Conference Paper

The See-Through System: From Implementation to Test-Drive

DOI: 10.1109/VNC.2012.6407443 Conference: 2012 IEEE Vehicular Networking Conference (VNC) (VNC 2012)
Cooperative awereness in vehicular networks is probably the killer application for vehicle-to-vehicle (V2V) communications that cannot be matched by infrastructure-based alternatives even when disregarding communication costs. New and improved driver assistance systems can be introduced by extending their reach to sensors residing in neighboring vehicles, such as windshield-installed cameras. In previous work, we defined theoretical foundations for a driver assistance system that leverages on V2V communication and windshield-installed cameras to transform vision-obstructing vehicles into transparent tubular objects. We now present an implementation of the actual see-through system (STS), where we combine the communication aspects with the control and augmented reality components of the system. We present a validation methodology and test the system with multiple vehicles on closed road segment. This evaluation shows that the STS is able to increase the visibility of drivers intending to overtake, thus increasing the safety of such critical maneuvers. It also shows that Dedicated Short Range Communication (DSRC) provides the required latency for this delay-critical inter-vehicle communication, which could hardly be guaranteed with infrastructure-based communication technologies.


Available from: Pedro Gomes, May 06, 2014
The See-Through System: From Implementation to
Pedro Gomes, Fausto Vieira, Michel Ferreira
Instituto de Telecomunicac¸
DCC, Faculdade de Ci
encias da Universidade do Porto,
Rua Campo Alegre, 1021/1055, 4169-007 Porto, Portugal
Email: {prg, fvieira, michel}
Abstract—Cooperative awareness in vehicular networks is
probably the killer application for vehicle-to-vehicle (V2V) com-
munications that cannot be matched by infrastructure-based
alternatives even when disregarding communication costs. New
and improved driver assistance systems can be introduced by
extending their reach to sensors residing in neighboring vehicles,
such as windshield-installed cameras. In previous work, we
defined theoretical foundations for a driver assistance system
that leverages on V2V communication and windshield-installed
cameras to transform vision-obstructing vehicles into transparent
tubular objects. We now present an implementation of the actual
See-Through System (STS), where we combine the communica-
tion aspects with the control and augmented reality components
of the system. We present a validation methodology and test the
system with multiple vehicles on a closed road segment. This
evaluation shows that the STS is able to increase the visibility
of drivers intending to overtake, thus increasing the safety of
such critical maneuvers. It also shows that Dedicated Short
Range Communication (DSRC) provides the required latency
for this delay-critical inter-vehicle communication, which could
hardly be guaranteed with infrastructure-based communication
Index Terms—Cooperative advanced driver assistance systems,
V2V communication, V2V video-streaming, augmented reality
Road traffic injuries are usually tolerated as an inherent
risk of driving, even though road traffic crashes caused over
1.27 million deaths in 2004 [1]. This problem is not confined
to developed countries but it rather has become a global
health and development problem of epidemic proportions. In
the United States (US), the Fatal Analysis Reporting System
(FARS) provides a breakdown of accidents, where types of
crashes and fatalities can be analysed. There were 3,986 fatal
head-on crashes in 2003, killing 5,063 people [2], which
almost guarantees that 2 persons will die from every head-
on crash. The FARS indicates that the vast majority of these
crashes occurs on rural, undivided, two-lane roads, which is
to be expected since urban scenarios do not usually provide
scenarios for head-on crashes but rather side crashes. These
head-on crashes are the result of either deliberate an action
such as executing a passing maneuver [3] or an inadvertent ac-
tion causing a run-off-road. The latter is already addressed by
modern driver assistance technologies such as Lane-Keeping-
System (LKS), which is an efficient approach to mitigate the
This work was supported in part by the Fundac¸
ao para a Ci
encia e
Tecnologia (FCT), under the projects DRIVE-IN (CMU-PT/NGN/0052/2008)
and VTL (PTDC/EIAC-CCO/118114/2010).
head-on crashes caused by the inadvertent actions of drivers.
