Nagata, Y. et al., 1 August 2018
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49

1

DRAFT
Visibility Assessment Using Driving Video Images
Recorded by Onboard Video Camera
Authors:
Yasuhiro Nagata, Ph.D., Principal Researcher, Hokkaido Development Engineering Center,Japan
North-11, West-2, Kita-ku, Sapporo, Hokkaido, 001-0011, Japan
Tel: +81-11-738-3363, Fax: +81-11-738-1889, E-mail: nagata@ decnet.or.jp
Toru Hagiwara, Ph.D., Professor, Graduate School of Engineering, Hokkaido University
North-13, West-8, Kita-ku, Sapporo, Hokkaido, 060-8628, Japan
Tel: +81-11-706-6214, Fax: +81-11-706-6214, E-mail: hagiwara@eng.hokudai.ac.jp
Yuki Nakamura, Student, Graduate School of Engineering, Hokkaido University
North-13, West-8, Kita-ku, Sapporo, Hokkaido, 060-8628, Japan
Tel: +81-11-706-6214, Fax: +81-11-706-6214, E-mail: yuki-nakamura@eis.hokudai.ac.jp
Yasuhiro Kaneda, M.S., Head Manager, Hokkaido Development Engineering Center, Japan
North-11, West-2, Kita-ku, Sapporo, Hokkaido, 001-0011, Japan
Tel: +81-11-738-3363, Fax: +81-11-738-1889, E-mail: kaneda@decnet.or.jp
Naoki Matsuoka, B.E., Managing Director, Hokkaido Weather Technology Center, Japan
North-4, West-23, Chuo-ku, Sapporo, Hokkaido, 064-8555, Japan
Tel: +81-11-622-2235, FAX: +81-11-622-8398, E-mail: matn@sapporo.jwa.or.jp
Kazuhiro Tanji, M.S., Head Manager, Japan Weather Association, Japan
Sunshine 60 Bld. 3-1-1, Higashi-Ikebukuro, Toshima-ku, Tokyo, 170-6055, Japan
Tel: +81-3-5958-8156, Fax: +81-3-5958-8157, E-mail: tanji@jwa.or.jp*corresponding author
The authors confirm contributions to the paper as follows. Study conception and design: Y.
Nagata, T. Hagiwara. Implementation of subjective experiments: Y. Nakamura. Interpretationof driver visibility: Y. Kaneda and N. Matsuoka. Collection of visibility data and driving video
images: K. Tanji. All authors reviewed the results and approved the final version of themanuscript.
Submission date: August 1, 2018
Word count: 3,500 + 250 x 11 (7 figures and 4 tables) = 6,250 words
Submitted for Presentation at the 98th Annual Meeting of the Transportation Research
Board and for Publication in Transportation Research Record
Transportation Research Board
98th Annual Meeting
13-17 January 2019
Washington, D.C.

Nagata, Y. et al., 1 August 2018
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49

2

ABSTRACTThe present study proposes a system for evaluating visibility from the driver’s viewpoint using
the weighted intensity of power spectrum (WIPS). The present study investigates whether the
average weighted intensity of power spectrum (WIPS) determined from driving video images
recorded by onboard video camera can be used to identify poor visibility conditions on theroad ahead by comparing WIPS values with subjective visibility evaluations of the same
driving video images. A total of 39 video clips of driving, each 10 seconds long, selected fromlarge number of clips were presented to 12 participants, who responded to a questionnaire on
their subjective evaluation of visibility on the road ahead. The WIPS values were found to beconsistent with the subjective visibility assessments. The meteorological visibility opticalrange values were found to not correspond to subjective visibility evaluations. It is supposedthat the WIPS values indicate visibility on the road ahead and that WIPS would be preferable
to meteorological optical range as an index of visibility.

