

measurements may be the least accurate, especially at low light levels. For both lighting types, 

photopic threshold contrasts were more accurate at higher luminances.

The unexpectedly low threshold contrast at 0.07 cd/m2 (0.020 fL) could be because at the far 

periphery, and at the lowest lighting level, the peripheral area of the retina was not adapted to the 

measured adaptation luminance. The adaptation levels for this study were calculated at the 

roadway surface, where the drivers were instructed to fixate. At the lowest levels of HPS 

lighting, the 10- and 14-degree locations were very dark, with a luminance approaching scotopic 

levels. The difference in adaptation levels between the fovea, where participants were looking at 

the brighter roadway, and 10 and 14 degrees in the periphery, where they detected the targets, 

meant the eye’s periphery was not adapted to the same level as the fovea. At the periphery, the 

eye was adapted to darker lighting levels, reducing the threshold contrast for those conditions. 

CIE Recommended System for Mesopic Photometry

The CIE model for mesopic spectral sensitivity makes it possible to calculate the mesopic 

luminance when the photopic luminance and S/P ratio of the light source are known.

(1) The 

outputs of the CIE model for mesopic spectral sensitivity are the mesopic luminance and the 

weighting factor for calculating the mesopic spectral luminous efficiency function from the 

photopic spectral luminous efficiency function V( ) and scotopic spectral luminous efficiency 

function V′( ).

An ideal model for mesopic spectral sensitivity would describe the mesopic luminances of the 

target and background (and therefore contrast) the same way for the light sources with different 

spectra; it would be independent of the lighting type’s color. The CIE model makes it possible to 

make the mesopic luminance independent of the spectrum of the light source. However, in

figure 122 (which shows the mesopic luminances for the different overhead-lighting types 

calculated using the CIE model), the mesopic contrast was different for different light sources at 

the same adaptation luminance. Therefore, for this experiment, the CIE model did not accurately 

produce mesopic luminance, contrast, and threshold contrast values that were independent from 

the light-source spectra. The greatest difference was for HPS lighting. The CIE model performed 

very well for 6- and 10-degree eccentricities for both of the LED lighting types; the mesopic 

threshold contrast lines are very close to each other for different adaptation luminances, as 

shown in figure 122. However, at an eccentricity of 14 degrees and adaptation luminance of 

0.07 cd/m2 (0.020 fL), the predicted mesopic contrasts were very different from one another.

λ

λ
166

1 cd/m2 = 0.3 fL

Figure 122. Graph. Mesopic modeling experiment (static portion)—mesopic contrast 

thresholds as predicted by the CIE Mesopic Spectral Sensitivity Model.
167

Predicted Versus Actual Difference in Mesopic Luminances 

The threshold contrasts in the HPS lighting were significantly different from both LED lighting 

types. This section compares the actual difference in the mesopic luminance (delta, expressed as 

a percentage) between the HPS lighting and both LED lighting types to the predicted difference 

(expressed as a percentage) in mesopic luminance from the CIE Recommended System for 

Mesopic Photometry Based on Visual Performance.

(1)

For example, the difference between 2,100-K HPS and 6,000-K LED lighting is calculated in the 

following way (figure 123, from table 11 in CIE Recommended System for Mesopic Photometry

Based on Visual Performance): 

Figure 123. Equation. Formula for calculating the mesopic luminance difference between 

2,100-K HPS and 6,000-K LED lighting.(1)

Where:

Lmesopic-HPS = Mesopic target luminance under 2,100-K HPS lighting.

LPhotopic-HPS = Photopic target luminance under 2,100-K HPS lighting.

LMesopic-LED6K = Mesopic target luminance under 6,000-K LED lighting.

LPhotopic-LED6K = Photopic target luminance under 6,000-K LED lighting.

For instance, the percentage difference in mesopic and photopic luminance for HPS lighting

(S/P = 0.64) and LED 6,000-K lighting (S/P = 1.82) at 0.1 cd/m2 (0.03 fL) is -5 and +27 percent,

respectively. Therefore, the predicted difference is (-5 – 27) percent = -32 percent.

Figure 124 illustrates the difference in the calculated and predicted delta between the HPS and 

LED lighting types at each adaptation luminance for each eccentricity. The differences in the 

calculated and the predicted mesopic luminance between HPS lighting and both the LED lighting 

types at eccentricities of 6 and 10 degrees show the same trend from the lowest adaptation 

luminance to 0.2 cd/m2 (0.06 fL). The calculated difference evened out above this level. For the 

14-degree eccentricity, the calculated deltas between the HPS and the LED lighting types were 

most similar at the lowest adaptation luminances. However, as the adaptation luminance 

increased, the predicted and calculated deltas diverged, with the greatest difference at 0.5 cd/m2

(0.15 fL). From this analysis, it is evident that the CIE model accurately predicted the mesopic 

luminances to about 0.2 cd/m2 (0.06 fL), up to 10-degree eccentricity off-axis. At higher 

adaptation luminances and eccentricities, the calculated delta between the HPS and LED lighting 

types evened out rather than decreased as the model predicted. The differences between 

calculated and predicted mesopic luminances increase with increasing eccentricity. Because the 

CIE model does not use eccentricity as an input parameter, the results for this study suggest that 

a mesopic model with eccentricity as an input parameter might predict the mesopic luminances 

more accurately.

LLMM MM −HH

LL hoo oo −HH

− LLMM MM −LLLL 6KK

LL hoo oo −LLLL 6KK
168

1 cd/m2 = 0.3 fL

Figure 124. Graph. Mesopic modeling experiment (static portion)—calculated versus

predicted deltas (difference in mesopic luminance expressed as a percentage) between HPS 

lighting and each of the LED lighting types.

Dynamic Experiment

In the dynamic experiment, adaptation luminance significantly affected the distance at which 

peripheral targets were detected. An increase in the adaptation luminance resulted in an increase 

in the detection distance. An increase in the eccentricity was also associated with an increase in

the detection distance. Unlike the results of the Akashi and Rea study, where the main effect of 

overhead-lighting type was significant with the metal halide light (higher S/P ratio) source 

eliciting shorter response times compared with HPS lighting for targets of eccentricity 15 and 

23 degrees, the current study did not show a significant main effect of overhead-lighting type.(42)

However, there are some significant differences in experimental design between the two studies 

that could have contributed to the different results. For instance, Akashi and Rea’s study did not 

look at different adaptation luminances. Also, that study used a single vehicle speed, whereas the 

current study looked at multiple adaptation luminances and two different vehicle speeds.

Overhead-lighting type alone did not significantly affect the detection distance of peripheral 

targets, which is surprising because it was anticipated that light sources with higher S/P ratios 

would result in significantly better peripheral visual performance. However, overhead-lighting 

source, along with adaptation luminance, had an effect on visual performance of peripheral 

targets. Overhead-lighting types with higher S/P ratios (the 3,500-K LED and 6,000-K LED 
169

lighting), had better peripheral visual performance for 10-degree off-axis targets. There were no 

clear trends regarding overhead-lighting type for the 6- and 14-degree off-axis targets. 

For the HPS lighting at larger eccentricities (10 and 14 degrees), the mean detection distances at 

0.07 cd/m2 (0.020 fL) were higher than those at 0.1 cd/m2 (0.03 fL). This phenomenon could be 

the result of differential adaptation of the fovea and the periphery of the eye. The adaptation 

luminances used in the study were measured at the surface of the roadway; the luminance level 

decreased as distances increased off-axis from the roadway. At a foveal adaptation luminance of 

0.07 cd/m2 (0.020 fL) and an eccentricity of 14 degrees, the adaptation luminance of the 

periphery of the eye is much lower—possibly close to the lower end of the mesopic range—

making the peripheral rod receptors more sensitive and increasing detection distances. That 

increased sensitivity in the periphery at low adaptation levels could have resulted in better 

performance at lower lighting levels. HPS was the only overhead-lighting type that displayed 

differential adaptation at 0.07 cd/m2 (0.020) at 10 degrees of eccentricity. The low S/P ratio of 

the HPS lighting could have contributed to the longer detection distances at 0.1 cd/m2 (0.03 fL). 

Both LED lighting types have a higher S/P ratios (1.31 and 1.82), which could have made them 

perform better at this eccentricity for the same adaptation luminance.

The main effect of speed was not significant. However, the interaction between speed and 

eccentricity was significant. Figure 118 is repeated below as figure 125 for reference. Targets at 

higher eccentricities were detected at a greater distance at lower speeds than those at lower 

eccentricities and higher speeds. The target with 14-degree eccentricity at the 56 km/h (35 mi/h) 

had the highest mean detection distance (M = 139 m (456 ft)) and the target with 6-degree 

eccentricity at (80.47 km/h (50 mi/h) had the lowest mean detection distance (M = 95.9 m

(315 ft)). These results indicate that at higher speeds, drivers’ visual fields narrow, negatively 

affecting their ability to detect objects in their peripheral vision. These results align with existing 

research done using driving simulators where peripheral visual performance was poorer at higher 

speeds.(97) Because the higher order interactions of eccentricity and speed with either overheadlighting type or adaptation luminance were not significant, neither overhead-lighting type nor 

adaptation luminance influenced the change in the size of the visual field caused by 

driving speed.
170

1 m = 3.3 ft

1 km/h = 0.62 mi/h

Figure 125. Graph. Mesopic modeling experiment (dynamic portion)—effect of eccentricity 

and speed on detection distance.