However, regarding passing maneuvers there are no available
systems that help drivers on the decision of whether it is
safe to engage on such maneuvers. A passing maneuver is a
dynamic situation that provides a difficult safety assessment,
particularly when the vehicle in front has no transparent
surfaces to allow the driver to see through it and perceive
incoming traffic. Trucks and buses are especially hazardous to
overtake since they constitute large vision blocking surfaces
besides presenting a length that increases even more the risk
in overtaking them.
We present an implementation of a cooperative driver as-
sistance system for the passing of vision-obstructing vehicles
which combines several technologies that are available for
next-generation vehicles, namely windshield cameras, com-
puter vision, vehicle-to-vehicle communication and visual
information projection onto windshields. The concept was
initially proposed in [4] and the original idea was based on
a common situation where truck drivers can signal vehicles
travelling behind when it is safe to overtake them. From
this basic concept of relying on technology rather than a
leap-of-faith, the system was further developed in [5] by
being able to transform large and vision-blocking vehicles into
transparent objects that simplify the driver’s task of evaluating
the safety of a passing maneuver. Dedicated Short Range
Communication (DSRC) provides the required low latency for
this delay-critical video-streaming application that otherwise
would not be possible with other vehicle-to-vehicle (V2V)
communication technologies.
We first introduce the augmented reality in vehicular envi-
ronment and then describe the system, in terms of its architec-
ture and communication protocols, its hardware and software
components and especially its computer vision blocks. We then
present the experimentation setup and the system validation
methodology. Finally, we present the evaluation results in a
real world scenario and the conclusions from this work.
Modern cars are already converging to the concept of a
virtualized windshield. A basic approach is found in the
replication of roadside traffic signs into in-vehicle virtual
traffic signs, either projected on the windshield or displayed
on LCD screens in the dashboard.
Page 1
The earliest example of in-vehicle road signs only appeared
in the 1990s with the introduction of GPS-based navigation
systems. Digital road maps that powered such navigation de-
vices included information about the speed limit of each road,
which was displayed as a digital, in-vehicle traffic sign on the
screen of the navigation device. The in-vehicle representation
and the awareness of the speed sign also allowed to check the
current speed of the vehicle against the enforced speed limit,
warning the driver about the violation. More sophisticated
in-vehicle representations of roadside traffic signs resort to
vehicular sensors other than the GPS and the associated digital
cartography. For example, in-vehicle radar-based systems are
able to determine the distance to the preceding vehicle and
warn the driver if the 2-second distance rule is violated. The
ubiquity and speed-awareness of the in-vehicle approach has
obvious advantages compared to the traditional representation.
Windshield camera-based systems are another novel trend for
the in-vehicle display of traffic signs. Such systems replace the
vision sense of the driver using computer vision techniques
that are able to recognize roadside traffic signs and automati-
cally duplicate them on in-vehicle displays. Compared to map-
based systems, the computer vision approach is able to detect
transitory changes on the posted speed due to, for instance,
temporary road works. Computer vision in the context of
vehicles has been a very active topic in ITS for the last
two decades [6]. Most of the in-vehicle displaying systems
for traffic signs described above are merely duplicating traffic
information found on existing road signs. A recent proposal for
virtualized in-vehicle traffic signs has been presented in [7], in
the context of intelligent intersection control based on virtual
traffic lights, solely supported by V2V communications. The
basic principle is replacing physical roadside infrastructures by
a temporary and virtualized infrastructure that is implemented
by a stopped vehicle at the intersection. Such leading vehicle
assumes the task of creating a virtual traffic light and broad-
casts traffic control messages to the vehicles in the different
approaches of the intersection. An interface that uses the
windshield to display such virtual traffic lights was introduced
in [8].
Another area where the virtualization of the windshield can
already be observed is that of navigational information. In
Figure 1 we display three examples of the evolution of GPS
navigators. From the traditional portable navigation devices
(PND) shown in frame A, the displaying of navigational
information has evolved to become more embedded on the
windshield, as displayed in frame C, which shows the naviga-
tional output of the new Series 5 BMW. An intermediate step is
shown in frame B, displaying the innovative Blaupunkt Travel
Pilot, which merges a video stream captured by a forward
facing camera on the device with pictographic content created
digitally, conveying navigational instruction in an augmented
reality fashion.