Nagata, Y. et al., 1 August 2018
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49

3

INTRODUCTION
Visibility information along the whole highway allows road maintenance managers to better
operate and maintain roads under adverse conditions. Road maintenance agencies in
Hokkaido, Japan, have been using road patrols several times per day during severe weather
conditions. Such patrols give reliable visibility information that assists in maintaining driving
safety on highways. Therefore, we have developed a system for monitoring visibility
accurately and widely for the entire length of the highway. Previously, vehicle-mounted
visibility meters were used to evaluate the visibility on the road ahead as perceived by thedriver. Nagata et al. (1) proposed a road visibility measuring system that uses still images
recorded by an onboard video camera to identify poor visibility on the road ahead. They
investigated whether the weighted intensity of power spectra (WIPS) value determined fromstill images recorded by the onboard video camera can be used to identify poor visibility
during driving. The changes in WIPS values were found to correspond roughly in time tochanges in visibility meter values.
A system for monitoring visibility accurately and easily evaluated by the driver's eye view isrequired. Several methods of assessing visibility from still images have been studied (2–7).
Hagiwara et al. (8) proposed a method for identifying poor visibility under adverse weather
conditions by processing CCTV digital images. The magnitude of the WIPS value represents
the difference in spatial frequencies within the image based on the human contrast sensitivity
function. Hagiwara proposed WIPS as a way of using images to quantify poor visibility.
Nagata et al. (9) conducted subjective assessments of road images taken by CCTV cameras.
They examined the amount of variation in subjective estimations of road conditions fromdigital images and how closely the estimated visibility for a given road image correlated with
the WIPS for that image. Assessments of daytime images had little variation among
participants and correlated closely with WIPS. Feasibility studies in the daytime during the
2005-2006 winter and the 2006-2007 winter were performed on National Route 230 and
National Route 231 in Japan (10). Using stored road visibility data, WIPS values were foundto correspond closely with subjective visibility evaluations for the same road images.
Thus, we propose a system for using WIPS to evaluate the visibility from the driver’s
perspective. The present study investigates whether the WIPS determined from driving video
images recorded by onboard video camera can be used to identify poor visibility conditions on
the road ahead by comparing WIPS values with subjective visibility evaluations of the same
driving video images. In the present study, we also compared the subjective visibility
evaluation estimated by drivers from driving video images with the meteorological opticalrange measured by a vehicle-mounted visibility meter.

Nagata, Y. et al., 1 August 2018
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49

4

DATA COLLECTION
Measurements on a Highway under Low-Visibility Conditions
On several occasions, we measured the meteorological optical range while recording thedriving video image under severe visibility conditions in the daytime during two months of the
2017-2018 winter outside of Sapporo City in Hokkaido, Japan. An instrumented vehicle
installed with a vehicle-mounted visibility meter (Figure 1) was used for measurements.
Meteorological Optical Range (MOR)
MOR is recorded by the vehicle-mounted visibility meter at 150 cm high. This compact
forward-scatter visibility sensor (Figure 1) was developed by Meisei Electric Co., Ltd. With ameasurement range of 20 to 2,000 meters, the Meisei Visibility Sensor TZF-31A offers
reliable visibility measurement in snowfall and snowstorms. The system outputs a value of
2,000 meters when the MOR value exceeds 2,000 meters. The sampling rate is 10Hz. The
present study uses the MOR value averaged for each second.

Visibility
meter
MOR
measurementarea
Onboard video
cameraFIGURE 1 The vehicle installed with a vehicle-mounted
visibility meter and an onboard video camera

Nagata, Y. et al., 1 August 2018
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49

5

Driving Video Images
The driving video images were recorded by an onboard video camera installed at center of thevehicle between the driver and the co-driver at 130 cm high. The onboard video camera is
Driving Pro 200, developed by Transcend Co., Ltd. The recorded video image was 1920×1088
pixels recorded at 29 frames per second.
In the present study, a 10-second driving video clip (hereinafter: a clip) was presented to thedrivers, and the drivers completed a questionnaire about the visibility conditions on the roadahead. The driving video clip recorded from 15:07:40 to 15:22:30 on 26 January 2018 wasselected from several clips in this study because this clip included conditions in which thevisibility ranged from 50 to 500 meters. We created one 10-second video clip for eachsuccessive 10 seconds of 890 clips from 15-minute driving videos. A total of 890 clips werecreated. It was thought that the assessment of subjective visibility in the clips would be
influenced by the presence of leading vehicles, oncoming vehicles, sharp curves ahead, andstops at intersections. After clips that included these influences were removed, 267 clips of 10
seconds each remained.

