Measuring Light and Geometry Data of Roadway Environments with a Camera

doi:10.4236/jtts.2014.41005

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Journal of Transportation Technologies, 2014, 4, 44-62

Published Online January 2014 (http://www.scirp.org/journal/jtts)

http://dx.doi.org/10.4236/jtts.2014.41005

OPEN ACCESS JTTs

Measur in g Light an d Ge o me t ry D a ta of Ro adw a y

Enviro nments with a Camera

Hongyi Cai*, Linjie Li

Department of Civil, Environmental & Architectural Engineering, The University of Kansas, Lawrence, USA

Email: *hyc ai@ku.edu

Received September 23, 2013; revised October 27; 2013; accepted November 21, 2013

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In accor-

ABSTRACT

Evaluation of the conspicuity of roadway environments for their environmental impact on driving performance

is vital for roadway safety. Existing meters and tools for roadway measurements cannot record light and geome-

try data simultaneously in a high resolution. This study introduced a new method that adopted recently devel-

oped high dynamic range (HDR) photogrammetry to measure the luminance and XYZ coordinates of millions of

points across a road scene with the same device—a camera, and a MatLab c od e for d ata tre atm e n t and vi s u aliz ation.

To validate this method, the roadway environments of a straight and flat section of Jayhawk Boulevard (482.8 m

long) at Lawrence, KS and a roundabout (1 5.3 m in diameter) at its end were measured under c lear and cloudy sky

in the daytime and at nighttime with dry and wet pavements. Eight HDR images of the roadway environments under

different viewing cond itions were g enerate d using the H DR photo grammetric t echniqu es and cali brated. Fro m each

HDR image, synchronous light and geometry data were extracted in Radiance and further analyzed to identify po-

tential roadway environmental hazards using the MatLab code (http://people.ku.edu/~h717c996/research .htm l ). The

HDR photogrammetric measurement with current equipment had a margin of errors for geometry measurement

that varied with the measuring distance, averagely 23.1% - 27.5% for the Jayhawk Boulevard and 9.3% - 16.2%

for the roundabout. The accuracy of luminance measurement was proven in the literature as averagely 1.5% -

10.1%. The camera-aided measurement is fast, non-contact, n on-destructive, and off the road, thus, it is deemed

more efficient and safer than conventional ways using meters and tools. The HDR photogrammetric techniques

with current equipment still need improvements on accuracy and speed of the data treatment.

KEYWORDS

Measurement; Geometry; Light; Roadway Environment; High Dynamic Range Photogrammetry

1. Introduction

The conspicuity of roadway environments is affected by

many factors, including the transitory lighting and

weather conditions, the geometry and layout of roadway

elements, the legibility of roadway signs and plaques,

and the visibility of traffic control signals, pavement

markings and markers, object markers, delineators,

channelizing devices, and pedestrian hybrid beacons, etc.

As a result, the conspicuity of roadway environments

varies over time under different viewing conditions, e.g.,

in the daytime or at nighttime; in sunny, rainy, foggy, or

snowy weathers; wi t h dr y or wet pavements, etc.

When the conspicuity of essential roadway environ-

ments (e.g., intersections) is harmed at nighttime or even

in the daytime under less-than -optimal viewing condi-

tions, roadway lighting is often needed to increase the

visibility of the pavement, traffic control devices, and

on-road/off-road hazards to facilitate drivers conducting

driving tasks safely. However, roadway lighting with low

quality may provide insufficient light, cast shadows on

the road pavement, or bring discomfort glare to motorists

and pedestrians, which all reduce the visibility of the

roadway environments.

Therefore, evaluation of the roadway environments is

vital for their environmental impact on driving perfor-

mance and roadway safety. Routine measurements of the

*Corresponding a uthor.

H. Y. CAI, L. J. LI

OPEN ACCESS JTTs

roadway environments could help to maintain a good

conspicuity of essential roadway elements, and also id en-

tify potential roadway hazards (e.g., illegible roadway

signs, insufficient light on pavement, glaring roadway

lighting, etc.) that may jeopardize the roadway’s visibili-

ty and the drivers’ visual comfort. Both ligh t and geome-

try data of the roadway environments need to be meas-

ured simultaneously.

What light and geometry data need to be measured in

the roadway environments? Lighting metrics measured

on the road often include surface illuminance and lu-

minance, their spatial variations, luminance contrast be-

tween a target and its background, etc. The commonly

measured geometries of the roadway environments in-

clude size and shape of the pavement, sidewalks, road-

way markings and markers, and surrounding objects and

their distances, e tc . In addition, the acquired light and

geometry data, if concurrent, are used for derivation of

some advanced metrics for timely roadway environment

assessment. Such metrics with which the light and geo-

metry data are jointly involved include visibility of ob-

jects on the road, legibility of roadway signs, discomfort

glare of roadway lights, roadway surface reflectance,

uniformity of pavement light distribution, and luminance

gradient (the frequency of the spatial lu minance variation)

[1] across the roadway environment, etc.

How to measure light and geometry data in the road-

way environments? Conventionally, meters and tools

have been used. Minolta LS-100 luminance meter, Mi-

nolta T-10 lux meter, and Minolta CL-200A chroma me-

ter are popular handheld light meters. Handheld retroref-

lectometers, such as Delta LTL-X, Mirolux Ultra 30 , can

provide traceable, accurate, repeatable and reproducible

retroreflectivity measurements. For measuring distances

and angles, rulers, tapes, wheel rolls, laser distance me-

ters, laser rangefinders, protractors, and magnetic angle

locators have been used. The measurement using meters

and tools is a reliable but tedious point-by-point process

with very low measurement resolution (number of meas-

ured points per surface area). The dynamic daylight,

transient weather, moving traffic, and other changing

viewing conditions on road all affect the accuracy of

overall roadway measurement using meters and tools that

take hours or even longer [2,3]. In addition, it is danger-

ous for workers to conduct the measurement on road with

active traffic flow.

To solve these problems, remote sensing technologies

are preferred for fast, non-contact, non-destructive and

off-road measurement. This paper introduced a new

camera-aided method for measuring millions of syn-

chronous light and geometry data of the roadway envi-

ronments. To validate this method, the roadway envi-

ronments on Jayhawk Boulevard in Lawrence, KS and a

roundabout at its end were measured under clear and

cloudy sky in the daytime and also at nighttime with dry

and wet pavements.