Transition From Static to Dynamic

Even though in the static (fixed geometry) experiments overhead-lighting type, adaptation 

luminance, eccentricity, and their interactions were all significant in the dynamic experiment 

(nonfixed geometry), only the main effect of adaptation luminance and the three-way interaction 

among overhead-lighting type, adaptation luminance, and eccentricity were significant. This 

shows that in dynamic conditions, unlike static conditions, overhead lighting alone does not 

contribute significantly to peripheral visual performance. In dynamic conditions, peripheral 

visual performance very much depends on factors such as adaptation luminance, eccentricity, 

and speed. Even though for 10-degree targets, the LED light sources had marginally better 

performance, this result could not be replicated at either 6- or 14-degree eccentricities. Overall, 

there were no clear trends in the dynamic experiment that indicate which overhead lighting 

source resulted in better peripheral visual performance. The only trend clearly evident from the 

dynamic experiment is the role of adaptation luminance in peripheral visual performance. The 

results indicate that increased adaptation luminance results in target detection from longer 

distances, thereby resulting in increased peripheral visual performance. The same result was also 

observed in the static experiment, where threshold contrasts were lower at higher adaptation 

luminances. These trends are clearly illustrated in figure 126. 

In the static phase of the experiment, the threshold contrasts for the HPS overhead-lighting

sources were significantly higher compared with the LED sources. Taking this information into 

consideration, it was expected that the HPS lighting would perform poorly in the dynamic 

experiment. From the previous discussion, it is clear the all three overhead-lighting sources had 

similar performance, with only marginal differences. These differences in peripheral visual 

performance are illustrated in figure 127. 
171

1 cd/m2 = 0.3 fL

1 m = 3.3 ft

Figure 126. Graph. Mesopic modeling experiment—peripheral visual performance in both 

static and dynamic experiments at different adaptation luminances.
172

1 cd/m2 = 0.3 fL

Figure 127. Graph. Mesopic modeling experiment—peripheral visual performance of 

overhead lighting types at different adaptation luminances.

Age

The effect of age was not statistically significant either in the static experiment or in the dynamic 

experiment. This was interesting result, because it was expected that age would be a significant 

factor in peripheral visual performance. Because the average age of the older participants is 

67 years, it is also possible that the selected sample of older drivers was not old enough to see 

statistically significant changes, especially in peripheral contrast sensitivity. There were, 

however, differences in the threshold contrasts and detection distances of targets in both static 

and dynamic experiments; younger participants detected targets at lower threshold contrasts 

(MYoung = 1.27 m (4.17 ft) versus MOld = 1.81 m (5.94 ft)) and longer detection distances 

(MYoung = 122.58 m (402.17 ft) versus MOld = 109.91 m (360.60 ft)) compared with older 

participants. These differences were also not statistically significant.
173

CONCLUSIONS

The primary goal of this mesopic modeling experiment was to verify the applicability of the CIE 

Recommended System for Mesopic Photometry in naturalistic nighttime roadway situations, 

addressing the project objective of evaluating mesopic models in a driving environment.(1) The 

CIE Recommended System for Mesopic Photometry accurately predicted the mesopic 

luminances and contrasts for both the LED light sources at smaller eccentricities but not for the 

HPS light source. Differences between the mesopic and the photopic luminances of HPS lighting

and both the LED overhead-lighting sources indicated that the calculated differences closely 

followed the predicted differences from 0.07 to 0.2 cd/m2 (0.020 to 0.06 fL). Beyond this range,

the calculated differences leveled off, whereas the predicted values continued to decrease. The 

results show that the existing system does not take eccentricity into account, and the results show 

that the mesopic luminances calculated at greater eccentricities were less accurate. An ideal 

mesopic model should also use eccentricity as an input to accurately calculate mesopic 

luminances.

The secondary goal of the study was to assess the effect of an overhead lighting source’s 

spectrum on drivers’ peripheral visual performance in both static and dynamic conditions, 

addressing the project objective of evaluating the impact of spectra of overhead-lighting systems. 

In the static experiment, overhead-lighting type, adaptation luminance, and eccentricity 

significantly affected the threshold contrast at which participants were able to detect a target. 

Light sources with higher S/P ratios (3,500-K and 6,000-K LEDs) had lower threshold contrasts 

at detection in the periphery, compared with light sources with lower S/P ratios (2,100-K HPS), 

as suggested by existing research. An increase in eccentricity resulted in an increase in the 

threshold contrast. An increase in the adaptation luminance resulted in a decrease in the 

threshold contrast. In the dynamic experiment, peripheral visual performance was mainly 

affected by adaptation luminance. Increases in the adaptation luminance resulted in detection of 

objects from longer distances. Surprisingly, overhead-lighting type did not influence peripheral 

visual performance in the dynamic experiment. Targets with larger eccentricities and lower 

speeds were detected from longer distances, indicating that drivers’ visual fields narrow at higher 

speeds, which negatively affects peripheral visual performance. 

Other results included an interesting observation in the static experiment: threshold contrast at 

the lowest adaptation luminance (0.07 cd/m2 (0.020 fL)) was lower than at higher adaptation 

luminances for all overhead lighting types and at all eccentricities. In the dynamic experiment at 

0.07 cd/m2 (0.020 fL), the detection distance for all overhead-lighting types was also longer than 

that at 0.1 cd/m2 (0.03 fL), especially for 10- and 14-degree eccentricities. These findings could 

be attributed to the differential adaptation of the fovea compared with that at the periphery of the 

driver’s eye at the lowest adaptation luminance. However, more research is required to confirm 

these findings. Lastly, age was not a significant factor in either the static or dynamic portions of 

this experiment. 

175

CHAPTER 8. FINAL PERFORMANCE EXPERIMENT

INTRODUCTION

The purpose of the final experiment for this project was to further investigate variables of 

interest identified in the previous spectral effects experiments. This experiment melded many 

factors used in the previous experiments so the combination of their effects could be explored. 

The configuration of the MPI system performance experiment described in chapter 5 was 

incorporated to determine the impact on pedestrian visibility at various off-axis positions. The 

overhead lighting levels incorporated into this study were also used in the mesopic modeling 

experiment as described in chapter 7. These levels were first researched in the overhead-lighting 

level experiment described in chapter 6. The impact these factors had in previous experiments 

were more closely examined in this final performance experiment.

There were no significant main effects for the MPI system configuration on pedestrian-detection 

distances found in the MPI system performance experiment, but there were significant two-way 

interactions involving the MPI system. To gather more data on MPI system performance, it was 

included in this final experiment, but only two configurations were used to reduce the number of 

variables and test the most likely MPI system configurations: the MPI system off and the MPI 

system tracking a roadside pedestrian. 

The mesopic modeling experiment found mixed results for overhead lighting type. Overheadlighting type significantly affected threshold contrast in the static portion of the experiment, but 

as a main effect, it did not significantly affect detection distance. Thus, this final experiment 

collected more data using the three overhead-lighting types used in previous experiments. 

Although reducing the level of overhead lighting results in energy savings, this goal is not 

acceptable if it compromises visibility. A combination of overhead-lighting level and pavement 

type affects adaptation luminance, which in turn affects the eye’s behavior in the mesopic range. 

In this experiment, as in the mesopic modeling experiment, the experiment was conducted with 

five distinct adaptation luminances to determine the points at which visibility might be 

compromised. 

Driver behavior differs at varying driving speeds, possibly resulting in different levels of visual 

performance. Thus, this experiment included two different speeds common to roadways, 56 km/h 

and 80 km/h (35 and 50 mi/h). 

Pedestrians were employed as the detection targets for this experiment because the current state 

of machine-vision technology limits an MPI system’s ability to detect and highlight small 

targets. The pedestrians were placed at various distances from the roadway. They wore different 

colors and appeared on both the left and right sides of the road. These conditions were selected to 

mimic naturalistic conditions, to reduce the chance that a participant might predict pedestrian 

position, and to introduce the possibility of detection at multiple eccentricities because 

eccentricity affects mesopic visual performance. Eccentricity was not controlled in this 

experiment, however, because participants were not instructed to keep their eyes focused on the 

roadway. To create the possibility for peripheral detection, pedestrians were placed at various 
176

offsets with respect to the driving lane. Participants could scan for pedestrians, and detection 

could take place either in the fovea or in the periphery. 

Research Objectives

The research objectives of the final performance experiment were the same as those of the 

overall project. They included evaluating the following:

• Impact of the spectra of overhead-lighting systems on driver visual performance.

• Interaction of vehicle headlamps and overhead lighting in terms of object visibility.

• Applicability of mesopic models and scaling factors in a roadway lighting design.

• Impact of a peripheral illumination system on driver visual performance.

EXPERIMENTAL DESIGN

A 2 by 3 by 5 by 2 by 2 by 4 by 2 by 3 mixed-factors experiment was designed to measure the 

effect of age, overhead lighting type, adaptation luminance, MPI system configuration, vehicle 

speed, visual angle, pedestrian position, and pedestrian clothing color on pedestrian-detection 

and color-recognition distances. The variables used in the experiment are listed in table 34,

through table 36.

Table 34. Final experiment—independent variables and values.

Independent Variable Levels

Age Younger (25–35), Older (65+)

Overhead-Lighting Type 2,100-K HPS, 3,500-K LED, 6,000-K LED

Adaptation Luminance 0.07, 0.1, 0.2, 0.3, 0.5 cd/m2 (0.020, 0.03, 0.06, 0.09, 

0.15 fL)

MPI System Configuration Off, Tracking

Speed 56 km/h (35 mi/h), 80 km/h (50 mi/h)

Offset 3.0, 7.7, 8.9, 21.0 m (9.8, 25, 29, 69 ft)

Pedestrian on Left or Right Left, Right

Pedestrian Clothing Color Gray, Red, Blue

Table 35. Final experiment—covariate and measurement method.

