1) Drivers first notice static road signs from about 110 meters away but don't begin processing the message until around 65 meters away.
2) When shown pictures of signs 32 seconds after passing them, 92% of drivers correctly identified the TW-7 sign indicating a need to change lanes.
3) Fixation durations on the TW-7 sign averaged around 400 milliseconds, consistent with previous research on how long drivers fixate on signs.
multi-view vehicle detection and tracking inAalaa Khattab
Multi-view vehicle detection and tracking in crossroads
is of fundamental importance in traffic surveillance yet
still remains a very challenging task. The view changes of
different vehicles and their occlusions in crossroads are two
main difficulties that often fail many existing methods.
LVTS - Image Resolution Monitor for Litho-MetrologyVladislav Kaplan
Significant challenges for various Critical Dimension (CD) measurement matching procedures are reaching a comparable complexity as result of negative effects of roughness on the features. Due to the constant trend of integrated circuit in features reduction, impact of roughness start to be more destructive for various sets of measurement algorithms. Commonly used attempts to increase magnification for pattern recognition in measurement mode could in turn detect higher deviation from predefined patterns and thus initiate shift in placement of measurement gate. The purpose of this paper is to discuss how to reduce measurement gate (MG) placement variation impact and filter acquired data using edge correlation approach. The essence of listed above approach is to create set of width correlation function represents particular feature under test and compare it to “golden” one as a mean of detection of uncorrelated scans, which in turn should be excluded from overall computation of matching results. We describe general approach for algorithm stepping and various techniques for judgment of measurement comparison validity. Presented approach also has particular interest in determination of specified tool performance for predefined pattern recognition feature as well as for pattern recognition algorithm robustness study - direct interest for manufacturer. Precise matching estimation as part of Round Robin (RR) routines creating possibility to work with restricted amount of data and perform quick reliable qualification procedures. This paper concentrated on practical approach and used both simulation and actual data measurements data before and after proposed optimization taken by various generation tools by Hitachi (S-8840, S-9300, S-9380) in production environment
Tteng 441 traffic engineering fall 2021 part5Wael ElDessouki
Capacity analysis and design for signalized and un-signalized intersections. Level of Service at Signalized Intersection. Traffic control delay estimation.
Transportation Engineering
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multi-view vehicle detection and tracking inAalaa Khattab
Multi-view vehicle detection and tracking in crossroads
is of fundamental importance in traffic surveillance yet
still remains a very challenging task. The view changes of
different vehicles and their occlusions in crossroads are two
main difficulties that often fail many existing methods.
LVTS - Image Resolution Monitor for Litho-MetrologyVladislav Kaplan
Significant challenges for various Critical Dimension (CD) measurement matching procedures are reaching a comparable complexity as result of negative effects of roughness on the features. Due to the constant trend of integrated circuit in features reduction, impact of roughness start to be more destructive for various sets of measurement algorithms. Commonly used attempts to increase magnification for pattern recognition in measurement mode could in turn detect higher deviation from predefined patterns and thus initiate shift in placement of measurement gate. The purpose of this paper is to discuss how to reduce measurement gate (MG) placement variation impact and filter acquired data using edge correlation approach. The essence of listed above approach is to create set of width correlation function represents particular feature under test and compare it to “golden” one as a mean of detection of uncorrelated scans, which in turn should be excluded from overall computation of matching results. We describe general approach for algorithm stepping and various techniques for judgment of measurement comparison validity. Presented approach also has particular interest in determination of specified tool performance for predefined pattern recognition feature as well as for pattern recognition algorithm robustness study - direct interest for manufacturer. Precise matching estimation as part of Round Robin (RR) routines creating possibility to work with restricted amount of data and perform quick reliable qualification procedures. This paper concentrated on practical approach and used both simulation and actual data measurements data before and after proposed optimization taken by various generation tools by Hitachi (S-8840, S-9300, S-9380) in production environment
Tteng 441 traffic engineering fall 2021 part5Wael ElDessouki
Capacity analysis and design for signalized and un-signalized intersections. Level of Service at Signalized Intersection. Traffic control delay estimation.
Transportation Engineering
Brief study on measurement of spot speed with the help of enoscope for diploma engineering students of civil engineering stream.
1. Introduction
Methodology
Conclusions
ROAD SIGN CONSPICUITY AND MEMORABILITY: WHAT WE SEE AND
REMEMBER
Urie BEZUIDENHOUT – Da Vinci Research e: urie@davincitransport.co.nz m: 021 367516
Static signage are effective in alerting drivers on the motorway to a lane closure ahead, without the need of additional
attenuator trucks.
Consistent with COPTTM, drivers first notice a static sign about 110m away, and only begin to process its message around
65m from the sign.
Total mean pursuit fixation durations on the TW-7 sign is around 400ms which is consistent with findings by Underwood et
al. (2002).