Calculating WIPS for each 10-second clip
Figure 2 shows process for calculating the WIPS value for each clip. First, 10 still images per
second were automatically captured from the clip. If a still image included a wiper blade, theexperimenter manually selected the nearest still image without a wiper blade. Each still image
was halved in size from 1920×1088 pixels to 960×544 pixels. Second, a two-dimensional
image of 256×256 pixels was cropped from the center of each still image. Third, the grayscaleintensity of each pixel was calculated from the intensities of the red-green-blue (RGB)
components recorded in the two-dimensional image of 256×256 pixels. The grayscaleintensity ranges from 0 to 255. Fourth, the image was broken down into sinusoidal gratings ofdifferent spatial frequencies using two-dimensional fast Fourier transform (FFT). The power
spectrum value computed by FFT corresponds to the amplitude of the spatial frequency foreach cycle per degree. At the final step, the power spectrum intensities in the range of 1.5 to 18
cycles per degree were summarized. In this study, the average of 10 WIPS values per clip was
calculated. Under clear conditions, the power spectra for each spatial frequency component of
the road image are great and the WIPS value is large. Under poor visibility conditions, such as
those in fog or snow, the power spectra are small and WIPS value is small.

Nagata, Y. et al., 1 August 2018
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49

6

FIGURE 2 The five steps in calculating the WIPS value for each 10-second video clip

Nagata, Y. et al., 1 August 2018
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49

7

COMPARISON OF WIPS AND SUBJECTIVE VISIBILITY EVALUATIONS
Eighteen clips used in experiment, classified into six ranges based on the average WIPSvalue
The average WIPS values for the 267 clips ranged from 6.83 to 9.12. The average WIPS
values were classified into six WIPS ranges: less than 7.0, 7.0 - 7.5, 7.5 - 8.0, 8.0 - 8.5, 8.5 - 9.0
and greater than 9.0. The numbers of clips corresponding to each range above are 13, 29, 51,
89, 56 and 29, respectively. The average MOR values for the 267 clips ranged from 109 meters
to 716 meters. Three clips were assigned to each WIPS range: a clip of the minimum MOR forthat range, the median MOR for that range and the maximum MOR for that range. Then, in theexperiment, the 18 clips listed in Table 1 were evaluated.
Twenty one clips used in the experiment, classified by seven ranges based on the average
MOR value
The average MOR values for the 267 clips ranged from 109 meters to 716 meters. The average
MOR values were classified into seven ranges: 102.0 t 102.1 m (100 - 126 m), 102.2 - 102.3 m (158
- 200 m), 102.3 - 102.4 m (200 - 252 m), 102.4 - 102.5 m (252 - 316 m), 102.5 - 102.6 m (316 - 398m) and greater than 102.6 m (greater than 398 m). The number of clips in each range is 9, 17,
19, 72, 83, 43 and 24, respectively. The average WIPS values for the 267 clips ranged from6.83 to 9.12. Three clips were assigned to each MOR range: a clip of the minimum WIPS forthat range, the median WIPS for that range and the maximum WIPS for that range. Then, inthe experiment, the 21 clips listed in Table 2 were evaluated.

Nagata, Y. et al., 1 August 2018
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49

8

TABLE 1 The 10-second video clips classified by the six WIPS ranges
Type of10-second
10-second
WIPS
NumberTime ofNo.
MOR
average of
average ofrangeof datavideo image
value
MOR values
WIPS values
15:08:07
1
Maximum
162 m
6.94- 15:08:16
less than
15:08:02
2
13
Median
191 m
6.85
7.0
- 15:08:11
15:07:58
3
Minimum
224 m
6.93- 15:08:07
15:08:08
4
Maximum
167 m
7.02- 15:08:17
15:08:45
5
Median257 m
7.21
7.0 - 7.5
29
- 15:08:54
15:07:40
6
Minimum
400 m
7.50- 15:07:49
15:17:24
7
Maximum
109 m
7.74- 15:17:33
15:08:30
7.5 - 8.0
51
8Median222 m
7.85- 15:08:39
15:07:41
9
Minimum
371 m
7.55- 15:07:50
15:20:15
10
Maximum
127 m
8.03- 15:20:24
15:18:21
11 8.0 - 8.5
89Median303 m
8.03- 15:18:30
15:12:53
12
Minimum
717 m
8.36- 15:13:02
15:16:29
13
Maximum
156 m
8.60- 15:16:38
15:13:56
56
14 8.5 - 9.0
Median284 m
8.78- 15:14:05
15:18:11
15
Minimum
520 m
8.55- 15:18:20
15:14:26
16
Maximum
284 m
9.03- 15:14:35
greater
15:14:38
17
29
Median359 m
9.02
than 9.0- 15:14:47
15:15:16
18
Minimum
447 m
9.00- 15:15:25