We introduced a new paradigm in the virtualization of the
windshield in [5], an overtaking assistance system that super-
imposes the video streaming that comes from the preceding
vehicle on its rear by projecting it on the windshield.
Fig. 1. GPS navigators.
The STS is a real-time system for providing a cooperative
video-based Advanced Driver Assistance System (ADAS).
Therefore, the system design was driven by safety require-
ments. In this section we present the system architecture and
all the different elements and protocols that were implemented
in the STS system.
Fig. 2. STS Architecture.
A. Architecture and protocols
The STS system is comprised of three main subsystems:
the unidirectional video-streaming chain, the bi-directional
control module and the computer vision and human-machine
interfaces. The Figure 2 shows the architecture of the STS.
1) Unidirectional video-streaming: The video-streaming
chain was designed to provide low latency video streaming
based on DSRC communications. Therefore, we employ the
Smoke video codec that is a low latency video codec. This
codec is a plug-in of the Gstreamer Linux framework [9].
The video-streaming packets must be carried in a typical
RTP/UDP/IP real-time protocol stack. The Real Time Protocol
(RTP) introduces timestamps that ensure timeliness of the
packets. Packets that arrive beyond the delay threshold can
Page 2
simply be discarded without having to be processed by the
higher layers. The packets are transported over UDP/IPv6 that
simply is an unreliable connectionless protocol, which has
a very low overhead and does not require establishing any
prior connection. The DSRC radios are equipped with a single
antenna WAVE/802.11p and implement the IPv6/802.11p dual-
protocol stack. The latter carries the video-streaming over one
of the several 802.11p 10MHz service channels.
2) Bi-directional control module: This subsystem provides
the cooperative capabilities of this ADAS system, since it
is responsible for establishing the connection, monitoring the
operations and assuring the safety of the takeover maneuver.
The control module receives the different inputs from the
vehicle sensors, the geographical and car awereness from the
DSRC radio and the visual awareness from the windshield
camera. After determining that all safety requirements are
met, it initiates a connection with the vehicle in front and
negotiates the establishment of the video-streaming connec-
tion. The vehicle in front transmits static information on the
vehicle characteristics, e.g. vehicle dimensions, and but also
information on the vehicle dynamics, e.g., speed, braking,
acceleration. It also monitors the reliability and the timestamps
of the received packets and disengages the system if the safety
requirements are not met.
3) Computer vision and human-machine interfaces: These
interfaces are constituted by the following elements: front-
facing windshield camera, transparent LCD monitor, dash-
board interfaces. The camera has two roles: provide road signs
detection; visually detect the edges of the preceding vehicle.
The computer vision modules process the visual information
and cue the control module relative to the detected road
signs, such as speed limits or overtaking forbidden signs.
Furthermore, this module also uses the vehicle dimensions
information provided by the control module to match these
to the image obtained with the camera in order to detect
the vehicle edges. It also calculates the 3D-looking frame
that provides the depth perception that matches the driver’s
perspective with the camera of the vehicle in front. The
incoming vehicle detection is also part of the computer vision
module. Optimally, this should be located in the vehicle in
front, before the video encoding in order to minimize the
image noise levels. This information would be streamed in
parallel with the video. The transparent LCD monitor is
mounted on the windshield, allowing the video streaming
image to be correctly superimposed on the driver’s field-of-
view, as well as the visual bracketting information of incoming
vehicles. Finally, the dashboard interfaces allows the driver to
activate the STS, which could be a dedicated or multi-function
button on the steering wheel. Alternatively, it could also be
activated with a set of conditions, including the activation of
turn signal lights.
B. Communication Protocol
The STS communication protocol is described in the Fig. 3.
The flowchart covers all the phases of the interaction between
vehicles during the overtaking maneuver.
The two vehicles periodically send beacons which typically
carry information with the location, heading and speed of the
Fig. 3. Flowchart describing the communication protocol between vehicles
A and B.
beaconing vehicle.
If the vehicle intending to overtake (A in Fig. 3) receives
the “STS enabled” beacon and is within a pre-defined distance
of vehicle B (for this implementation, 50 meters), then a
“STS Available” sign is displayed to its driver. If the driver
decides to activate the STS system, the cooperative protocol
between the two vehicles is initiated, with vehicle A asking
vehicle B for its relevant dimensions (length, height and width)
and camera parameters (mounting point and viewing angles).