92% correctly identified the TW-7 sign to change lanes when presented with pictures of the sign from the video 32s after
passing the sign.
SC3 and 7 with lane changes attracted
more fixations in the bottom central area
SC 4 and 8 without the need for lane
changes, recorded less fixations in the
bottom parafoveal view. This is consistent
with drivers focussing their field of view to
the vanishing point
Analysis Results
The purpose of this experiment was to evaluate how drivers would respond to the use of static TW-7 signage without preceding
attenuating trucks, and to evaluate the conspicuity of the signs ,in terms of drivers cognitive awareness when approaching static
warning, temporary and regulatory signs showing a left lane closure. This static layout is proposed as an alternative to the use of
two advance warning attenuator trucks with flashing lights and keep-right sign (Figure 1 (a & b)). Eye tracking assesses where a
driver is looking in general, and specifically focusing (fixate) their eyes (Figure 1 (c)), and considering that 90% of the driver’s
perception and cognitive reaction are through the use of their visual sensory motoric system, eye gaze and fixations locations are an
important metric to determine sign perception. If humans do not fixate an object, they cannot adequately process the visual
information, and therefore cannot accurately respond to visual stimuli if drivers “do not perceive it.” This is analogous to not tasting
or feeling something, but imagining what it might taste or feel like, without actually sensing it physically.
This static sign layout was installed on SH1 between Albany and
Silverdale. Figure 2 shows the signs that were visible to the drivers
during the experiment. Successive drive-overs recorded video from
the drivers point of view from various lane positions. Video based
analysis using SAFEye methodology using an eye tracker, presented
the 4 videos to 24 subjects.
Data Collection: Dash-mounted video camera 1920 x 1080 pixels
and Mirametrix S2 eye tracker
Angle of View: The field of view spanned 45˚ (horz.) and 25˚ (vert.)
Analysis Metrics: Area of Interest (Figure 3)
First time to fixation, Fixation duration, Pursuit fixation duration
(figure 4.)
Table 1: Experimental design
TW-1 TW-7 TW-7 RG-4 RG-34
Figure 1: Gaze heat maps (a) near attenuator (b) around TW 7 sign (c) fixations overlaid on gaze heatmap
Figure 2 – Sign sequence
HuD(u)
HuD(l)
Opp-traf
Car-F
TW-71
TW-72
Figure 3: Areas of interestRecording Scenario Left sign
placement
Right sign
placement
2-lane
Left-lane 1 SC3 yes no
Right-lane 2 SC4 yes no
3-lane
Left-lane 1 SC7 yes no
Right-lane 3 SC8 yes no
Figure 5: Mean fixation hits in AoI
Perph-TL
Perph-BL
P-Foveal-
T
P-Foveal-
B
Perph-TR
Perph-
BR
Foveal
10 deg
arc
0
5
10
15
20
25
30
35
Numberoffixation
SC3_all
SC4_all
SC7_all
SC8_all
Figure 4: Pursuit fixations
Time to first Scenario Gaze (m) Fixation (m)
Lane 1 of 2 SC3 113 62
Lane 2 of 2 SC4 114 46
Lane 1 of 3 SC7 100 75
Lane 3 of 3 SC8 119 76
Mean
distance
111 65
The mean distance from the TW-7 sign to the
first gaze or fixation is 111m and 65m
respectively. (Table 2)
These findings are consistent with the way
drivers detect signage by first noticing, but not
necessarily processing a sign message,
around the 110 m mark, and then processing
the detail around 60 m or 2 seconds later.
The majority of subjects (88-100%) gazed on the first TW-7 sign with
little difference between scenarios. (Table 6)
Fixations for SC3 and 7, which drivers have to change lanes due to
the closure, only 54% and 79% respectively fixated the first TW-7(1)
sign, but this increased to 71% and 96% respectively by the second
TW-7(2) sign.
Lane and
position
Scenario Time to first gaze
or fixation
TW-71 (TW-72)
Individual mean
fixation duration
(ms)
Total fixation
dwell time
on sign
(ms)
Gaze Fixation
Lane 1 of 2 SC3 100% 54%
(71%)
155 360
Lane 2 of 2 SC4 92% 38% 148 380
Lane 1 of 3 SC7 96% 79%
(96%)
149 450
Lane 3 of 3 SC8 88% 46% 174 390
Table 2: Mean distance from sign
Table3: Percentage that fixated the TW7 sign
Lane Merge
Left
Road
Narrows Left
200m
TW-7 Lane
Closed
200m
Merging
Traffic 200m
RG-4
Temporary
Speed Limit
TW-1 Work
Zone Speed
Limit
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Which signs do you recognise?
As a measure of explicit memory, subjects selected from a list
of seven signs which signs (Figure 6) they recognised from the
video sequence. Red bars indicate a sign that was absent from
the video, whereas the blue bars indicate signs that were
present in the video.
Figure 6: Sign recognition- Explicit memory