Nagata, Y. et al., 1 August 2018
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49

No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21

9

TABLE 2 The 10-second video clips classified by the seven MOR ranges
Type of10-second
10-second
NumberTime of
MOR range
WIPS
average of
average ofof datavideo image
value
WIPS values
MOR values
15:17:23
Maximum
7.69
119 m- 15:17:32
102.0 - 102.1 m
15:17:28
9
Median7.80
122 m(100 - 126 m)- 15:17:37
15:20:13
Minimum
7.92
122 m- 15:20:22
15:17:22
Maximum
7.59
129 m- 15:17:31
15:20:16
102.1 - 102.2 m
Median
8.09
140 m
17
- 15:20:25(126 - 158 m)
15:16:29
Minimum
8.60
156 m- 15:16:38
15:08:10
Maximum
6.83
180 m- 15:08:19
102.2 - 102.3 m
15:17:16
19
Median7.88
161 m(158 - 200 m)- 15:17:25
15:20:20
Minimum
8.34
197 m- 15:20:29
15:08:01
Maximum
6.88
201 m- 15:08:10
102.3 - 102.4 m
15:08:29
72
Median7.63
222 m(200 - 252 m)- 15:08:38
15:20:43
Minimum
8.59
223 m- 15:20:52
15:07:57
Maximum
7.02
265 m- 15:08:06
102.4 - 102.5 m
15:18:22
83
Median8.04
282 m(252 - 316 m)- 15:18:31
15:14:33
Minimum
9.07
315 m- 15:14:42
15:08:43
Maximum
7.39
326 m- 15:08:52
2.5
2.6
10 - 10 m
15:15:24
43
Median8.25
343 m(316 - 398 m)- 15:15:33
15:15:04
Minimum
9.12
397 m- 15:15:13
15:07:40
Maximum
7.50
400 m- 15:07:49
greater than
102.6 m
15:12:53
24
Median8.36
717 m(greater than
- 15:13:02
398 m)
15:15:07
Minimum
9.09
418 m- 15:15:16

Nagata, Y. et al., 1 August 2018
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49

10

Participants
Table 3 lists the attributes of the 12 participants. All were licensed to drive in Japan, and all
drove at least once a month in winter. They have not participated in surveys or analyses of roadvisibility, nor did they receive information regarding the experiment prior to the experiment.
At the beginning of experiment, the experimenter spent 20 minutes explaining the schedule,
the experimental overview and the visibility evaluation tasks to be performed during theexperiment, the risks of the experiment, the cancellation policy and emergency procedures.
Once the explanation was complete, participants gave written informed consent ofparticipation. No individual declined to participate. The research methodology was approvedby the Ethical Review Committee for Research with Human Subjects in the Engineering
Course of Hokkaido University, Japan.

Group

Group 1

Group 2

Group 3

TABLE 3 The attributes of the 12 participants
No.
Sex
Age
Driving frequency
Vision correction
1

M

24

Monthly

Glasses2

M

24

Weekly

None

3

M

23

Monthly

Glasses4

M

21Monthly

Contact lens

5

M

54

WeeklyGlasses

6

M

50

Daily

Glasses7

F

45

WeeklyGlasses8

M

30

WeeklyGlasses

9

M

63

Daily

Glasses

10

F

42

Daily

Glasses

11

M

38

WeeklyGlasses

12

F

38

WeeklyGlassesNagata, Y. et al., 1 August 2018
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49