2. Literature Review

In the past 20 years, several non-contact measurement

techniques were developed for measuring the roadway

geometry or light. They are reviewed below.

2.1. Roadway Geometry Measurement

Drakopoulos and Ornek developed a non-contact method

for measuring roadway geometry [4]. A vehicle was used

to carry a distance-measuring device, a vertical gyros-

cope, and a gyrocompass. Data collected include odome-

ter reading, gradient, transverse slope, and compass

reading. From these data, an algorithm was developed for

calculating the roadway geometry, including road length,

deflection angle, and degree of curves. The conventional

inertial data logging and advanced GPS (global position-

ing system) data logging were used in their study, both

had low accuracy. Inertial hardware has acceleration ef-

fect on readings, which needs frequent calibration. Al-

though the GPS technology has no acceleration effect,

atmospheric disturbances, receiver errors and other fac-

tors reduce the accuracy of GPS devices. Due to these

problems, the measurement errors of this algorithm were

relatively large, up to 63.8%.

Tsai et al. developed another algorithm for roadway

geometry computation based on video logs [5-8]. Pave-

ment images were collected using a camera fixed on a

moving vehicle. This algorithm used vanishing points

and vanishing lines to calculate the projection metric

from the 3D world coordinate system (WCS) to the 2D

camera coordinate system. Vanishing points and lines

were calculated using orthogonal edges of rectangular

pavement markings in the images. Next, roadway geo-

metries such as line width, roadway length, lane width

and marking size were extracted. The measurement error

was within 0.01 - 0.3 m, depending on the measuring

distance. An advantage of using vanishing points and

vanishing lines is that this algorithm does not need any

intrinsic parameters of the camera, such as the focal

length and the focal point [9-11]. On the other hand, this

method has a limitation that the camera must capture

rectangular pavement markings or other objects with

orthogona l edges in th e picture f or 2D/3D recons truc tion.

If a road section is not linear, e.g., a horizontal curve or a

roundabout, and doesn’t contain any rectangular pave-

ment markings, this algorithm is not applicable. Fur-

thermore, using this method, the pavement markings

were segmented based on colors and length [7]. As a

result, only pavement markings with specific colors and

certain lengths were extracted. Other objects on the road

or on roadside were left untreated.

H. Y. CAI, L. J. LI

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GPS was also used for measuring roadway geometry.

Nehate and Rys used GPS data to calculate stopping-

sight dis t a nc e of hi ghways [12]. A profili n g va n c ol lected

GPS raw data such as latitude, longitude, and altitude of

road surface and sight obstructions, which were trans-

formed into B-spine curves. Drivers’ sight line inter-

sected with these two curves. By examining the intersec-

tions, the sight distance was derived. The measurement

error was ±0.2 m. This algorithm was not capable of

showing the actual image of sight obstructions and de-

tailed information of the road surface. Castro et al. used a

Leica System 500 GPS to track the geometry of highway

system [13]. The GPS were mounted on a vehicle to

measure the coordinates when the vehicle was moving

along the highway, with maximum err or of 1 m. Koc also

used GPS for rail-track surveying [14]. He used a trailer

equipped with GPS antennas. The GPS measured the

coordinates of the trailer at a frequency of 20 Hz with

error of 0.01 - 0.03 m. It was assumed that the rail-track

was on a horizontal plane, thus, lacked information for

3D modeling. This method was also not capable of

showing detailed visual information of the scene.

Tsai and Li used a laser system to detect the cracks on

the pavement [15]. Two laser units were mounted on a

vehicle to cover a full lane width. Laser beams were pro-

jected on the pavement. CMOS cameras were used to

detect the reflected light of the laser. Likewise, Amarasiri

et al. used a CCD camera for measuring pavement ma-

cro-texture and wear of pavement [16,17]. The CCD

camera sensed the optical reflection properties of the

pavement and recorded them on the image.

2.2. Roadway Light Measurement

Arditi et al. developed a system called LUMINA [18].

This system deployed an analog video camera and a

photometer to record the image and luminance value of

the safety vests synchronously. A device called AI-

GOTCHA was used to convert the frames of the analog

videotape to BMP format. The luminance values of the

vests were calculated using RGB values extracted from

the image. The luminance values were also measured

with a photometer for physical calibration of the calcu-

lated luminance. This study had two limitati ons. First, the

analog/digital conv ersion introduced new errors. Second,

the camera had a very low resolution of only 400 lines,

which was inadequate and outdated compared to digital

cameras.

To improve, other researchers used digital cameras for

recording the roadway luminance. Zatari et al. used two

CCD cameras mounted on top of a vehicle for measure-

ment of glare, luminance and illuminance of road light-

ing [19]. Due to the limited field of view of the cameras,

one camera was aiming straight forward to cover the road,

the other was aiming upward to photograph the lumi-

naires on poles. The measurement error of absolute illu-

minance was within 15 lx, while the average error of

luminance measurement was 0.3185 cd/m2. Likewise,

Aktan et al. used a CCD photometer that was mounted

outside a vehicle and automatically took photos of the

road scene while the vehicle was moving [20]. The reso-

lution of the CCD photometer was 1 megapixel and the

measurement error of road luminance was within 10%.

Ekrias et al. developed a technique to measure the lu-

minance on road with a ProMetric 1400 photometer [2,3].

The CCD camera of the photometer captured the scene

and luminance values a across the whole scene in a few

seconds. Due to the reduced measurement time, the error

of this CCD photometer system was reduced to ±3%.

However, the pixel resolution of this CCD ProMetric

1400 photometer is only 500 × 500, which was not

enough to measure large scenes. Armas and Laugis used

an LMK camera developed by the TechnoTeam Compa-

ny for measuring roadway luminance [21,22]. The LMK

camera could record both the image and luminance val-

ues of the scene with a pixel resolution of 5184 × 3456

for image acquisition and 2592 × 1728 for luminance

mapping [23]. Ylinen et al. also used the LMK Mobile

Advanced luminance measuring camera for evaluation of

LED street lighting [24]. In spite of their potentials for

wide uses in roadway environments, all these measure-

ment techniques proposed by Ekrias et al. [2 ,3], Aktan et

al. [20], Armas and Laugis [21,22], and Ylinen et al. [24]

require d hi gh-cost equipment.