Vehicle B retrieves this data to vehicle A, which then computes
the best video resolution, accounting as well with the distance
to vehicle B.
In this implementation, the video streaming is based on two
levels of resolution which are associated with two distance
intervals. The degradation of the wireless links performance as
a function of the inter-vehicle distance is thus balanced through
a reduction on the bandwidth requirements for streaming video
with a resolution that reduces as the inter-vehicle distance in-
creases. Vehicle A then asks vehicle B for the video streaming
with a specific resolution and updates this resolution request
if the inter-vehicle distance changes to a different interval.
During this stage of the STS protocol, vehicle B just sends
the video streaming with the required resolution.
Page 3
The STS communication protocol can be automatically
terminated, based on the relative position and heading of the
vehicles, or manually deactivated by the driver of vehicle A.
C. Hardware
The hardware used in the implementation of the STS
2 vehicles
2 laptops
2 GPS receivers
2 DSRC radios
2 high gain antennas Mobile Mark ECOM6-5500 (5-
2 high-resolution Logitech C270 webcams
1 Samsung Transparent LCD
Each vehicle is equipped with a laptop running Ubuntu
Linux 12.04, a high-resolution webcam, a GPS receiver and
a DSRC radio equipped with a high gain antenna. We used
802.11p compliant radios [10]. These radios implement the
WAVE standard [11]. The overtaking vehicle is also equipped
with a transparent LCD to provide the augmented reality
apparatus needed by the STS.
The preceding vehicle must be a truck or an equivalent
vision-obstruction vehicle. A magnetic coloured board is at-
tached to the rear of this vehicle, in order to its detection by
the overtaking vehicle can be fast and accurate.
D. Software
The software implementation of the STS is designed to
deliver the user with a reliable and intuitive system. Due to
the nature of this system and its criticalness, we chose C++ as
the main programming language. Its low memory usage, speed
and the possibility to integrate with all type of frameworks,
were the main choice factors. The GStreamer framework [9]
was used to provide the real-time video streaming between
the two vehicles. Specifically, its C++ and OpenGL plugins
that make it possible to directly integrate with the rest of the
1) Computer Vision: Computer vision allow to correctly
super-impose the video-streaming that comes from the in
front vehicle over its rear. We employed the Open Source
Computer Vision Library (OpenCV) [12], which provides all
the functions needed to perform the vehicle detection and aims
to real-time detection. As the STS needs to quickly detect the
magnetic board attached to the preceding vehicle’s rear, the
segmentation technique is used.
Generally, the detection is done in two steps. First, we
perform the color segmentation based on a predefined color
range. Figure 4 displays the result of applying this technique
to the frame captured by the webcam placed on the overtaking
vehicle’s windshield. The white area represents the coloured
areas that are within the predefined color range. Second, we
detect the contours that the resulted image has, and we test if
those contours match a rectangle. If a match is found, those
contours are used as bounds of the 3D-looking image with the
video-streaming embedded.
Fig. 4. Image representing the color segmentation technique applied.
2) Frame Generation: The frame generation of a 3D-
looking image that merges the video with a computed frame
occurs immediately after the in front vehicle has been detected.
Several variables, such as inter-vehicle distance, inter-
vehicle angles and vehicle dimensions, are used for the com-
putation of the 3D-looking frame that will be super-imposed
over the rear of the truck seen through the windshield. The
description of these variables can be found in the Table I. The
vehicle that intends to pursue with the overtaking maneuver is
represented as vehicle A and the preceding vehicle as vehicle
The 3D-looking image grounds on two elements: the video-
streaming and the shape of the transparent tubular object. The
angles α and β allow us to compute the distance at which the
camera can see the road, e. Adding the computed e and the
length of vehicle B, l, we can generate a tubular object with
a more realistic length that reflects a more accurate distance
at which the objects are captured by the camera. We can thus
provide the driver with a better depth perception of the video-
Page 4
Variable Description
, y
) position of the driver’s eye point
, y
) position of the camera
d distance between vehicles
e distance from camera to the ground capturing point
h, l, w height, length, width of the vehicle B
α horizontal view angle of the camera of the vehicle B
β vertical view angle of the camera of the vehicle B
ω horizontal angle between the cameras positions
ρ vertical angle between the cameras positions
streaming transmitted by vehicle B. Furthermore, the image
conveyed to the driver needs not only to give a real depth
perception of the real distance of the objects in the video-
streaming, but also to exhibit possible limitations that can
arise from this system, such as the blind-spot. This limitation
emerges as a consequence of the fact that the video-streaming
only displays the view beyond the distance at which the camera
starts to capture the road.