Dependent Variables Measurement Method

Contrast Weber contrast, measured with ProMetric® system

Table 36. Final experiment—dependent variables and measurement method.

Dependent Variables Measurement Method

Pedestrian-Detection Distance Participant first sees pedestrian

Pedestrian Color-Recognition 

Distance

Participant first correctly identifies pedestrian clothing 

color
177

Independent Variables

Age

Participants were divided into the same age groups as previous experiments: drivers 

(25–35 years old) and older drivers (65 years old and older). 

Overhead Lighting Type 

All three overhead-lighting systems were used.

Adaptation Luminance

Adaptation luminance was the luminance, viewed from inside the test vehicle, for a given 

combination of overhead-lighting level and pavement type. The same design was used here as in 

the mesopic modeling experiment, and adaptation luminances are reported in table 37. 

Table 37. Final performance experiment—adaptation luminance per overhead lighting 

level and pavement type.

Overhead-Lighting Level

Luminance on Concrete

(cd/m2 (fL))

Luminance on Asphalt

(cd/m2 (fL))

Low 0.1113 (0.032) 0.0716 (0.021)

Medium 0.3535a (0.103) 0.1987 (0.056)

High 0.5385 (0.157) 0.3535a (0.103) a

Same luminance.

MPI System Configuration

The mockup MPI system was configured to behave in two ways: off, with headlamps aimed 

ahead, like in a normal vehicle; and tracking, with headlamps illuminating a pedestrian from 

about 183 m (600 ft) away, and then swiveling the headlamp to keep the pedestrian in the beam 

as the vehicle approached. The configurations are illustrated in figure 59.

Speed

The participant drove the vehicle at 56 and 80 km/h (35 and 50 mi/h). 

Pedestrian Offset

The eccentricity at which participants detect pedestrians for dynamic experiments cannot be 

fixed, because as the driver approaches the pedestrian, the eccentricity is constantly changing. 

Also, for this experiment, participants were not instructed to focus along the roadway. To create 

the possibility for peripheral detection, pedestrians were placed at four offsets with respect to the 

travel lane. To determine the pedestrian positions, a theoretical detection distance was fixed at 

83 m (277 ft), because based on a 1-degree downward viewing angle with a vehicle height of 

1.45 m (4.76 ft), this is the resulting distance.(5) Four offsets were calculated based on detection 

from that distance—3.0, 7.7, 8.9, and 21.0 m (9.8, 25, 29, and 69 ft)—and are described in

figure 128. 
178

1 m = 3.3 ft

Figure 128. Diagram. Final performance experiment—pedestrian positions and offsets 

from the roadway.

All pedestrian positions were in the lighted section of the Smart Road, and all pedestrian 

positions had VI on the pedestrian’s face as similar as possible to each other within a specified 

lighting condition. VI levels are listed in table 38. 
179

Table 38. Final performance experiment—VI for all pedestrian positions, overhead lighting 

levels, and visual angles.

Offset (m) Light Level Average VI (lx (fc))

Standard Deviation

(lx (fc))

3.0 Low 0.35 (0.03) 0.027 (0.0025)

3.0 Medium 1.05 (0.1) 0.307 (0.0285)

3.0 High 1.6 (0.15) 0.316 (0.0294)

7.7 Low 0.27 (0.025) 0.027 (0.0025)

7.7 Medium 0.88 (0.082) 0.267 (0.0248)

7.7 High 1.25 (0.116) 0.261 (0.0242)

8.9 Low 0.27 (0.025) 0.023 (0.0021)

8.9 Medium 0.88 (0.082) 0.342 (0.0318)

8.9 High 1.3 (0.121) 0.343 (0.0319)

21.0 Low 0.1 (0.009) 0.047 (0.0044)

21.0 Medium 0.36 (0.033) 0.287 (0.0267)

21.0 High 0.47 (0.044) 0.298 (0.0277)

1 m = 3.3 ft

Pedestrian on Left or Right

The pedestrian stood on the left or right of the road with respect to the participant vehicle. 

Pedestrians at 3.0 m (9.8 ft) offset were located on both sides of the road. Pedestrians at 7.7 m 

(25 ft) offset were located only on the left side of the road, and pedestrians at 8.9 and 21.0 m 

(29 and 69 ft) offset were located only on the right side of the road, as shown in figure 128.

Pedestrian Clothing Color

Pedestrians wore clothing in three colors: gray, red, and blue, as described in chapter 3. 

Covariate

Contrast

The luminance of the pedestrians and the background behind them was measured using the 

ProMetric® luminance system from 83 m (277 ft) up the road from the pedestrian, the same 

distance at which RP-8 advises roadway lighting designers to measure roadway luminance from 

a luminaire.(5) The Weber contrast was calculated from that measurement.

Dependent Variables

Detection and color-recognition distances were the dependent variables. Orientation-recognition 

distance was not measured, and pedestrians all faced toward the roadway. 
180

METHODS

Facilities and Equipment

This experiment was conducted on the Virginia Smart Road using the same test vehicles as in 

previous experiments. The headlamps were the HID headlamps installed on the test vehicles for 

the previous experiments in this project but with low-intensity neutral-density filter with a CCT 

of 3,530 K and transmittance of .3413. 

Participants

Participants were recruited and screened as described in Chapter 3; however, for this experiment, 

they were not tested for UFOV. 

A total of 36 participants performed the experiment, 18 older and 18 younger. Within each age 

group, participants were divided equally by gender. Mean and standard deviation of participant 

age, visual acuity, mesopic visual acuity, and low contrast visual acuity are listed in table 39. 

Table 39. Final performance experiment participant characteristics.

Participant Characteristic

Older 

Drivers

Mean

Older 

Drivers

Standard 

Deviation

Young 

Drivers

Mean

Younger 

Drivers

Standard 

Deviation

Age 70.4 6.3 30.5 3.2

Visual Acuity 20/21.6 5.25 20/17.3 3.53

Mesopic Visual Acuity 20/38.1 13.5 20/25.8 6.4

Low Contrast Visual Acuity 20/27.8 7.3 20/20.9 5.3

Procedure

Participants were recruited, screened, and directed to the Smart Road as described in chapter 3. 

Each participant attended three experimental sessions, one for each overhead-lighting type. Each 

session consisted of 1 practice run and 12 experimental runs up and down the Smart Road, for a 

total of 24 trials for each lighting type. Lighting level was changed twice during each session, so 

all three intensities were tested. Two participants in two vehicles typically completed the 

experiment at one time. 

During the experiment, on-road experimenters adjusted the overhead lighting level and 

pedestrian positions for the each experimental run. In-vehicle experimenters directed participants 

to drive at the speed called for by the protocol and recorded detection distances. 

Data Analysis

Detection and Color Recognition Distances

After video data reduction, an ANOVA was used to determine whether the independent variables 

significantly affected pedestrian-detection and color-recognition distances. When results were 
181

significant, Tukey HSD tests were performed to determine which factors differed from each 

other. When Tukey HSD tests are reported on charts, data points sharing a letter do not 

significantly differ from each other.

For missed detections, Fisher’s Exact Test was conducted to determine the degree of difference 

between two binomial variables. The results of the test are reported as the probability that 

one variable occurs over another variable.

This experiment focused mainly on gray-target detection; colored targets were not distributed 

evenly among the visual angles. Therefore, although data analysis was performed on all target 

colors, it was repeated only for the gray targets. 

Contrast 

Weber contrast was calculated for the pedestrians the same way it was calculated for the other 

experiments in this project. An ANCOVA using contrast as a covariate was performed to 

determine the relationship between contrast and detection distance. Contrast was not analyzed 

with respect to color-recognition distance. Detection is more crucial for driving safety than color 

recognition. Only luminance contrast, not color contrast, was measured.

Contrast and detection-distance data were analyzed separately for when the MPI system was off 

and when it was on, but results indicated the MPI system introduced a great deal of variation in 

the data—system-on data and system-off data were not necessarily comparable. In addition, it 

was not possible to calculate the contrast for the instant a participant detected a pedestrian, 

because it was calculated from luminance measurements taken 83 m (277 ft) along the road from 

the pedestrian. Actual detection occurred at between 124 and 97.4 m (407 and 320 ft), 

introducing error in the contrast measurement. 

RESULTS 

Main Effects

Main effects on detection distance and color-recognition distance occurred for many of the same 

independent variables. Significant main effects are listed in table 40 with results explained in 

detail following the table.

Table 40. Final performance experiment results.