11

Evaluation Sheets
Each clip was evaluated according to the three subjective rating scales shown in Figure 3: thesubjective visibility scale (SVS), the driving comfort scale (DCS) and the visibility-range
scale (VRS). The SVS is a measure of visibility conditions whereby participants judge thevisibility of the road ahead by viewing the clip. The DCS is a measure of subjective driving
comfort that is based on viewing the clip. The VRS is a measure of visibility conditions
whereby participants judge the visibility range of the road ahead from a presented clip. SVS
ranged from the low of 1 (“Not visible”) to the high of 7 (“Visible”). DCS ranged from the lowof 1 (“Uncomfortable”) to the high of 7 (“Comfortable”). VRS ranged from the low of 1(“Visibility range of less than 50 m”) to the high of 7 (“Visibility range of more than 500 m”).
Experimental Design and Procedure
The experiment was carried out in a conference room of the Hokkaido Development
Engineering Center. The participants were divided into the three groups shown in Table 3.
Before the experiments, the participants were instructed on how to assess the visual conditionsof the clip according to the three scales shown in Figure 3. The experiment took about one
hour per group. Table 4 shows the sequence of six tests per group. In each test, the clips wereshown randomly on a screen 0.9 m in height by 1.7 m in width to subjects seated 3.4 m in frontof the screen. Each test took around 5 minutes. After viewing each clip, the participants wereasked to mark their ratings on an assessment sheet.

Driving comfort scale (DCS)
Visibility range scale (VRS)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49

Subjective visibility scale (SVS)

Nagata, Y. et al., 1 August 2018

Figure 3 The assessment sheets for the experiments

12Nagata, Y. et al., 1 August 2018
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49

13

TABLE 4 Order of the three assessment runs for each group in the experiments
Group
No.
10-second video clips
Assessment sheets1
10-second video clips Subjective visibility scale (SVS)
2
3Group 1

Driving comfort scale (DCS)

classified by
6 WIPS ranges

Visibility range scale (VRS)
10-minute break

4
5
6
1
2
3
Group 2

10-second video clips
classified by
7 MOR ranges

Visibility range scale (VRS)

10-second video clips
classified by
6 WIPS ranges

Driving comfort scale (DCS)

Driving comfort scale (DCS)
Subjective visibility scale (SVS)
Visibility range scale (VRS)
Subjective visibility scale (SVS)

10-minute break
4
5
6
1
2
3

Group 310-second video clips
classified by
7 MOR ranges

Subjective visibility scale (SVS)

10-second video clips
classified by
6 WIPS ranges

Visibility range scale (VRS)

Visibility range scale (VRS)
Driving comfort scale (DCS)
Subjective visibility scale (SVS)
Driving comfort scale (DCS)

10-minute break
4
5
6

10-second video clips
classified by
7 MOR ranges

Driving comfort scale (DCS)
Subjective visibility scale (SVS)
Visibility range scale (VRS)Nagata, Y. et al., 1 August 2018
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49

14

RESULTS
Subjective visibility evaluations for the 10-second video clips classified by the six WIPS
ranges
The results of subjective visibility evaluation using the clips classified by the six WIPS ranges
from Table 1 are shown in Figure 4. Responses for each WIPS range totaled 36. According to
Figure 4(A), the SVS value decreased proportionally to decreases in the WIPS value. The
percentage of participants who gave the low rating (“SVS of 1 or 2”) was 0% for the clips withhigh WIPS values, i.e., WIPS of greater than 8.5. However, the percentage of participants who
gave the low rating (“SVS of 1 or 2”) was high for the clips with low WIPS values, i.e., WIPSof less than 7.5. In Figure 4(B), the DCS value indicates a similarly proportional relation tothat between WIPS value and DCS value. The DCS value decreases proportionally todecreases in the WIPS value. Figure 4(C) shows the percentage of participants choosing each
VRS value for each of the six WIPS ranges. The VRS value decreases proportionally todecreases in the WIPS value. The percentage of participants who gave the low VRS rating(“less than 100 m”) was low for the clips with high WIPS values, i.e., WIPS of greater than
8.0. However, the percentage of participants who gave the low VRS rating (“less than 100 m”)
was over 50% for the clips with low WIPS values, i.e., WIPS of less than 7.5.
Subjective visibility evaluation for the 10-second video clips classified by the seven MOR
ranges
The results of subjective visibility evaluation using the clips classified by the seven MOR
ranges from Table 2 are shown in Figures 5. Responses for each WIPS range totaled 36. Figure5(a) shows the relationship between the SVS value and the WIPS value. This relationship was
unclear. The 200-m to 252-m interval of MOR shows the greatest percentage of participants
who gave the “SVS of 1 or 2”. The 158-m to 200-m interval of MOR shows thesecond-greatest percentage of participants who gave the “SVS of 1 or 2”. When the MOR
range was 100 m to 126 m or 126 m to 158 m, the percentage of participants who gave thelowest rating (“SVS of 1”) was 0%. The DCS rating in Figure 5(b) and the VRS rating in
Figure 5(c) have similar composition ratios to those for SVS. The subjective visibility
evaluations for the clips as classified by the seven MOR ranges were not consistent with thethree types of subjective visibility evaluations.