Figure 5 shows the schematics of the computed image rep-
resenting the 3D-looking frame on which the video-streaming
is super-imposed, where we can observe two distinct visual
areas. First, the outside frame that overlays the rear of the
preceding vehicle. Second, the inside frame which reproduces
the front view of the tubular object, by rendering the video-
Using the previously calculated width/height (w
) ob-
tained by computer vision and the real width/height of the
in front vehicle (w, h), we can compute the ratio between
this values. (1) illustrates this computation. This ratio will
continuously reflect the current distance, not only to the rear
of the in front vehicle (d), but also to the camera’s ground
capturing point (d + l + e). (2) and (3) reflect the computation
of the inside frame (w
, h
). Moreover, this image changes
according to the relative position between the driver and the
camera, given by the angles ω and ρ. This will not affect the
size of both frames, it just shifts the inside frame horizontally
(ω) or vertically (ρ). However, if these angles are too wide,
the inside frame needs to be cropped in order to fit the
outside frame. Finally, we link the correspondent vertex of
each frame, resulting in a 3D-looking image that displays the
video-streaming of the preceding vehicle with a realistic depth
r =
(d + l + e) r
(d + l + e) r
A. Experimentation setup
As the STS is focused on providing a video-based ADAS
that could cause life-threatening situations, we chose to held
it in a closed road in order to ensure safety while doing the
Fig. 5. Schematics of the computed image representing the 3D-looking frame
on which the video-streaming is super-imposed.
experiment. This road was similar to the country roads for
which this system is primarily designed to.
For creating the scenarios that make possible to validate
this system, several vehicles were used to simulate the normal
traffic that we can find in a normal situation in this type
of roads. During the experiment, the distance between the
overtaking and preceding vehicles was kept within a 50 meters
range. Considering the link quality between the radios, the
video resolution was adjusted to ensure that all the video
frames were displayed in the overtaking vehicle.
All the experimentation setup parameters are described in
Table II.
Scenario Setup
Road Topology Two-way road
Lanes Single Lane
Road Scenario Country road
Distance between vehicles 50 meters
Legal speed limit 90 Km/h
Preceding Vehicle
Model Volkswagen Transporter
Length 529 cm
Width 190 cm
Height 199 cm
Video Codec smoke
Video Resolution 640x480/340x240
Frames per second 30
Frame Display Frequency 30 Hz
Vehicle Detection Frequency 20 Hz
Position Update Frequency 1 Hz
Page 5
B. Validation methodology
This experimentation setup was designed not only to
demonstrate the implementation of the STS but also to validate
it as an efficient and reliable system. Therefore, the validation
methodology focuses on achieving two goals: show that the
Quality-of-Experience (QoE) of the video-streaming meets the
safety requirements in terms of end-to-end delay and image
quality as perceived by the driver; show that the blind spot vi-
sual representation corresponds to its physical characteristics.
For the first goal, we analyze the different contributions to
the end-to-end delay. Furthermore, we insert visual timestamps
into the video-stream and compare them in a split screen
image. We also analyze the video capture delay by recording
the image of the timestamp clock and comparing it with the
raw stream obtained from the camera. With this methodology,
we also extend it to analyze the encoding and decoding delay
of the codec, by providing a local playback of the encoded
For the second goal, we analyze a static and a dynamic
scenario. In the static scenario, we position the vehicles on
the road segment and take different photographs and screen
captures in order to guarantee that the blind spot representation
is accurate. In the dynamic scenario, we use an incoming
vehicle to pass at different speeds, while recording the received
video-stream as well as a video capture from the driver’s point-
of-view. Both video-streaming have timestamps in order to
measure the time that it takes an incoming vehicle to cross
the blind spot. The absolute speed of the incoming vehicle
must be high in order to obtain a realistic relative speed of
traffic travelling in opposite road lanes.