On the other hand, high dynamic range (HDR) photo-

graphy, as one of the seven types of known HDR imag-

ing techniques [25], could be used for luminance map-

ping roadway environments with lower entry barriers.

The HDR photography uses affordable consumer grade

digital cameras fitted with wide-angle or standard lenses

to acquire luminance data across a static scene within 1 -

2 minutes [26]. The scene is photographed via sequential

exposures to cover a high dynamic range of light, given

limited recording capability of current image sensor

technology. The low dynamic range (LDR) photographs

are then picked, those fallen in an appropriate range of

RGB values are fused into a raw HDR image, which is

calibrated for luminance mapping the scene at pixel level

[26]. Free data-fusion software programs include Pho-

tosphere, hdrgen, Radiance and Photolux. The HDR

photography has proven average errors of 1.5% - 10.1%

for luminance mapping gray, black, color, and light-

emitting surfaces [26,27]. Due to time lapse between

exposures, the HDR photography is ideal for static

scenes under constant light. This limitation w ill n o longer

exist with the emergence of superb image sensors

(>1,000,000:1) that will capture a high dynamic scene

with only a single shot [2 5].

H. Y. CAI, L. J. LI

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2.3. Roadway Geometry and Light Measurement

Other researchers developed techniques to measure both

geometry and light of the roadway environments. Zhou et

al. mounted a light meter and a longitudinal distance

measurement instrument on a vehicle [28]. A computer

was used to control these devices for measuring the loca-

tion and illuminance of the road elements while the ve-

hicle was moving. This technique did not use the same

device to measure the concurrent light and geometry data.

As a result, the light and geometry data obtained sepa-

rately were not aligned at the same measurement points.

It also had low measurement resolution, a problem affi-

liated with the conventional meters.

Gutierrez et al. developed a camera-aided method to

measure the luminance of a traffic sign lighted by the

vehicle headlights solely from the driver’s view, as well

as its distance [29]. A van was equipped with two mo-

nochrome CCD cameras mounted close to the driver, and

LEDs in the front to simulate the headlight. When the

van was moving, a computer was used to switch the

LEDs on and off. One camera took an image with the

LEDs turned on, later the other camera took an image

with the LEDs turned off. From the two consecutive im-

ages, luminance values of the traffic sign were extracted.

The difference between the extracted luminance values

of the two images was assumed the luminance of the

traffic sign lighted by the LEDs only. This technique

could exclude the influence of ambient light from road

lamps and the headlamps of other vehicles. The distance

from the van to the sign was calculated from the norma-

lized size of the sign and the traveled distance of the van

over the time delay when the two consecutive images

were photographed. Nonetheless, Gutierrez et al.’s tech-

nique had two insufficiencies. First, their technique did

not capture a high dynamic range of light in the roadway

environment, since both cameras, which lacked a superb

image se nsor, did not ph ot ograph the sc ene via sequential

exposures. Second, an accurate measurement of lumin-

ance is dependent on the measurement direction. The

luminance differential extracted from the two consecu-

tively photographed images was not truly the sign lu-

minance when lighted only by the LEDs, because the

viewing direction from the cameras to the sign was

changed over the time delay.

3. Research Gap and Problems

The non-contact measurement techniques developed by

those researchers aforementioned are of great improve-

ment upon meters and tools in light of the measurement

speed, resolution, overall accuracy under varying road-

way conditions (due to the shortened measurement time),

and the capability of measuring large non-uniform sur-

faces. However, none of those techniques is able to

measure geometry and light level of the entire roadway

environment simultaneously with the same device. The

separated measurements increase the field measurement

labor, slow the data collection, and cause difficulties in

follow-up data alignment. Capturing synchronous light

and geometry data across a roadway environment with

the same device can speed up the measurement in that

there is no need to measure twice, and no pixels on the

photographic images are wasted in data treatment later

(e.g., in the study by Wu and Tsai [7]. The concurrent

light and geometry data could serve more needs than

separately measured data for roadway environment as-

sessment, e.g., visibility of objects on the road, legibility

of roadway signs, glare, uniformity, roadway surface

reflectance, and luminance gradient, etc.

On the other hand, on-road measurement, using either

handheld meters and tools or non-contact measurement

equipment mounted on vehicles running on road, is

harmful for roadway safety and overall efficiency. Dur-

ing the measurement, the workers and instrumented ve-

hicles constantly on the road could hinder the roadway

traffic, and increase the risk of rear-end collisions. Off-

road non-contract measurement does not interrupt road-

way traffic, thus, is safer. The HDR photography could

be used for luminance mapping an entire roadway envi-

ronment off the road by approximating the sightline and

field of view of a typical observer. It has the attraction

due to its low entry barrier, extraordinarily high resolu-

tion, rapid measurement speed, and acceptable accuracy.

Yet the HD R photography does no t record any geometry

of the roadway scene. The geometric features, which are

needed for roadway condition evaluation, have to be

measured separately.

Recently, based on the HDR photography, Cai devel-

oped the HDR photogrammetry for measurement of

synchronous light and geometry data of an entire scene

with the same device (the camera) [30]. When used for

close- and middle-range scenes (i.e., ≤8.14 m), the accu-

racy of the HDR photogrammetry was validated, with

average errors of 1.8% - 6.2% for luminance mapping

and 12.9 - 24.3 mm for measuring geometry. The HDR

photogrammetry has great potentials to solve aforemen-

tioned problems to facilitate the measurement and evalu-

ation of roadway environments. However, the accuracy

of the HDR photogrammetry for measurement of road-

way environments, which are often in far ranges > 8.14

m, has not yet been tested. This study was thus aimed to

validate the feasibility of the HDR photogrammetry for

synchronous roadway light and geometry measurement

under different viewing conditions, and also explore its

potentials for facilitation of expedited roadway environ-

ment evaluation.

H. Y. CAI, L. J. LI

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4. The HDR Photogrammetric M ea su re men t

of Roadway Environments

The HDR photogrammetry was developed for acquisition

of luminance of millions of target points across a scene

and their right-handed Cartesian coordinates XYZ in the

field [30]. It deploys a consumer grade digital camera

fitted with a standard, w id e-angle, or fisheye lens

mounted on a portable photogrammetric measurement

platform. The camera is mounted in the test scene with

yaw angle κ, pitch angle η, and roll angle φ related to the

world coordinates XYZ, following the right hand rule. A

notebook computer controls the camera to take multiple

LDR photographs of the test scene, which are later fused

into an HDR image. The HDR image plane is located on

the image sensor of the camera, with pixel coordinates xz.