Nagata, Y. et al., 1 August 2018

Subjective visibility scale (SVS)A

Driving comfort scale (DCS)

B

C
Visibility range scale (VRS)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49

15

FIGURE 4 SVS, DCS and VRS values for the 10-second video clips
classified by the six WIPS ranges

Nagata, Y. et al., 1 August 2018

Subjective visibility scale (SVS)a

Driving comfort scale (DCS)bcVisibility range scale (VRS)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49

16

FIGURE 5 SVS, DCS and VRS values for the 10-second video clips
classified by the seven MOR ranges

Nagata, Y. et al., 1 August 2018
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49

17

Results of comparison between the average of subjective visibility evaluations (SVS,
DCS) and the average WIPS value
The scatterplots in Figure 6(A) and Figure 6(B) show the results of the experiment using the
18 clips listed in Table 1. The scatterplots in Figure 6(a) and Figure 6(b) show the results of theexperiment using the 21 clips listed in Table 2. Figure 6(A) and Figure 6(a) show scatterplotsof the relationship between the average SVS values and the average WIPS value. Each of thetwo determination coefficients in Figure 6(A) and Figure 6(a), which plot WIPS values versus
SVS values, exceeds 0.9, and the parameters of the estimated regression equations are similar
between the two scatterplots. Figure 6(B) and Figure 6(b) show scatterplots of the relationshipbetween the average DCS values and the average WIPS values. Each of the two determination
coefficients in Figure 6(B) and Figure 6(b), which plot WIPS values versus SVS values, arearound 0.9, and the parameters of the estimated regression equations are similar between thetwo scatterplots. The determination coefficients for DCS are smaller than those for SVS.
Results of comparison between the average of subjective visibility evaluations (SVS,
DCS) and the average MOR value
The scatterplots in Figure 7(A) and Figure 7(B) show the results of the experiment using the
18 clips listed in Table 1. The scatterplots in Figure 7(a) and Figure 7(b) show the results of theexperiment using the 21 clips listed in Table 2. Figure 7(A) and Figure 7(a) show scatterplotsof the relationship between the average SVS values and the average MOR values. Figure 7(B)and Figure (b) show scatterplots of the relationship between the average DCS values and theaverage MOR values. The determination coefficient for each of these scatterplots is less than
0.3.

Nagata, Y. et al., 1 August 201818

Driving comfort scale (DCS)

Subjective visibility scale (SVS)

1
2
Video clips classified by six WIPS ranges Video clips classified by seven MOR ranges
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
A
a
Responses: 18
Responses: 21
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
B
b
36
Responses: 18
Responses: 21
37
38 FIGURE 6 Scatterplots of subjective visibility evaluations for the 10-second video clips
versus average WIPS values
39
40
41
42
43
44
45
46
47
48
49

Nagata, Y. et al., 1 August 201819

Driving comfort scale (DCS)

Subjective visibility scale (SVS)

1
2
Video clips classified by six WIPS ranges Video clips classified by seven MOR ranges
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
A
a
19
Responses: 18
Responses: 21
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
B
b
36
Responses: 18
Responses: 21
37
38 FIGURE 7 Scatterplots of subjective visibility evaluations for the 10-second video clips
versus average MOR values
39
40
41
42
43
44
45
46
47
48
49

Nagata, Y. et al., 1 August 2018
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49