Factor(s)

Detection 

Distance 

F

Detection 

Distance

p

ColorRecognition 

Distance

F

ColorRecognition 

Distance

p

Adaptation Luminance 5.24 0.0006a 2.75 0.0313a

Age 2.9 0.0987 2.3 0.1401

Color 20.69 < 0.0001a 2.46 0.0947

Offset 3.28 0.0253a 3.1 0.0316a

Overhead-Lighting Type 0.14 0.8718 0.04 0.9622
182

Factor(s)

Detection 

Distance 

F

Detection 

Distance

p

ColorRecognition 

Distance

F

ColorRecognition 

Distance

p

MPI Configuration 0.5 0.4865 1.68 0.205

Speed 1.43 0.2414 0.13 0.7218

Age by Color 1.38 0.2589 0.57 0.5706

Age by MPI Configuration 0.35 0.5605 1.12 0.2989

Age by Speed 0.02 0.8791 0.68 0.4164

Color by MPI Configuration 2.62 0.0816 0.36 0.7008

Color by Speed 0.25 0.7782 0.35 0.7097

Offset by Adaptation Luminance 6.52 < .0001a 1.72 0.0777

Offset by Age 0.03 0.9919 0.41 0.7481

Offset by Color 2.9 0.0649 0.95 0.3967

Offset by Overhead-Lighting Type 1.97 0.0799 1.3 0.2666

Offset by MPI Configuration 0.45 0.6422 0.27 0.7672

Offset by Speed 3.55 0.0184a 2.01 0.1201

Overhead-Lighting Type by Adaptation 

Luminance

2.78 0.0071a 0.77 0.6298

Overhead-Lighting Type by Age 0.04 0.9561 0.1 0.9089

Overhead-Lighting Type by Age by 

Color

0.69 0.6027 1.03 0.402

Overhead-Lighting Type by Color 0.81 0.5236 1.38 0.2523

Overhead-Lighting Type by MPI

Configuration

1.35 0.2734 0.29 0.7535

Overhead Lighting Type by Speed 0.56 0.5784 0.46 0.6331

MPI Configuration by Speed 0.26 0.6137 0 0.9567

Adaptation Luminance by Speed 2.31 0.0619 2.23 0.0697

Age by Adaptation Luminance 1.34 0.2605 0.38 0.8241

Age by Adaptation Luminance by 

Speed

1.07 0.3727 1.14 0.3387

Age by Color by Adaptation 

Luminance

1.08 0.3768 0.6 0.7327

Age by Color by MPI Configuration 2.98 0.0588 1.27 0.29

Age by Color by Speed 0.24 0.7892 0.06 0.9394

Age by MPI by Adaptation Luminance 0.25 0.9066 1.79 0.1356

Age by MPI by Speed 0.56 0.4593 0.15 0.7002

Color by Adaptation Luminance 1.16 0.3296 1.17 0.3247

Color by MPI Configuration By 

Adaptation Luminance

1.08 0.3759 2.23 0.0435a

Color by MPI Configuration by Speed 0.08 0.9245 0.56 0.5725

Offset by Adaptation Luminance by 

Speed

1.92 0.0432a 1.53 0.1291
183

Factor(s)

Detection 

Distance 

F

Detection 

Distance

p

ColorRecognition 

Distance

F

ColorRecognition 

Distance

p

Offset by Age by Adaptation 

Luminance

1.9 0.0462a 1.24 0.2652

Offset by Age by Color 2.55 0.0889 2.88 0.0676

Offset by Age by MPI Configuration 0.54 0.5828 2.06 0.1343

Offset by Age by Speed 1.28 0.287 3.51 0.0194a

Offset by Color by Adaptation 

Luminance

0.24 0.7887 0.49 0.6187

Offset by Color by MPI Configuration 0.61 0.548 0.26 0.7736

Offset by Overhead-Lighting Type by 

Adaptation Luminance

1.27 0.1971 0.97 0.5041

Offset by Overhead-Lighting Type by 

Age

0.47 0.8298 0.44 0.851

Offset by Overhead-Lighting Type by 

Color

6.72 0.0004a 2.71 0.0503a

Offset by Overhead-Lighting Type by 

MPI Configuration

0.4 0.806 1.7 0.1578

Eccentricity by Overhead-Lighting

Type by Speed

2.12 0.0583 0.99 0.4386

Offset by MPI Configuration by 

Adaptation Luminance

0.46 0.8352 0.97 0.445

Offset By MPI Configuration by Speed 0.81 0.447 2.98 0.0568

Overhead-Lighting Type by Age by 

Adaptation Luminance

1.37 0.2168 0.35 0.9457

Overhead-Lighting Type by Age by 

MPI Configuration

1.25 0.2998 0.01 0.9879

Overhead-Lighting Type by Age by 

Speed

0.32 0.7286 0.14 0.8671

Overhead-Lighting Type by Color by 

Adaptation Luminance

2.03 0.0254a 0.94 0.5134

Overhead-Lighting Type by Color by 

MPI Configuration

1.1 0.364 4.01 0.0065a

Overhead-Lighting Type by Color by 

Speed

0.91 0.4639 0.8 0.5343

Overhead-Lighting Type by MPI

Configuration by Adaptation 

Luminance

1 0.4395 2.07 0.043a

Overhead-Lighting Type by MPI

Configuration By Speed

0.26 0.7696 0.56 0.5771

MPI Configuration by Adaptation 

Luminance

0.57 0.6855 1.4 0.2387

a

Significant at p < 0.05.
184

Table 41. Final performance experiment significant main effects, gray only.

Independent Variable

Detection 

Distance 

F

Detection 

Distance

p

Adaptation Luminance 8.26 < 0.0001a

Age 7.16 0.0119a

Offset 1.75 0.163

Overhead-Lighting Type 1.71 0.1966

MPI Configuration 0.05 0.8272

Speed 0.62 0.4379

Adaptation Luminance by Speed 2.35 0.0579

Age by Adaptation Luminance 0.64 0.6327

Age by MPI Configuration 1.66 0.2079

Age by Speed 0.1 0.7484

Offset by Adaptation Luminance 7.69 <0.0001a

Offset by Age 0.41 0.7498

Offset by Overhead-Lighting Type 1.46 0.2017

Offset by MPI Configuration 1.44 0.2437

Offset by Speed 3.96 0.0114a

Overhead-Lighting Type by Adaptation Luminance 0.43 0.9033

Overhead-Lighting Type by Age 0.41 0.6693

Overhead-Lighting Type by MPI Configuration 0.41 0.6701

Overhead-Lighting Type by Speed 0.6 0.5543

MPI Configuration by Adaptation Luminance 0.28 0.8935

MPI Configuration by Speed 0.25 0.6219

Age by Adaptation Luminance by Speed 1.04 0.3909

Age by MPI Configuration by Adaptation Luminance 0.91 0.46

Age by MPI Configuration by Speed 0.12 0.7306

Offset by Adaptation Luminance by Speed 2.33 0.0123a

Offset by Age by Adaptation Luminance 2.23 0.0172a

Offset by Age by MPI Configuration 1 0.3713

Offset by Age by Speed 1.24 0.3001

Offset by Overhead Lighting Type by Adaptation Luminance 1.21 0.2467

Offset by Overhead Lighting Type by Age 0.21 0.974

Offset by Overhead Lighting Type by MPI Configuration 0.74 0.5706

Offset by Overhead Lighting Type by Speed 2.2 0.0505

Offset by MPI Configuration x Adaptation Luminance 0.86 0.5222

Offset by MPI Configuration x Speed 0.83 0.4384

Overhead Lighting Type by Age by Adaptation Luminance 1.31 0.2437

Overhead Lighting Type by Age by MPI Configuration 3.37 0.0464a

Overhead Lighting Type by Age by Speed 3.68 0.0356a
185

Independent Variable

Detection 

Distance 

F

Detection 

Distance

p

Overhead-Lighting Type by MPI Configuration by Adaptation 

Luminance 0.91 0.5072

Overhead-Lighting Type by MPI Configuration by Speed 1.78 0.1841 a

Signficant at p < 0.05.

Adaptation Luminance

The results for adaptation luminance’s effect on detection and color-recognition distances are 

shown in figure 129. When all pedestrian clothing colors were analyzed, it was found adaptation 

luminance significantly affected detection distance, with mean detection distance at 0.1 cd/m2

(0.03 fL) shorter (M = 95.7 m (314 ft)) than at the other adaptation luminances (all more than 

109 m (358 ft)). The same effect was seen in the previous experiment. Adaptation luminance 

also significantly affected color-recognition distance, following the same trend as described for 

detection distance but to a lesser extent. This could be because of poor target contrast at that 

luminance, a condition described in more detail in the contrast section of the results.

When only gray-clothed pedestrians were analyzed, it was found that adaptation luminance also 

significantly affected detection distance, with a general trend of higher adaptation luminances 

having longer detection distances. 

1 cd/m2 = 0.3 fL

1 m = 3.3 ft

Figure 129. Graph. Final performance experiment—detection distance and colorrecognition distance by adaptation luminance.
186

Age

Age significantly affected pedestrian-detection distance when data for only gray-clothed 

pedestrians were analyzed. The mean detection distance for older participants (M = 107 m 

(351 ft)) was significantly shorter than that for younger participants (M = 135 m (440 ft)). 

The Snellen visual acuity test performed during participant screenings found that older 

participants had poorer visual acuity than younger participants. While normal vision is 20/20, 

younger participants had average visual acuity of 20/17.2, while older participants had an 

average visual acuity of 20/21.6. Older participants also had poorer contrast sensitivity than 

younger participants. When the contrast sensitivity exam was administered with lighter-colored 

gray letters on the Snellen eye chart, younger participants had an average acuity of 20/20.8, 

while the older participants had an average of 20/27.8. These two exams were performed in 

photopic conditions. A third Snellen acuity test was performed in mesopic conditions; for that 

exam, younger participants had an average visual acuity of 20/25.7, while older participants had 

an average visual acuity of 20/38.1. As the difficulty of the Snellen eye exams increased, from 

normal-to-low contrast to mesopic conditions, older participants’ visual acuity decreased when 

compared with the younger participants. That difference in visual acuity was reflected in the 

gray-only target detection distances because the colored targets were distributed unevenly among 

the visual angles, introducing variations in the detection distances not caused by color alone. 