Any single target plane, on which the target points are

randomly distribu ted, also has ya w ang le θ, pitch angle τ,

and roll angle ρ in light of the world coordinates XYZ. A

target P (X, Y, Z) and a reference point Pi (Xi, Yi, Zi) are

both located on the target plane. The position (Xi, Yi, Zi)

of the reference point Pi is measured in the field. Mini-

mum three, ideally four, reference points are needed for

identifying a target plane. If the yaw, pitch, and roll an-

gles of the target plane, e.g., a flat road pavement, are

known, then only one reference point is needed. Aided

by the reference point Pi, the location (X, Y, Z) of the

target P in the field can be converted from its pixel loc a-

tion (x, z) on the HDR image, by using some photo-

grammetric transforming equations [30]. Meanwhile, the

luminance value of the target P can be obtained from the

HDR image. Then it is possible to extract the luminance

values and XYZ coordinates of the entire scene from the

HDR image at pixel level.

Below is an introduction to the deployment of the

HDR photogrammetry for roadway measurement, in-

cluding the field measurement procedure and data treat-

ment. Figure 1 is the flow chart to depict the field mea-

surement procedure and follow up data treatment.

4.1. Field Measurement Procedure

Below is a protocol divided into five steps of the HDR

photogrammetric measurement in the field.

Step 1: field preparation. The camera location in the

road scene may be any typical viewpoint of the observer

(e.g., a driver, a pedestrian, etc.). An off-road mounting

location is preferred for the benefit of roadway safety and

traffic flow, if the sightline of the observer could be ap-

proximated off the road. With the right lens, the camera’s

field of view shall cov er all interested target poin ts in the

roadway environments. In addition, four reference points

need to be identified for each target plane (e.g., pavement,

sidewalk, roadway sign, etc.). Common reference points

include visible roadway marks, edges of the curb, or field

Figure 1. The flow chart of the field measurement and fol-

low up data treatment.

objects such as light poles, plants, etc. Some reference

objects, such as wood white boards, could be placed on

interested points for the benefit of meter measurement.

An X-Rite 18% gray checker is also mounted on a tripod

and placed close to the camera shooting line for photo-

metric calibration.

Step 2: platform setup and leveling. The camera is then

mounted on a 3D angular measurement tripod head [30],

which is mounted on a heavy-duty flat head tripod and

leveled. The tripod head has an adjustable base with

three screws for micro-adjustment to ensure the platform

is truly horizontal at its initial position. The tripod head

has three angular dials on the base, the front and the side,

respectively, to record the aiming yaw, pitch and roll

angles of the camera with a precision of 0.1 ˚. A Dell La-

titude notebook computer is used for remote control of

the camera and data recording.

Step 3: determining the XYZ coordinates. In roadway

scenes, the World Coordinate System (WCS) is used,

which has fixed coordinates—X (east), Y (north), Z (up).

The origin point O (0, 0, 0) of the photogrammetric

coordinates is overlapped at the optical center of the

camera. To determine the initial yaw pitch and roll an-

gles (κXYZ , ηXYZ, φXY Z) of the XYZ coordinates, a compass

is put on the platform to adjust the 3D angular measure-

ment tripod head until the camera is aiming north. Yaw

angle κXYZ and pitch angle ηXYZ are recorded from the

angular dials. Roll angle φXYZ is assumed 0˚ for roadway

measurement.

Step 4: HDR photographing. The camera is manually

focused on an aiming point in the field, which is careful-

ly selected to balance the field of view and make sure all

targets across the entire roadway environment are in fo-

cus. A chroma meter Minolta CL-200A is used to meas-

ure the correlated color temperature (CCT) and vertical

illuminance at the camera lens. The CCT is used for

white balancing of photographing. The vertical illumin-

H. Y. CAI, L. J. LI

OPEN ACCESS JTTs

ance at the lens is used to control the light measurement

in the field and as an input of potential glare calculation

(not covered in this study). The Dell notebook computer

is used to remotely control the camera to take a total of

18 LDR photographs via sequential exposures (1/4000 s

to 30 s @ aperture of f/5.0). Some of them fallen in a

good exposure range are fused into a raw HDR image.

During the photographing, the luminance meter Minolta

LS-100 mounted beside the camera is used to measure

the luminance of the X-Rite 18% gray checker for pho-

tometric calibrations of the raw HDR image.

Step 5: field measurements and calibration. From the

three dials of the tripod head, the camera’s initial aiming

direction (κ0, η0, φ0) is recorded. The actual aiming an-

gles (κ, η, φ) are calculated as κ = κ0 − κXYZ, η = η0 −

ηXYZ, φ = φ0 − φXYZ. Next, the camera is dismounted from

the tripod head and replaced with a laser distance meter

Leica DISTO™ D5 for measuring the distance di of the

reference point Pi. If faraway points exceed the mea-

surement range (650 ft) of the laser distance meter, a

Bushnell EliteTM 1600 laser rangefin de r is used to repla c e

the laser distance meter. The ai ming angles of Pi (ϕi,a, αi,a)

are then recorded from the angular dials on the side and

base of the tripod head. Note that due to d ifferent optical

structures, when replacing the camera with the laser dis-

tance meter or the rangefinder, the aiming direction may

change. To calibrate this error, the laser distance meter or

the rangefinder is re-aimed to the same aiming point of

the camera. The new aiming direction (κm0, ηm0, φm0) is

recorded from the three angular dials. The offsets of the

aiming angles are calculated using Equation (1). The

off-axis angles are then calculated using Equation (2).

The four reference points Pi (Xi, Yi, Zi) (i = 1, 2, 3, 4) are

later calculated using Equation (3).

κκ κ

ηη η

∆= −



∆= −



(1)

ii aXYZ

φφ κκ

αα ηη

= −−∆





= −−∆





(2 )

( )

cos sin

cos cos

sin

ii i

i iii

iii

αφ



−





= −





(3)

4.2. Data Treatment

The field-measured data are then treated using personal

computers at six steps below, which are numbered after

the previous five steps.

Step 6: generation of raw HDR images. Raw HDR

images are generated from an appropriate exposure range

of those LDR photographs using Photosphere or Ra-

diance [26]. Before that, if necessary, the LDR photo-

graphs need treatments to filter any unwanted voltage

bias, dark current and conductive noise, and fixed pattern

noise [26,31].