20

DISCUSSION AND CONCLUSIONSThe present study investigated whether the average weighted intensity of power spectrum(WIPS) determined from driving video images recorded by onboard video camera could be
used as an indicator of poor visibility conditions on the road ahead by comparing the WIPS
values with subjective visibility evaluations of the same driving video images. The
meteorological optical range (MOR) and the driving video image of the road head were
simultaneously recorded in the daytime under severe visibility conditions in winter. A total of
267 video clips, each 10-seconds long, were created from the recorded diving video images,
and the WIPS value was calculated for each clip. In the laboratory, 39 video clips selectedfrom 267 clips were presented to the 12 participants, and they had to complete a questionnaire
about the visibility conditions on the road ahead. From this investigation, the WIPS value wasfound to be consistent with both the SVS and the DCS value. The MOR value did notconsistently correspond to the SVS value nor to the MOR value. It is supposed that the WIPS
value could show the visibility level of the road ahead and that it was preferable to the MORvalue as an index of visibility.
The WIPS value might represent not only the visibility range immediately in front of thevehicle but also the visibility conditions for the entire scene ahead. Matsuzawa et al. (11)revealed that the visibility as perceived by drivers during blowing snow is affected by the roadsurface conditions and the surrounding environment (i.e., urban versus suburban). When the
background of the road ahead has few objects within the image, the driver might judge thevisibility to be poor even when the visibility conditions are not so poor. WIPS values
determined from images recorded by onboard camera might indicate visibility levels that
correspond to those perceived by drivers.
A method is needed whereby visibility can be monitored easily, accurately and along the entire
length of the highway, so that a smart system for winter highway maintenance can be
developed. The system proposed in the present study might be feasible for evaluating thevisibility easily and accurately from the driver’s-eye view using WIPS. Probe vehicles ofdetermine visibility conditions based on WIPS values of the road travel environment.
However, WIPS values of images with a large number of objects should become large
whereas visibility conditions are poor. We should propose a reliable and feasible estimation
method for calculating WIPS values using images recoded by the onboard camera.

Nagata, Y. et al., 1 August 2018
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49

21

REFERENCES(1) Nagata, Y., Hagiwara, T., Kaneda, Y., Matsuoka, N., and Kazuhiro, T. Development of
Visibility Assessment Method using Images Recorded by On-board Camera. Presented at 97th
Annual Meeting of the Transportation Research Board, Washington, D.C., 2018.
(2) Chiba, T., Ishimoto, K., and Kajiya, Y. Spatial and Temporal Changes of Visibility in
Blowing Snow and Fog. 4th International Symposium on Snow Removal and Ice Control
Technology, 1993. TRB, Washington, D.C.
(3) Kwon, T.K. Measurement of Motorist’s Relative Visibility Index (MRVI) through Video
Images. Presented at 80th Annual Meeting of the Transportation Research Board, Washington,
D.C., 2001.
(4) Hagiwara, T., Fujita, S., and Kizaka, K. Assessment of Visibility on Roads under Snowy
Conditions Using Digital Images. Proceedings of 11th International Road Weather
Conference, 2002.
(5) Hagiwara, T., Kizaka, K., and Fujita, S. Development of Visibility Assessment Methods
with Digital Images under Foggy Conditions. Transportation Research Record: Journal of theTransportation Research Board, 2004. No. 1862: pp. 95-108.
(6) Hallowell, Robert G., Matthews, Michael P., and Pisano, Paul A. Automated Extraction of
Weather Variables from Camera Imagery. Proceedings of the 2005 Mid-Continent
Transportation Research Symposium, 2005. pp. 1-13.
(7) Nicolas Hautiere, Raouf Babari, Eric Dumont, Jacques Parent Du Chatelet and Nicolas
Paparoditis. Measurements and Observations of Meteorological Visibility at ITS Stations.
Climate Change and Regional/Local Responses, 2013. World’s largest Science, Technology &Medicine Open Access book publisher: pp. 89-108(8) Hagiwara, T., Ota, Y., Kaneda, Y., Nagata, Y. and Araki, K. Method of Processing
Closed-Circuit Television Digital Images for Poor Visibility Identification. Transportation
Research Record: Journal of the Transportation Research Board, 2006. No. 1973: pp. 95-104.
(9) Nagata, Y., Hagiwara, T., Kaneda, Y., Araki, K., and Murakami, K. Simple Way to Use
Closed-Circuit Television Road Images for Poor-Visibility Information. Transportation
Research Record: Journal of the Transportation Research Board, 2006. No.1980: pp.
105-116.
(10) Nagata, Y., Hagiwara, T., Kaneda, Y., Araki, K., and Sasaki, H. Application of Road
Visibility Information System (RVIS) to Winter Maintenance. Transportation Research
Record: Journal of the Transportation Research Board, 2008. No.2055: pp. 128-138.
(11) Matsuzawa, M., Takechi, H., Kajiya, Y., Ito, and Y., Igarashi, M. How Drivers Perceive
Visibility in Blowing Snow. Transportation Research Record: Journal of the Transportation
Research Board, 2009. No.2017: pp. 143-149.