Pedestrian Clothing Color

Mean detection distances were significantly different among the three pedestrian clothing colors, 

with gray (M = 123 m (404 ft)) seen from farther away than red (M = 87.3 m (286 ft)) and blue 

(M = 79.4 m (260 ft)). Mean color-recognition distances did not differ significantly by pedestrian 

clothing color. Pedestrian clothing color results are shown in figure 130.
187

1 m = 3.3 ft

Figure 130. Chart. Final performance experiment—detection and color-recognition 

distances by pedestrian clothing color.

The experiment was designed so that pedestrians wearing gray were seen 10 times more 

frequently than those wearing red or blue, and the visual angle was not divided evenly among the 

colors. Therefore, definitive results regarding pedestrian clothing color cannot be obtained. 

Offset

For all pedestrian clothing colors, detection distance differed significantly with offset. As offset

increased, detection distance decreased. A Tukey HSD test found that the only detection 

distances that differed significantly from each other were those at 3.0 m (9.8 ft) offset

(M = 124 m (407 ft)) and 21.0 m (69 ft) offset (M = 97.4 m (320 ft)). This result was expected, 

because visual acuity decreases as offset increases and objects are viewed more in the eye’s 

periphery. Figure 131 illustrates the results. 

Color-recognition distance differed significantly with offset but followed a different trend

(figure 131). Color-recognition distance was shortest at 21.0 m (69 ft) offset (M = 58.8 m 

(193 ft)) and longest at 8.9 m (29 ft) offset (M = 76.6 m (251 ft)), but the standard deviation of 

the color-recognition data, between 47.5 and 68.9 m (156 and 225 ft) depending on offset, 

warrants caution when attempting to draw meaning from the results. 
188

1 m = 3.3 ft

Figure 131. Chart. Final performance experiment—detection distance and colorrecognition distance versus offset for all pedestrian clothing colors.

MPI System Configuration

The MPI system configuration did not significantly affect detection distance but did significantly 

affect the percent of detections, as tested with a Fisher’s Exact Test. The probability that MPI-off 

resulted in significantly more misses than MPI-on was significant. With the MPI system on, 

drivers either missed or did not detect 24 percent of the pedestrians. With the MPI system off, 

drivers missed 38 percent of pedestrians. 

Two-Way Interactions

There were a number of significant and marginally significant two-way effects on detection 

distance and percent of missed detections. 

Offset and Adaptation Luminance

For all pedestrian clothing colors, the combination of adaptation luminance and offset affected 

detection distance (figure 132). At an adaptation luminance of 0.07 cd/m2 (0.020 fL), detection 

distances decreased with increasing offset. This is expected, given that visual acuity decreases as 

eccentricity increases, and if drivers’ eyes were focused on the roadway, higher offset would 

correspond to higher eccentricity. At higher adaptation luminance levels, however, the 

relationship between offset and detection distance changed; detection distance decreased with 

increasing offset, but the trend was less clear and more general. A similar trend was seen for only 

gray-clothed pedestrians, as shown in figure 133. 
189

1 cd/m2 = 0.3 fL

1 m = 3.3 ft

Figure 132. Chart. Final performance experiment—detection distance by adaptation 

luminance and offset for all clothing colors.

1 cd/m2 = 0.3 fL

1 m = 3.3 ft

Figure 133. Chart. Final performance experiment—detection distance by adaptation 

luminance and offset for gray-clothed pedestrians only.
190

Offset and Age

The number of missed detections at each location indicated that there was a much greater 

probability for missed detections at 21 m (69 ft) offset than at the other offset distances

(figure 134). The effect was greater for older participants, who missed 61 percent of pedestrians 

at 21.0 m (69 ft) offset, than it was for younger participants, who missed 29 percent of 

pedestrians at 21.0 m (69 ft) offset (Fisher’s Exact Test, p ≤ 0.0001). 

1 m = 3.3 ft

Figure 134. Chart. Final performance experiment—missed detections by age and offset.

Offset and Overhead Lighting Type

For gray-clothed pedestrians, the interaction effect between offset and overhead-lighting type 

was not significant; however, there were differences in detection distances among the overheadlighting types that depended on offset. At 3.0 and 7.7 m (9.8 and 25 ft) offset, the overheadlighting types had similar detection distances. At 8.9 and 21.0 m (29 and 69 ft) offset, the 

6,000-K LED lighting had greater detection distances, possibly because the 6,000-K LED 

lighting’s spectral distribution is more efficient in the mesopic range, and mesopic effects are 

only seen in the periphery. At 21.0 m (69 ft) offset, the lighting types’ detection distances 

increased with increasing color temperature. The results are illustrated in figure 135.
191

1 m = 3.3 ft

Figure 135. Chart. Final performance experiment—detection distance by offset and 

overhead-lighting type for gray-clothed pedestrians. 

Offset and MPI System Configuration 

Offset and MPI system did not have an interaction effect on detection distance but did have an 

interaction effect on missed detections. The MPI system was not used for pedestrians at the 

3.0 m (9.8 ft) offset. When detection rates were analyzed without the 3.0 m (9.8 ft) offset data, it 

was found that when the MPI system was on, participants missed the pedestrians at 7.7 and 8.9 m 

(25 and 29 ft) offsets more than when it was off (figure 136). Conversely, for the pedestrians at 

21.0 m (69 ft) offset, participants missed more pedestrians with the MPI system off than with it 

on (Fisher’s Exact Test, p ≤ 0.0001). 

Similarly to its main effect, the MPI system’s on or off status did not affect detection distance 

but did affect detection rate; however, it did so differently for different offsets. It was most 

beneficial in increasing detection rates for the pedestrian at 21.0 m (69 ft) offset. It could have 

had a slight negative effect on detection rates for the pedestrians at 7.7 and 8.8 m (25 and 29 ft) 

offsets because the MPI system swiveling beam could have caused an extreme change in 

contrast. At the 7.7 m (25 ft) offset, missed detections were significantly greater with the MPI 

system on than off (Fisher’s Exact Test, p = 0.0226). At the 8.9 m (29 ft) offset, the difference 

was not significant. 
192

1 m = 3.3 ft

Figure 136. Chart. Final performance experiment—missed detections by MPI system 

configuration and offset.

Offset and Speed

For all clothing colors, participants driving 80 km/h (50 mi/h) detected the pedestrians from 

slightly farther away (M = 111 m (364 ft)) than those driving 56 km/h (35 mi/h) (M = 119 m 

(390 ft)), but the main effect was not significant. However, a two-way interaction between speed 

and offset found that detection distances were longer for higher speeds at 3.0, 8.9, and 21.0 m 

(9.8, 29, and 69 ft) offsets but not at the 7.7 m (25 ft) offset (figure 137). 
193

1 m = 3.3 ft

1 km/h = 0.62 mi/h

Figure 137. Graph. Final performance experiment—detection distance by speed and offset

for all clothing colors.

Some explanations for these results could stem from roadway design, driving speed, and 

scanning behavior. At faster speeds, drivers are likely more vigilant and might have different 

scanning behavior. Drivers at higher speeds might be more vigilant, scanning might be restricted 

to within a few degrees of the roadway on the left but cover a broader angle on the right, or rely 

more on periphery. Therefore, at higher speeds, more-vigilant drivers would detect pedestrians 

from farther away within their scanning region. The pedestrians at 7.7 m (25 ft) offset, 

corresponding to 5 degrees, were only on the left side of the road, and the location of the vehicle 

A pillar might have hindered scanning patterns and limited detection distances at that visual 

angle for both speeds.

A similar effect was seen when only data for gray-clad pedestrians were analyzed. Detection 

distances were longer for higher speeds at 3.0 and 21.0 m (9.8 and 69 ft) offsets (2 and 

14 degrees at 83 m (277 ft)), but detection distances were shorter for higher speeds at 7.7 and 

8.9 m (25 and 29 ft) offsets (5 and 6 degrees at 83 m (277 ft)) (figure 138).
194

1 m = 3.3 ft

1 km/h = 0.62 mi/h

Figure 138. Graph. Final performance experiment—detection distance by speed and offset 

for gray only.

Overhead Lighting Type and Adaptation Luminance

To isolate the effects of overhead lighting and adaptation luminance, figure 139 shows their 

combined effect on detection distance in cases when the MPI system was off. Figure 135 shows

that overhead lighting with a higher CCT and with an SPD more efficient in the mesopic range 

had longer detection distances but only for larger eccentricities, where mesopic effects are

expected. One would also expect higher adaptation luminances to correlate with longer detection 

distances for large-object detection because the general environment is brighter. However, that 

effect was only seen for the LED lighting; at adaptation luminances of 0.07 and 0.1 cd/m2 (0.020 

and 0.03 fL), detection distances for the LED lighting types were shorter than for adaption 

luminances of 0.2 cd/m2 (0.06 fL) and higher. There may exist a threshold adaptation luminance,

above which brighter lighting does not significantly increase the visibility of large objects such 

as pedestrians, thus creating a limited return to additional lighting. The interaction of adaptation 

luminance and color might also explain the difference because overhead-lighting type and 

adaptation luminance did not significantly affect detection distances for gray-clothed pedestrians. 
195

1 cd/m2 = 0.3 fL

1 m = 3.3 ft

Figure 139. Chart. Final performance experiment—detection distance by adaptation 

luminance and overhead-lighting type with the MPI system off.