Step 7: calibrations of the raw HDR images. The raw

HDR images are then calibrated in Radiance, including,

in order, 1) vignetting effect correction, which corrects

the light drop-off in the periphery of the field of view,

and 2) photometric calibration, which corrects the lu-

minance differential between the camera-aided mea-

surement and the luminance meter measurement of the

X-Rite 18% gray check er [26].

Step 8: data retrieval from the calibrated HDR images.

Of every single pixel, the luminance and pixel location

Ppix (xpix, zpix) are extracted from the calibrated HDR im-

ages in Radiance, using the command “pvalue” (pvalue

-o -h -b $HDR > L_$HDR.txt). The output text file en-

closes per-pixel luminance values across the scene in a

data format of {xpix, zpix, luminance}. In addition, from

the pixel location, the distorted geometric coordinate Pd

(xd, zd) of the target point P on the image plane xz is then

derived [30].

Step 9: correction of the lens distortion. A wide-angle

lens often has radial distortions that need corrections for

geometry measurement. Of every single pixel, the dis-

torted geometric coordinates Pd (xd, zd) are then corrected

as undistorted Pu (xu, zu). More details of the correction

models are available in the literature [30].

Step 10: photogrammetric transformation & calibra-

tion. The XYZ coordinates of the target point P in the

field are then transformed from its undistorted geometric

coordinates Pu (xu, zu) on the image plane, using some

transformation equations developed by Cai [30]. The

local coordinates X′Y′Z′ of the target point P are first

calculated from Pu (xu, zu). Then the local X′Y′Z′ coordi-

nates are converted back to the WCS coordinates P (X, Y,

Z) with the aid of the field measured XYZ coordinates of

the reference points. The last step is the photogrammetric

calibration using the four reference points. The average

geometric deviation between their measured and the cal-

culated XYZ coordinates is used to furth er calibrate th e P

(X, Y, Z) [30].

Step 11: collection of synchronous data for further

treatment. Of every single pixel, its luminance L and

calibrated 3D geometric coordinates P (Xcali , Ycali, Zcali)

are then collected for further data treatments, e.g., calcu-

lations of visibility of roadway objects, roadway signs

legibility, discomfort glare of roadway lights, roadway

surface reflectance, pavement uniformity. The output text

file that encloses per-pixel luminance values of each ca-

librated HDR image is used to plot in MatLab a 2D lumin-

ance map {xpix, zpix, luminance}, a 2D luminance gradient

magnitude map {xpix, zpix, luminance gradient m agnitude}, a

2D luminance gradient direction map {xpix, zpix, luminance

gradient direction}, and a comb in ed 3D luminance gradient

map {xpix, zpix, luminance gradient magnitude, luminance

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gradient direction} [1]. The underlined coordinate of each

map is in pseudo color. The MatLab code for such data

treatment and ima ge plotting is availa ble online

(http://people.ku.edu/~h717c996/research.html).

5. Validation of the Method in the Field

5.1. Field Measurements

The HDR photogrammetric measurement is fast, non-

contact, non-destructive, and off the road. To validate

this method, the roadway environments of a straight and

flat section of Jayhawk Boulevard (482.8 m long) at

Lawrence, KS and a roundabout (15.3 m in diameter) at

its end were measured. The Jayhawk Boulevard went

through the campus of the University of Kansas. On both

sides of this road, there were buildings, trees, light poles,

traffic signs, statues, newspaper booth, etc. The rounda-

bout had three asymmetrical non-straight approaches.

In this study, the Canon camera EOS rebel T2i with an

18-mega pixel CMOS sensor fitted with a S igma EX DC

HSM 10 mm diagonal fisheye lens was deployed. The

HDR photogrammetric measurement platform is shown

in Figure 2, on which the camera was mounted. The op-

timized lens aperture size of f/5.0 was used based on

previous studies [26,31]. Several pieces of wood white

boards were placed on interested locations serving as the

target and reference points. In both test scenarios, an

off-road measuring location was chosen.

For measuring Jayhawk Boulevard, four HDR images

were generated under clear or cloudy sky in the daytime,

and also at night with dry or wet pavement, as shown in

Figu re 3, which were photographed at the same off-road

location aiming at the same direction.

In addition, a to tal of 24 points on Jayhawk Bo ulevard

were selected for measurement of their XYZ coordinates,

as shown in F igure 4 . Points 1 - 4 were ref erence points.

Others were target po ints. Points 5 - 11 a nd 15 - 20 were

the centers of crosswalk strips. Point 14 was the focusing

target of the camera, which was a blue newspaper booth.

Points 12 and 13 were the bottoms of trees. Points 21 -

23 were the bottoms of light poles. Point 24 was a traffic

booth at the end of the road. Independent of various

viewing conditions, the field measurement of the 24

points was conducted in the daytime under clear sky due

to better legibility of the far targets. White wood boards

were placed at those points to help the laser distance me-

ter and rangefinder acquire the targets.

The roadway environment at the roundabout was also

measured off the road. Figure 5 shows the four HDR

images taken under clear or cloudy sky in the daytime,

and also at night with dry or wet pavement.

A total of 21 points, as shown in Figure 6, were

measured in the field in the daytime under clear sky for

their XYZ coordinates. Points 1 - 6 were the top left cor-

ners of the crosswalk strips. Points 7 - 13 were white

(a) (b)

Figure 2. The HDR photogrammetric measurement plat-

form used (a) on Jayhawk Boulevard, and (b) at the roun-

dabout.

(a) (b)

Figure 3. Four HDR images of Jayhawk Boulevard taken at

the same off-road location aiming at the same direction but

under different environmental conditions: (a) Dayt ime ,

clear sky, (b) Daytime, cloudy sky, (c) Nighttime, dry pave-

ment, and (d) Nighttime, wet pavement.

wood boards place d on the curb. Points 7 and 13 were on

roadside. Points 8 - 10 were on the central island. Points

11 and 12 were on a deflection island. Points 14 - 16

were the centers of two “STOP” signs. Points 17 - 21

were corners of deflection markings. Since the rounda-

bout was not flat, those 21 points were divided into three

groups in data treatment based on their Z coordinates. In

each group, the target points had very similar Z values.

The first group consists of Points 1 - 8, 17 - 19. Among

them, Points 1, 6, 7, and 9 were used as reference points

for data treatment. Other points were used as target

points to validate the photogrammetric measurement.

The second group consists of Points 10, 11, 12, and 16.

Points 10 - 12 were used as reference points, while Point

16 was used as a target point. The third group consists of

Points 13 - 15, 20 and 21. Points 13, 14, 15 and 20 were

used as reference points, while Point 21 was used as a

target point.

5.2. Results of Geometry Measurement

Of every single point measured in the two roadway en-

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Figure 4. Locations of the 24 points measured on Jayhawk Boulevard in the daytime under clear sky.

(a) (b) (c) (d)

Figure 5. The four HDR images of the roundabout taken at the same location aiming at the same direction but under differ-

ent environmental conditions: (a) Daytime, clear sky, (b) Dayti me , cloudy sky, (c) Nighttime, dry pavement, and (d) Night-

time, wet pavement.

Figure 6. Locations of the 21 points measured at the roundabout in the daytime under clear sky.

vironments, the calibrated geometric coordinates Xcali,

Ycali, Zcali were calculated from its pixel location extracted

from the calibrated HDR image generated in the daytime

under clear sky. The calculated Xcali, Ycali, and Zcali were

then compared to the coordinates Xmeter, Ymeter, and Zmeter

actually measured in the field using the laser distance

meter or rangefinder mounted on the 3D angular tripod

head. Equation (4) was used to calculate the measure-

ment errors in percentage of the 3D geometric XYZ

coordinates. Since the measurement errors vary with the

distance, an absolute error in millimeter is not as useful

as the error percentage in reflecting the results of geome-

H. Y. CAI, L. J. LI

OPEN ACCESS JTTs

try measurement, thus, not included.

% 100

cali meter

meter

cali meter

meter

cali meter

meter

−

= ×



−

= ×





−

= ×





(4)

Table 1 summarizes the measurement errors of the 24

points on Jayhawk Boulevard. Likewise, the geometric

errors of measuring the 21 points at the roundabout are

shown in Table 2. In Tables 1 and 2, the interquartile

range (IQR) covers the most common (the middle 50%)

measurement errors in practice (Cai, 2013). It was found

that the errors of geometry measurement on Jayhawk

Boulevard, regardless of X, Y, or Z coordinate, varied a

lot with the measuring distance, e.g., from 5.7% to 39.5%

with an interquartile r ange (IQR) of 14 .3% - 37.5% for X

coordinate, from 6.8% to 40.3% with an interquartile

range (IQR) of 21.1% - 38.6% for Y coordinate, and

from 0.6% to 61.6% with an interquartile range (IQR) of

4.4% - 39.4% for Z coordinate. In addition, the average

errors of measuring the XYZ coordinates were ranged

from 23.1% to 27.5%. The margin of errors for measur-

ing the geometry of the roundabout was also very large,

0.1% - 90.0% (X), 0 .2% - 52.3 % (Y), 0.0% - 13 7.5 % (Z),

due to its sloped pavement. The largest error (137.5%)

occurred at Z coordinate for approximation of the sloped

and curved roundabout pavement. The average errors of

the measured XYZ coordinates of the 21 target points

were in a range of 9.3% - 16.2% (for XYZ coordinates),

with IQR of 1.9% - 16.5% (X), 4.9% - 14.4% (Y), and

0.0% - 2.2% (Z), smaller than those of the longer Jay-

hawk Boulevard.

5.3. Results of L ight Measurement

Luminance mapping the two roadway environments was

at pixel level. Figures 7 and 8 show the light distribution

maps plotted in MatLab measured on Jayhawk Boulevard

and at the roundabou t, respectively, in the daytime und er

clear sky or cloudy sky, or at nighttime with dry or wet

pavement. Each map contains 18 millions values. For the

benefit of direct comparison, the luminance scale of dif-

ferent maps is fixed in a range of 0.00519 cd/m2 (the

minimum luminance recorded in those eight HDR im-

ages)—117782.0 cd/m2 (the maximum luminance rec-

orded). Likewise, the luminance gradient magnitude is

set in a fixed range of 8.950 × 10−7 - 60834.900 cd/m2/

pixel. The luminance gradient direction is in a fixed an-

gular range of −180˚ - 180˚, measured anticlockwise in

light of the x axis of the photographic images (e.g., the

angle 0˚ is to the right hand, 90˚ is up, 180˚/−180˚ is to

Table 1. Errors of measuring the XYZ coordinates of the 24

points on Jayhawk Boulevard.

Measurement Errors (%)

X Y Z

Max 39.5 40.3 61.1

Min 5.7 6.8 0.6

Average 25.0 27.5 23.1

Median 24.1 27.1 10.2

IQR 14.3 - 37.5 21.1 - 38.6 4.4 - 39.4

Table 2. Errors of measuring the XYZ coordinates of the 21

points at the roundabout.

Measurement Errors (%)

X Y Z

Max 90.0 52.3 137.5

Min 0.1 0.2 0.0

Average 16.2 12.1 9.3

Median 8.6 8.6 0.0

IQR 1.9 - 16.5 4.9 - 14.4 0.0 - 2.2

the left, −90˚ is down). Figures 7 and 8 visualize the per-

pixel lighting conditions across the two roadway envi-

ronments.

The two roadway environments had the highest overall

light level in the daytime under clear sky, and the lowest

light level at rainy night. At nighttime, the luminance of

the wet pavement was lower and less uniform than that

of the dry pavement. In particular, puddles that reflected

the light of the road lamps to the camera appeared brigh-

ter. The overall light distribution in sunny days was less

uniform (the gradient magnitude was higher) than that in

cloudy days, and than that at night. In particular, the sky

on cloudy days was less uniform (the gradient magnitude

was higher) than that on sunny days, while the sky un-

iformity was the best at night independent of the pave-

ment conditions.

On Jayhawk Boulevard, the pavement, building façade

and plants had much higher change rate of luminance

than concrete sidewalks, glass windows and the sky. This

is because the pavement and building façade have unpo-

lished rough surface, and plants have complex geometric

shapes, which diffuse the reflected light very well in dif-

ferent directions. At the roundabout, the triangle YIELD

sign had more uniform luminance than surrounding ob-

jects, due to its well-maintained retro-reflective surface.