Contrast and Detection Distance

Weber contrast was calculated from luminance measurements taken 83 m (277 ft) along the road 

from the pedestrian positions. An ANCOVA was performed using contrast as a covariate. 

Contrast and detection-distance data were only analyzed for gray pedestrians because only 

luminance contrast, not color contrast, was measured. The ANCOVA was only performed for 

detection distance. The results are listed in table 42. 
196

Table 42. Final performance experiment results for Weber contrast and detection distance 

for gray pedestrians only. 

Factor(s)

Detection 

Distance

F

Detection 

Distance

p

Adaptation Luminance 8.46 < 0.0001a

Offset 1.88 0.1393

Overhead-Lighting Type 1.61 0.2142

MPI System Configuration 0.04 0.8386

Speed 0.69 0.4136

Offset by Adaptation Luminance 6.57 < 0.0001a

Offset by Overhead-Lighting Type 1.49 0.1909

Offset by MPI System Configuration 1.45 0.2413

Overhead-Lighting Type by Adaptation Luminance 0.39 0.9256

Overhead-Lighting Type by MPI System Configuration 0.43 0.655

MPI System Configuration by Adaptation Luminance 0.28 0.8897

Age by MPI System Configuration by Adaptation Luminance 0.92 0.4546

Offset by Overhead-Lighting Type by Adaptation Luminance 1.23 0.2313

Offset by Overhead-Lighting Type by MPI System Configuration 0.74 0.5677

Offset by MPI System Configuration by Adaptation Luminance 0.97 0.4463

Overhead-Lighting Type by MPI System Configuration by

Adaptation Luminance 0.98 0.4582 a

Signficant at p < 0.05.

Adaptation Luminance

The detection distance at 0.1 cd/m2 (0.03 fL) was lower than at the other adaptation luminance 

levels, and the Weber contrast results could explain why. At 0.1 cd/m2 (0.03 fL), the pedestrian 

was seen in positive contrast, but at all the other adaptation luminance levels, the contrast was 

negative, as illustrated in figure 140. At some point, when the vehicle approached the pedestrian, 

the pedestrian contrast passed from negative to positive. Detection is much more difficult as 

contrast passes through zero, causing longer detection distances. 
197

1 cd/m2 = 0.3 fL

1 m = 3.3 ft

Figure 140. Graph. Final performance experiment—detection distance and Weber contrast 

by adaptation luminance.

MPI and Adaptation Luminance

When the MPI system was on, detection distances were shorter, albeit not statistically 

significantly. That effect was greatest at 0.1 cd/m

2 (0.03 fL). At most adaptation luminance 

levels, the MPI-on condition caused the Weber contrast to be closer to zero because the 

headlamps illuminated the pedestrian, bringing him or her closer to positive contrast, as shown in

figure 141. Thus, while the MPI system illuminated the pedestrian, it also decreased contrast, 

making detection more difficult. The MPI system could have also distracted the participant, 

negatively affecting detection distances. 
198

1 cd/m2 = 0.3 fL

1 m = 3.3 ft

Figure 141. Graph. Detection distance and Weber contrast by adaptation luminance for 

MPI system on and off. 

Offset

As offset increased, contrast approached zero and detection distance decreased (figure 142). 

There was a sharp increase in contrast, and contrast passed through 0, 8.9, and 21.0 m (29 and 

69 ft) offsets. There was also a sharp decrease in detection distance between 8.9 and 21.0 (29 and 

69 ft) offsets. Contrast polarity factor (CFP), described by Adrian, states that objects in negative 

contrast are easier to detect than those in positive contrast.(54) In this experiment, the contrast at 

the 21.0 m (69 ft) offset was positive, but at the other offsets, the contrast was negative. That 

could help explain why detection distances were so much shorter at the 21.0 m (69 ft) offset than 

at the other offsets, even though the contrast at 21.0 m (69 ft) offset was close to 0.1.

Probability summation, or the probability of detecting an object with both eyes, cannot be 

factored into the visibility of the targets at the various offsets because of this study’s lack of 

control over the participants’ eye gaze behavior, and offsets did not necessarily correspond to 

eccentricity. 
199

1 m = 3.3 ft

Figure 142. Graph. Final performance experiment—detection distance and Weber contrast 

by offset.

DISCUSSION

The main purpose of this experiment was to further evaluate the variables of interest identified in 

previous experiments in this project.

One of the outcomes of the scoping experiment was that more information was needed on the 

effect of overhead-lighting type and level on peripheral visibility, and a project objective was to 

address the effect of the SPD of overhead lighting on driver visual performance. The mesopic 

modeling experiment found that the spectral distribution of overhead lighting affected detection 

distances but only at higher eccentricities. This experiment attempted to extend the investigation 

to determine the effects of overhead lighting’s SPD on visibility while driving.

The final performance experiment found no significant differences in detection and colorrecognition distances between overhead-lighting types at any offset, although results showed a 

trend toward longer detection distances with 6,000-K LED lighting at greater offsets. The results 

do not strongly support a spectral effect of overhead lighting on mesopic visibility in the 

periphery. That result, different from the one in the mesopic modeling experiment where 

eccentricity was fixed, means that drivers depended on glance patterns to scan their driving 

environment and detect peripheral objects. Therefore, they likely detected objects in the fovea, 

where mesopic effects do not occur. 

MPI

Evaluating the performance of drivers when using a peripheral illumination system was a project 

objective. This experiment found that the MPI system configuration did not significantly affect 
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detection distance. This is consistent with findings from the spectral interaction experiment, 

where the headlamp’s effect on visibility was much less than that of the overhead-lighting level. 

In the final performance experiment, most detection occurred at distances greater than the 75 to 

90 m (246 to 295 ft) window where the headlamps first illuminated the pedestrian. Therefore, in 

most cases, the MPI system highlighted the off-axis pedestrians after participants had 

detected them.

The MPI system configuration did significantly affect the probability that a pedestrian was 

detected, but the results varied among offsets. For the pedestrian at 21.0 m (69 ft) offset, the MPI 

system significantly increased detection rates. For the pedestrians at 7.7 and 8.9 m (25 and 29 ft) 

offsets, the MPI system slightly decreased detection rates. Congruent with the results of the 

spectral interaction experiment, at the 21.0 m (25 ft) offset location, luminance from overhead 

lighting was low, and headlamps were the primary light sources driving detection. At the 7.7 and 

8.9 m (25 and 29 ft) offset locations, the pedestrians were largely illuminated by overhead 

lighting and were in negative contrast. When the headlamp light struck them, it reduced their 

contrast until they shifted into positive contrast. Near-zero contrast conditions reduced visibility 

and detection distances. 

Because the MPI system only increased the detection rate for pedestrians farthest from the 

roadway, it is important to consider how far an object is from the roadway to evaluate its 

potential as a hazard. The pedestrian at the highest offset was 21.0 m (69 ft) to the right of the 

test vehicle (figure 128). If the pedestrian is traveling directly toward the road, and the vehicle is 

traveling at 80.4 km/h (50 mi/h) and 83 m (272 ft) from the point where the pedestrian would 

intersect the road, the pedestrian would have to be moving 20.3 km/h (12.6 mi/h) for it to 

intersect the vehicle and cause a collision. Large animals native to North America, such as deer 

and black bear, can easily achieve that speed, as can some runners and cyclists.(98,99)

The MPI system might be distracting to drivers. Results of the MPI system performance 

experiment indicated the MPI system illuminating an area of road with no pedestrian interfered 

with drivers detecting pedestrians on the opposite side of the road. This research has shown that 

when overhead lighting is in place, the MPI system does not significantly alter detection 

distances, but it did increase the probability of detection, especially at high eccentricity angles.

Further research should explore these limits. In addition, driver behavior associated with false 

positives and other potential errors the system should be further investigated.

Age

Age did not significantly affect detection or color-recognition distances for this group of 

participants, who were all experienced drivers and comfortable with nighttime driving. The 

difference in detection distances between the younger and older groups, however, was effectively 

different at 22 m (72 ft), the difference in safe stopping distances for vehicles traveling 50 and 

60 km/h (31 and 37 mi/h). Other studies reported in chapter 2 found diminishing visual acuity 

with age, but peripheral contrast sensitivity diminishes with age more slowly than foveal contrast 

sensitivity.(95) If participants initially detected the pedestrians using peripheral vision, that could 

explain the lack of age effects, similar to the lack of age effects in the mesopic modeling 

experiment, which ensured peripheral detection. 
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Color

Gray-clad pedestrians were often detected from farther away than red- or blue-clad pedestrians. 

Color-recognition distances for the red- and gray-clad pedestrians were longer than for the blueclad pedestrians. The experimental design, and the fact that color contrast was not measured, 

meant that it was difficult to determine why those results occurred. 

Adaptation Luminance

Although this experiment did not specifically evaluate mesopic models in a driving 

environment—a project objective—it did address visual performance in the mesopic range. The 

detection differences among the adaptation luminance levels were in line with Adrian’s 

model.(54) As adaptation luminance changed, so did contrast threshold. Comparing VL to Weber 

contrast (figure 143) showed that as adaptation luminance increased, contrast decreased and VL 

increased. This is because higher adaptation luminances require higher threshold contrasts for 

object detection.

1 cd/m2 = 0.3 fL

Figure 143. Graph. Final performance experiment—VL and Weber contrast by adaptation 

luminance. 