On the gradient direction maps, objects with polished

surfaces can be easily detected, like road lamp poles,

tripod, building façade, and signs. However, the unpo-

lished rough surfaces, like pavement and plants, were

barely visible, since their reflected light was well dif-

fused without a primary direction. The edges of objects,

H. Y. CAI, L. J. LI

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(a)

(b)

H. Y. CAI, L. J. LI

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(c)

(d)

Figure 7. The lighting conditions of Jayhawk Boulevard (a) in the daytime under clear sky, (b) in the daytime under cloudy

sky, (c) at nighttime with dry pavement, (d) at nighttime with wet pavement.

H. Y. CAI, L. J. LI

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(a)

(b)

H. Y. CAI, L. J. LI

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(c)

(d)

Figure 8. The lighting condi tions of the round about (a) in the dayti me unde r cle ar sky, ( b) in the dayti me under cloudy sky, (c)

at nighttime with dry pavement, (d) at nighttime with wet pavement.

H. Y. CAI, L. J. LI

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e.g., buildings, pavement, sidewalks, poles, plants, could

still be detected due to changed directions of light reflec-

tion on edge s.

Moreover, those luminance maps and luminance gra-

dient magnitude maps shown in Figures 7 and 8 were

further treated for identifying potential roadway hazards,

including unshielded roadway lamps, too bright pave-

ment puddles, extremely high roadway contrasts, and

large non-uniform pevement shadows, etc. Any electric

light sources brighter than a threshold value of 500 - 700

cd/m2 will cause glare at night [32]. This threshold value

could be much higher in the daytime due to increased

adaptation level. Extreme light contrasts can also be

identified by a threshold luminance gradient magnitude,

e.g., 500 - 700 cd/m2/pixel that is consistent with the

threshold luminance, whose polarity is shown by the

gradient direction. The distracting glare sources and large

and sharp pavement shadows will harm the drivers’ de-

tection of pavement markings and road targets, thus,

need to be identified and eliminated for roadway safety.

For demonstration, this study adopted the threshold val-

ues of 5000 cd/m2 and 5000 cd/m2/pixel in the daytime

and 500 cd/m2 and 500 cd/m2/pixel at nighttime. The

number of 5000 was assumed, since the threshold value

in the daytime is not a constant which varies with the

light adaptation level. The threshold values could be ad-

justed for more or less stringent needs. Figure 9 shows

the identified potential hazards of the two roadway envi-

ronments at their pixel lo cations, from which the geome-

tric locations could be calculated (but not actually calcu-

lated in this study due to tremendous workload). Figure

9 is useful to find the quantity, location and magnitude of

any potential lighting hazards in the two roadway envi-

ronments at first glance. It was found that the sky was a

potential glare source in cloudy days but not under clear

sky. At nighttime, the roadway electric lights were the

only potential glare sources.

6. Discussion

Capturing synchronous light and geometry data using the

HDR photogrammetric measurement method developed

in this study could speed up the measurement of roadway

environments. The measurement speed is much faster

than conventional point-by-point measurement using

meters and tools in that millions of target points visible

to the camera lens can be measured simultaneously. The

camera-aided measurement takes only 1 - 2 minutes for

photograph ing the entire scene with sequen tial exposures,

plus another short period of time for measuring reference

points in the field using the laser distance meter or

rangefinder mounted on the 3D angular measurement

tripod head. The average speed was approximately 3

minutes per point in this study. Often four reference

points are sufficient for measuring the pavement and the

sidewalks. For measuring other targets on different

planes, more reference points are needed. A short time of

HDR photographing is critical for the accuracy of lu-

minance measurement. Yet the measurement of steady

reference points in the field could take longer time with-

out jeopardizing the accuracy of their XYZ coordinates.

The HDR photogrammetric measurement can solve

some problems of existing roadway environment mea-

surements and facilitate the data treatment. The existing

measurements of light and geometry of the roadway en-

vironments are separated using different devices. The

separated measurements increase the field measurement

labor, slow the data collection, and cause difficulties in

follow-up data alignment due to inconsistent measuring

resolutions and displaced measuring points. These prob-

lems can be solved using the HDR photogrammetric

measurement, in which concurrent luminance and geo-

metry data can be acquired in extraordinarily high reso-

lution. The so acquired 18 millions of synchronous light

and geometry data embedded on an HDR image of the

roadway environment can also facilitate the evaluation of

the overall roadway lighting conditions. Example appli-

cations include 1) spatial and temporal light distributions

illustrated on 2D luminance map and 2D/3D luminance

gradient maps, 2) identification of potential road visual

hazards (e.g., highlight, non-uniformity, large shadow,

extremely large contrast, etc.) on the pavement and si-

dewalks together with their sizes and shapes, and 3)

identification of potential glaring sources and their geo-

metries and locations. These applications were tested

useful in this study in the roadway environments on Jay-

hawk Boulevard and at the roundabout. Moreover, the

acquired concurrent light and geometry data could be

used for derivation of some higher level judging metrics,

with which the measured geometry and light are jointly

involved, for timely roadway environment assessment.

Such advanced metrics include visibility of objects on

the road, legibility of roadway signs, glare, uniformity,

and roadway surface reflectance, etc. Their calcultions

were not covered in this paper but will be tested in fur-

ther studies.

The HDR photogrammetric measurement of the road-

way environments is aided by affordable consumer-grade

digital cameras, personal computers, and free online

software programs (e.g., Photosphere, Radiance, and the

MatLab code shared online for data treatment and image

plotting (http://people.ku.edu/~h717c996/research.html).

Although more equipment is needed, such as a laser dis-

tance meter or rangefinder, the Minolta meters LS-100

and CL-200A, they can be shared with other uses. The

specially designed 3D angular measurment tripod head

that is mounted on the photogrammetric measurement

platform is still affordabe (recently quoted $1387 for a

prototype).

H. Y. CAI, L. J. LI

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(a)

(b)

(c)

(d)

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(e)

(f)

(g)

(h)

Figure 9. The identified potential lighting hazards and their pixel locations on the HDR images, (a) on Jayhawk Boulevard in

the daytime under clear sky, (b) on Jayhawk Boulevard in the daytime under cloudy sky, (c) at the roundabout in the day-

time under c lear sky, (d) at the roundabo ut in t he day ti me under cloudy sky, (e) on Jayhawk Boulevard at nighttime with dry

pavement, (f) on Jayhawk Boulevard at nighttime with wet pavement, (g) at the roundabout at nighttime with dry pavement,

(h) at the roundabout at nighttime with wet pavement.

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In addition, the HDR pho tog rammetric measurement is

non-contact, non-destructive, and could be off road. Thus,

it is safer and more efficient than on-road measurement

using meters and tools and the aforementioned remote

technologies equipped on vehicles moving on road.

However, off-road measurement is preferred only when

the approximation of the typical viewing directions of

drivers and pedestrians at a off-road location is valid. It is

challenging but theoretically possible to extend the

sightline of a driver on road to an off-road position given

that the measurement of luminance and geometry (e.g.,

size, shape, spacing, width, slope, etc.) of the targets is

indepen dent of the viewing distance .

On the other hand, since the luminance measurement

depends on the viewing direction of the camera lens (or

the aiming direction of a luminance meter), the measured

2D luminance maps and 3D/2D luminance gradient maps

can only be used to evaluate the roadway environments

viewed at that specific viewing direction at which the

camera lens aims. However, this problem is not new,

which also exists in the conventional meter measurement.

By taking multiple HDR images of the roadway envi-

ronments from different viewing directions of a typical

observer, this problem could be relieved. Multiple HDR

images of the same roadway environment can be taken

either simultaneously using multiple test rigs mounted at

different locations or sequentially using the same set of

test rig.

The HDR photogrammetric measurement can handle

many roadway elements in different shapes, sizes and

orientations, as long as they are visible to the camera lens.

Theoretically, an HDR image is embedded with millions

of light and geometry data of target points visible to the

camera lens. Thus, the HDR photogrammetric measure-

ment can cover any visible planes with available refer-

ence points. In this study, as two examples, dozens of

roadway points were measured not only on the straight

and flat Jayhawk Boulevard, but also on the oblique and

circular pavement and sidewalks of the roundabout.

Therefore, the HDR photogrammetric measurement is

deemed more useful than the existing remote methods

developed by Drakopoulos and Ornek [4], Tsai et al.

[5,6], Wu and Ts ai [7,8], and Nehate and Rys [12], which

are unable to measure oblique and circular planes.

Nonetheless, this study did not validate the accuracy of

the HDR photogrammetric measurement for luminance

mapping the two roadway environments, because of two

reasons. First, the accuracy of the HDR photography in

indoor and outdoor environments have already been

proven acceptable (1.5% - 10.1% for luminance mapping

gray, black, color, and light-emitting surfaces) in the li-

terature [26,27]. The two roadway environments do not

differ from the previous measured environments in light

of the dynamic range of luminance. Second, there is no

appropriate way to validate the accuracy of luminance

measurement on road using a luminance meter. With a

low resolution, the luminan ce meter is incapable to mea-

surement a small target point faraway on road. In addi-

tion, since luminance measurement depends on the

viewing direction, both the luminance meter and the

camera lens should aim at the target point at exactly the

same direction, which is impossible without blocking the

view of the camera lens for photographing. Then, why

not measure the luminance in sequence? Unfortunately,

meter measurement before or after the HDR photo-

graphing is useless since the roadway lighting often va-

ries over time.

On the other hand, it was found that the HDR photo-

grammetric measurement with current equipment had a

large margin of errors for g eometry measurement, which

varied with the measuring distance. Target points that

were farther from the camera had larger measuring er-

rors. For measuring the longer Jayhawk Boulevard, the

errors varied from 0.6% to 61.1%. For measuring the

closer roundabout, the margin of errors was from 0.0% to

137.5% that is larger due to its sloped pavement. The

average errors were calculated as 25.0% (for X coordi-

nate), 27.5% (Y), and 23.1% (Z) for measuring the 24

points on Jayhawk Boulevard, and 16.2% (X), 12.1%

(Y), 9.3% (Z) for measuring the 21 points at the rounda-

bout with smaller measurement distances. The large er-

rors were caused by some limitations of current mea-

surement equipment. First, the 3D angular measurement

tripod head has a precision of 0.1˚, which is insufficient

to measure targets that are very far from the camera, due

to the high sensitivity of angles in the photogrammetric

transformation. Measuring the XYZ coordinates of a

target point in a roadway environment is more difficult,

thus, less accurate, than the simple distance measurement

from the target to the camera. Second, the rangefinder

was used to replace the laser distance meter for measur-

ing faraway targets that were beyond its range of 650

feet. The rangefinder had an error of ±1 m, which con-

tributed to the large errors of measuring faraway targets.

Despite the large errors for geometric measurement,

the HDR photogrammetric measurement is still having

similar accuracy to most existing roadway measurement

methods, e.g., Drakopoulos and Ornek’s algorithm (the

measurement errors were up to 63.8%) [4], Tsai’s algo-

rithm (error was within 0.01 - 0.3 m) [5-8], Nehate and

Rys’ method using GPS data (error was ±0.2 m) [12],

Castro et al.’s Leica System 500 GPS (with maximum

error of 1 m) [13]. Nonetheless, the HDR photogramme-

tric measurement is useful for measuring synchronous

light and geometry data in different types of roadway

environments, which no previous measurement methods

could do in such a high resolution.

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7. Conclusion

The HDR photogrammetric measurement developed in

this study is useful for evaluating roadway environments

but still needs improvements on the measurement accu-

racy and the speed of data treatment. Two measures can

be used in further studies to reduce the margin of errors

of geometry measurement. First, the angular resolution of

the 3D angular measurement tripod head could be in-

creased from 0.1˚ to 0.05˚, which is expected to largely

reduce the measurement errors. Second, deployment of

more accurate techniques for measuring the XYZ coor-

dinates of reference points in the field, e.g., using more

advanced remote sensing technologies, will also largely

reduce the workload in the field. Future studies will also

need to look into accurate compensation of the difference

in aiming angles between the camera and distance meters

when they are aiming at the same target due to their dif-

ferent optical structures. In addition, a program running

in MatLab is currently under development and testing in

the University of Kansas lighting research laboratory to

automatically conduct the photogrammetric transforma-

tion and calibrations. This program running on standard

PC can speed up the tremendous data treatment.

Acknowledgements

This research was supported by a Start-up Fund provided

by the School of Engineering and a New Faculty General

Research Fund provided by the Research and Graduate

Studies at the University of Kansas. The authors thank

Ms. Xiaomeng Su for her help on the MatLab code for

data treatment and image plotting.

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