At the lowest adaptation luminance level, 0.07 cd/m2 (0.020 fL) and at 14 degrees

(corresponding to a 21.0 m (69 ft) offset at 83 m (277 ft) distant), light levels approached the 

lower end of mesopic vision. The eye is more adapted to darkness and more contrast-sensitive in 

those conditions, possibly causing the longer detection distances than those at 0.1 cd/m2

(0.03 fL), where vision is more mesopic. 

In general, visual performance depended more on adaptation luminance than on overheadlighting type. At higher adaptation luminances and closer to the photopic region, overhead-
202

lighting type had even less effect on visual performance. At the lowest adaptation luminance, 

detection was only about 10 m (30.5 ft) shorter than at higher adaptation levels, likely because a 

dark-adapted eye is more contrast sensitive. The fact that detection distances were longer at the 

larger visual angles, where both pedestrians and background were darker, supports that 

conclusion. The 0.1 cd/m2 (0.03 fL) adaptation luminance showed the shortest detection 

distances and was the only adaptation luminance level where the target had positive contrast. The 

contrast of that adaptation level probably passed through zero as the vehicle approached the 

pedestrian, reducing the pedestrian’s visibility. 

Offset

There was no difference in detection or color-recognition distances among the different lighting 

types for the pedestrians stationed closest to the roadway. For pedestrians at 21.0 m (69 ft) offset, 

the two LED lighting types had longer detection distances than HPS lighting, because the LED 

lighting had a greater S/P ratio and better SPD in the mesopic range. That result might have 

occurred only at 21.0 m (69 ft) offset (corresponding to 14-degree eccentricity at 83 m (277 ft) 

away) because the maximum rod density is at 15 degrees and is where a mesopic effect would 

most likely occur.(100) However, without restricting the participants’ eye-glance behavior, it is 

difficult to determine whether detection occurred in the fovea or periphery. 

Speed

Speed interacted with visual angle to significantly affect detection distance, with detections 

longer for 3.0, 8.9, and 21.0 m (9.8, 29, and 69 ft) offsets but not the 7.7 m (25 ft) offset, when 

the participant was driving faster. This could be because driver scanning behavior is broader 

when driving slower and more vigilantly and narrower when focused down the road and driving 

faster. At faster speeds, drivers might also focus more on the roadway and rely more on 

peripheral cues for object detection on the shoulder. 

CONCLUSIONS

The four objectives of this project—and of the final performance experiment—were to evaluate 

the following: (1) impact of the spectra of overhead lighting systems on driver visual

performance; (2) interaction of vehicle headlamps and overhead lighting in terms of object 

visibility; (3) applicability of mesopic models and scaling factors in a roadway lighting design; 

and (4) impact of a peripheral illumination system on driver visual performance. 

First, the impact of spectra of overhead-lighting systems on off-axis visibility was minimal

because changes in lighting type did not affect visibility or color recognition significantly. The 

results of the mesopic modeling experiment indicated significant differences in the overheadlighting systems were found in the static but not in the dynamic portions of the experiment. The 

results of this experiment indicate the same is true for pedestrian visibility as it was for targets.

Second, peripheral models were found to be not effective in predicting visual behavior in a 

driving environment. The inability to control where drivers are looking and what they are 

attending to at any given moment during a driving task, makes predicting visibility of any 

roadway object difficult, especially those in low contrast.
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Third, this experiment found that the MPI system provides a benefit for off-axis visibility in 

terms of whether pedestrians are detected or not detected, but the system does not provide a clear 

benefit of detection distance and may be considered a distraction from the forward roadway.

Other findings were that object size, ambient lighting level, and contrast affected the visibility of 

gray-clad pedestrians and gray targets in this experiment. The larger an object, the more visible it 

is, as shown by the VL calculations; pedestrians had higher VLs than targets. Also, the ambient 

lighting level affects the eye’s adaptation, which in turn affects contrast sensitivity. Findings 

related to contrast polarity and ambient luminance were that high ambient luminance can make a 

positively contrasting object difficult to see, and low ambient luminance can make a negatively

contrasting object difficult to see. An object with very low contrast will be difficult to see 

regardless of object size and adaptation level; however, that poor visibility can be mitigated by 

adjusting object size and adaptation luminance. In addition, a target in negative contrast becomes 

briefly invisible as it transitions to positive contrast when it starts to be illuminated by 

headlamps. The effects of higher adaptation levels to brighter environments are particularly 

noticeable at night with low-contrast objects. Findings related to detail recognition were that 

orientation recognition typically occurred only within 30 m (100 ft) of the object, requiring the 

driver to drive slower than 24 km/h (15 mi/h) to be able to stop in time to avoid colliding with 

the object. Distant objects were more visible without headlamps than with headlamps, likely 

because the headlamp light caused the eye to adapt to the brighter environment. 

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CHAPTER 9. SUMMARY

This project is a comprehensive review of the applicability of mesopic functions to roadway 

applications. While the model to determine the impact of mesopic adaptation to visual 

performance has been well-established and verified, both in the laboratory and in some very 

carefully prescribed experiments, the real-world applicability of the model has remained in 

question. Determining the impact of mesopic lighting on high-speed roadways was the focus of 

this effort. In addition, an MPI system for highlighting pedestrians was developed and tested. 

The MPI system’s effect on visibility may have also been affected by the overhead-lighting 

source’s SPD and level; pedestrians on the roadside might be detected in the periphery, where 

mesopic effects occur. 

This project was developed as a stepwise approach to these problems. The first two steps were to 

develop a scoping experiment that defined the nature of the effect of the spectral distribution of 

overhead lighting on visibility and to provide guidance for the development of the subsequent 

experiments. The primary outcomes from this scoping experiment were that both the type and 

level of overhead lighting significantly affected the detection and recognition of objects on the 

roadway. This was also evident for objects that were off of the roadway. One of the primary 

determinants for detection was the color of pedestrian clothing and targets in the roadway, thus 

indicating that color contrast is a significant component of object detection. The results also 

indicated that roadway lighting uniformity has an important role in object detection. The final 

aspect was that of headlamp color and intensity. In scenarios when overhead lighting was used, 

headlamp configuration did not affect visibility. 

These results drove the direction of the next two experiments. The first was an investigation of 

conditions when headlamps have an impact on object detection and when they do not have an 

impact. The second was an investigation of the applicability of the mesopic model to roadway 

lighting.

Before these experiments were performed, however, an investigation of the applicability of an 

MPI system was conducted. Although the scoping experiment showed a minimal spectral effect 

of the headlamp color on detection distance, two headlamp colors were used to further explore 

this relationship. A mock-up MPI system was created with servo-activated headlamps that either 

tracked the pedestrians as the vehicle approached or highlighted them for a short time as the 

vehicle approached. The results of this experiment were that use of the MPI system resulted in 

both shorter detection distances and an increase in detection rate. Headlamp color did not seem 

to have a significant impact on detection. When the MPI system highlighted an area across from 

a pedestrian, participants’ detection rates and distances for that pedestrian were lower. This 

highlights the importance of careful design in a full-featured MPI system; participants’ behavior 

indicated that they expected it to work properly, so it must not produce false positives, which 

could distract drivers from actual roadside hazards. 

The next experiment was an investigation of the interaction of vehicle headlamps and overhead 

lighting on roadway-object detection. Small targets and a pedestrian were located in specific 

locations along the roadway that created high- and low-visibility conditions. The overhead 

lighting was then dimmed and headlamps turned off and on while participant drivers tried to 

detect the objects. Results indicated that the impact of the headlamps varied by object size. For 
206

most lighting levels, the overhead lighting was the dominant force driving object detection, but 

that was not the case when the overhead lighting was at the lowest levels. The results of the 

experiment show that when a vehicle approaches a negatively contrasted target and the vehicle’s 

headlamps begin to illuminate that target, the target contrast passes from negative to positive 

contrast and, importantly, through a point of invisibility—zero contrast—during that transition. 

Therefore, it is desirable for roadside objects to be positively contrasted at all times. The results 

also show that headlamps affect pedestrian luminance up to 91.4 m (300 ft) away. Headlamps 

were the driving factor for orientation-recognition distances, the direction the object was facing. 

The applicability of these results are critical for roadway lighting design. Headlamps dominate 

object recognition and also drive adaptation luminance. Therefore, the effect of the SPD of the 

roadway lighting may be overridden by the headlamps’ effect on adaptation level and the 

contribution of headlamp illumination to object luminance.

The other experiment resulting from the scoping experiment considered the mesopic model. Here 

both static and dynamic target detection experiments allowed the research team to evaluate the 

mesopic model in the field. The static portion of the experiment was performed by determining 

the threshold contrast for small targets, and the dynamic portion examined target detection from 

a moving vehicle. In both cases the drivers were instructed to fix their gaze on the roadway, with 

the targets located at a number of peripheral angles. Gray targets were used to minimize the 

contribution of color contrast on object detection. The results indicate that overhead-lighting 

level significantly affected object detection; higher adaptation levels resulted in a lower threshold 

contrast. The results also showed that in the dynamic experiment, higher speeds typically 

resulted in longer detection distances. In terms of the mesopic model, for white overheadlighting sources, the experimental results corresponded well to the model; however, for HPS

lighting, they did not. An issue with the mesopic model could be that it does not include a term 

for eccentricity that accounts for different retinal sensitivities at different angles. The main 

conclusion was that although the mesopic model predicted some of the results at lower lighting 

levels, it also has limitations. 

The final experiment performed did not attempt to limit driver eye glances or fix eccentricities at 

detection. This experiment included an MPI system, overhead lighting, two speeds, and 

pedestrian detection at different offsets from the roadway. The results indicate that for 

pedestrians close to the roadway, there was no impact of overhead lighting’s spectral distribution 

on detection distance. For those pedestrians, adaptation luminance was the most influential factor 

on visibility. For pedestrians farther from the roadway, spectral effects were more significant, but 

those results might not be applicable to roadway lighting design, because objects that far away 

would have to be moving fairly quickly and on a collision path with the vehicle to become a 

hazard. The MPI performance results were similar to those of the initial MPI experiment. The 

MPI reduced detection distances and increased detection rates for objects in the periphery. The 

results of this experiment show that in a natural driving environment at the speeds tested, there is 

limited applicability of the mesopic model to lighting design. It is likely that drivers scan the 

roadway and detect objects in the fovea, where mesopic effects are not seen. Headlamps might 

also cause a high adaptation luminance, further limiting the applicability of the mesopic model to 

lighting design for nighttime driving.
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CHAPTER 10. CONCLUSIONS AND FUTURE CONSIDERATIONS

CONCLUSIONS

Effect of Overhead-Lighting SPD on Driver Visual Performance

The primary result of this project shows that the SPD of overhead lighting affects object 

visibility but only in selected conditions. Adaptation luminance affected detection distance more 

so than light source type and correlated color temperature. Eccentricity affected the extent to 

which overhead-lighting spectrum affected detection distance; objects closer to the line of sight 

of the driver were less affected by source spectrum than objects farther from the line of sight. 

This may be a result of driver scanning behavior. For off-axis pedestrians, broad-spectrum 

overhead lighting had greater, albeit not statistically significant, color-recognition distances than 

HPS lighting. It is noteworthy that this result is related to the light sources used for the 

experiment, and this result may vary based on a different light source.

The contrast of the overhead lighting system appeared to affect pedestrian visibility. At the time 

of detection, pedestrians contrasted more against the background under HPS lighting than they 

did under the other light sources, likely because HPS lighting is less uniform and created bright 

lines across the roadway. This result may be related to the light source intensity distribution and 

the inefficiencies in lighting design with the HPS light source. Future experiments should 

investigate the effect of overhead lighting uniformity on object detection.

Combined Effect of Overhead Lighting and Headlamps on Driver Visual Performance

When only overhead lighting was used and headlamps were off, objects could be detected from 

farther away than with overhead lighting and headlamps on because adding vehicle headlamps 

increased the ambient luminance of the forward roadway, increasing adaptation luminance and 

decreasing contrast sensitivity. The result was that with headlamps, distant objects were more 

difficult to detect under the same overhead lighting levels. When only headlamps were used and 

overhead lighting was off, visibility was confined to the limits of the vehicle headlamps. Adding 

overhead lighting increased the probability of detecting objects off-axis or from distances beyond 

the reach of the vehicle’s headlamps.

The combination of headlamps and overhead lighting appears to have its greatest impact when 

the two sources contribute nearly equal amounts of lighting. This requires the object to be within 

the area illuminated by the vehicle’s headlamps, approximately 91 m (300 ft) for the vehicles 

used in this study. When headlamps and overhead lighting were combined, detail recognition 

increased because multiple sources of light illuminated the object from different angles.

The effects of visibility caused by the combination of headlamps and overhead lighting depend 

on ambient luminance and contrast. High ambient luminance causes objects in positive contrast 

to be more difficult to detect, and low ambient luminance causes negatively contrasted objects to 

be more difficult to detect. Also, the extent of ambient luminance’s impact on visibility depends 

on the amount of luminance contrast between object and background; for example, concrete 

surfaces reflect more light than asphalt and result in a higher ambient luminance. These results 

highlighted the complex relationship between ambient luminance, contrast, and visibility. Future 

research should consider additional factors, such as the impact of headlamps and overhead 
208

lighting on object visibility from different vehicle types, with and without oncoming vehicles, in 

different road profiles, and in twilight conditions. 

Applicability of Mesopic Models to the Driving Environment

The CIE Recommended System for Mesopic Photometry accurately predicted the mesopic 

luminances and contrasts for both the LED light sources but not for HPS lighting. The change in 

the calculated difference to the predicted difference between the mesopic and the photopic 

luminances of HPS lighting and both LED overhead-lighting sources indicated that the 

calculated differences closely followed the predicted differences from 0.07 to 0.2 cd/m2 (0.020 to 

0.06 fL). Above that luminance value, the calculated differences leveled off while the predicted 

values continued to decrease. An ideal mesopic model should also have the eccentricity as an 

input to accurately calculate mesopic luminances. 

Impact of MPI System on Driver Visual Performance

The MPI system did not improve the detection distance of off-axis pedestrians but did improve 

the rate at which they were detected. Also, participants detected pedestrians from the greatest 

distances when the MPI system was off. This was the case for sections of the road with and 

without overhead lighting. This could be because the MPI system’s moving beam distracted the 

participants and prevented them from scanning normally. Lastly, participants seemed to assume 

the MPI system would accurately illuminate pedestrians. They detected the pedestrian about 

70 to 80 percent of the time with the MPI system illuminating the pedestrian, compared with 

about 50 to 60 percent of the time with the MPI system illuminating the roadway opposite the 

pedestrian position rather than illuminating the pedestrian.

Additional Findings

Pedestrian clothing color and target color both significantly affected detection and colorrecognition distances, whether or not overhead lighting was used. 

Object size, ambient lighting level, and contrast affected the visibility of pedestrians and targets 

in this experiment. The larger an object, the more visible it was, as shown by the VL 

calculations; pedestrians had higher VLs than targets. 

Speed appeared to affect driver attention and gaze patterns, and detection distances increased 

with speed. When the vehicle was traveling faster, it appeared that the driver focused more on 

the roadway ahead and glanced less to the side of the roadway. 

Distant objects were more visible without headlamps than with headlamps, likely because the 

headlamp light caused the eye to adapt to the brighter environment. The pool of light in front of 

the driver made by the headlamps has an impact on the adaptation luminance of the driver. The

other results of the experiment show that a higher adaptation luminance required a higher 

threshold contrast. Distant objects that typically have low contrast would be harder to detect with 

higher adaptation luminance from the headlamps.
209

CONSIDERATIONS

With overhead lighting systems and standard headlamps, the design of higher speed roads need 

not consider spectral effects because the gaze behavior of a driver cannot be controlled in a realworld driving situation.

Mesopic factors should be applied for low-speed roadways where lighting is designed to provide 

more benefit to pedestrians than drivers. 

Significant effort in the detection and warning methods for the MPI system require significant 

effort and regulation. While the system shows promise, care must be taken to ensure that 

negative outcomes are not encountered.

FUTURE RESEARCH

The results of this project are limited by the conditions tested. These tests were for drivers in 

vehicles only. Additional testing to study the application of mesopic factors should be 

investigated for pedestrians and slower-moving roadway users. 

The other aspect noted in these results is the applicability of the mesopic model in a variety of 

conditions with objects at various eccentricities to the roadway. The relationship of these results 

to the mesopic model shows that an eccentricity factor may apply to the model and should be 

investigated further.

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CHAPTER 11. APPLICATION TO PRACTICE

The interpretation of these results is an important aspect of this project. As mentioned, the results 

of the experiments show that the impact of overhead lighting spectrum on driver visual 

performance is limited to specific situations. It is important to note that, in many situations, the 

broad-spectrum light source did not improve driver visual performance over the narrowspectrum light source, but neither did it worsen driver visual performance. Other studies have 

shown benefits of the use of broad-spectrum light sources beyond providing better visual 

performance. In user preference studies, broad-spectrum light sources were preferred for their 

user comfort and acceptance.(23,101) Other research has shown that broad-spectrum sources 

provide for better object contrast, thus increasing the detection of objects along the roadside. 

These results indicate that broad-spectrum lighting is a valid choice in general and likely a 

desirable choice for roadway lighting. 

LED lighting is the optimal broad-spectrum source for roadway lighting. The benefits of LED 

lighting, in addition to those of other broad-spectrum sources, are significant. Typically LED 

installations use half the energy of that used by traditional HPS roadway lighting. Similarly, LED 

lighting can be controlled and dimmed in adaptive-lighting designs that actively correlate

lighting levels to environmental demands, saving even more energy. These are compelling 

reasons to use LED lighting, which does not reduce driver visual performance. Other factors 

such as circadian impacts, sky glow, and glare should be considered but were outside of the 

scope of this project.

The final aspect to consider regarding broad-spectrum lighting is the application of mesopic 

scaling factors. A significant finding of this research is the non-applicability of these scaling 

factors, based on the spectral component of the light source, to roadway lighting design criteria. 

It is important, however, to highlight that the results of this project are consistent with the 

Illuminating Engineering Society (IES) RP-8-14 Recommended Practice.(5) The IES position is 

that lighting levels can be scaled based on the light source spectrum if the lighting design is for a 

roadway with a posted speed limit of less than 40 km/h (25 mi/h). Scaling can be performed on 

roads in this category because the lighting system is primarily for the benefit of pedestrians, and 

vehicle headlamps provide adequate lighting for drivers. This project shows that there are no 

spectral effects of roadway lighting on driver visual performance. However, a pedestrian has a 

wider field of view, and there may be spectral effects of lighting on pedestrian visual 

performance. 

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