Finally, we test the system as operating in a normal road
scenario and obtain the different analytical measurements as
well as personal experience from using this system while
A. Expected results
The expected results in terms of delay depend on the
individual delay contributions associated to the application and
network layers.
1) Application layer delay: The STS consists of several
modules in which each one introduces a delay that tends to
be constant. This was considered during the implementation
phase. Nevertheless, both hardware and software where the
STS runs will also affect the application layer delay. The frame
capture delay depends on the webcam used, on the capacity of
the operating system to process the raw data that comes from
the device, and the performance of the library used. This delay
tends to overcome the frame display delay, which depends on
the frame rate of both application and monitor. Furthermore,
the vehicle detection introduces a negligible delay and the
application was designed to perform the detection in parallel
with the frame display.
Considering the overall delay, the application layer delay
will be substantial, though this can be easily improved in the
future with the hardware and software evolution.
2) Network delay: The WAVE/802.11p protocol stack in-
cludes the Enhanced Distributed Channel Access (EDCA) that
provides a probabilistic mechanism for traffic prioritization
in terms of channel access. This allows for the transmission
of high-priority traffic such as real-time video-streaming in
the presence of low-priority traffic. The stack also defines a
Service Channel (SCH) and a Control Channel (CCH), where
the latter must be monitored by all devices for exchanging
safety-related data. In order to support single radio DSRC
devices, it is mandatory to support channel switching between
CCH and SCH, with sinchronization provided by the UTC
clock from the GPS signal.
Therefore, the network delay will be mostly due to channel
switching, usually defined in 50ms time-slots, which bounds
the channel access delay to 50-60ms, when considering all
the guard times and different transmission rates. However,
this assumes that all the packet queueing and scheduling
mechanisms are properly implemented and that there are no
other high-priority data transmissions.
B. Obtained results
The obtained results include the delay of both application
and network layers, and the real implementation of the STS.
1) Delay: During the experimentation, the 802.11p radio
devices provided a low-delay and stable transmission. The
results in the Figure 6 show that we obtained a network
delay of an average of 65ms during the STS activation range,
50 meters. The overall delay of the STS also comprises the
application layer delay. The process of capturing a single video
frame from the webcam, encoding, decoding and display such
frame takes aproximately 100ms. Adding the frame display
frequency (see Table II), the delay increases to 133ms. As we
expected, the delay of the application layer was significant.
Combining both network and application delays we have a
delay near to 200ms. Nevertheless, in the worst scenario
with a combined velocity of 180Km/h, the overall delay will
create a visual gap of 10 meters, which for the STS is almost
Fig. 6. Delay versus distance.
Page 6
Fig. 7. STS being activated in a country road scenario. The generated
frame, containing the video-streaming, super-imposes the rear of the preceding
2) STS Implementation: The final result of the STS imple-
mentation is shown in the Figure 7. This image illustrates the
process of super-imposing, on the rear of the in front vehicle,
the video streaming that it transmits. We can observe the video
streaming embedded in the frame generated using the method
described in Section III-D2. The transparent LCD provided
the best apparatus for implementing the STS. However, due
to this LCD being a prototype, it lacks the ability to completely
block the background, which results in a magenta tinted image
as a result of the coloured panel attached to the rear of the
preceding vehicle. Thus, the coloured background that appears
on the image in the Fig. 7.
The STS demands that the video streaming is real-time and
that its quality provides the driver a perfect representation
of what the preceding vehicle sees. The smoke codec of
the Gstreamer framework allowed us to transmit in real-time
without almost any delay (see Section V-B1). We analysed
the quality of the frames received in the overtaking vehicle to
ensure that the video streaming is up to the demands of this
system. The peak signal-to-noise ratio (PSNR) is a simple
analytical method for measuring the video quality, which
provides a basic understanding of the QoE especially when
the main focus in on measuring the quality in a wireless envi-
ronment. Figure 8 shows the computed PSNR values between
the captured and displayed video sequences. During the first 40
meters, these video sequences were almost undistinguishable
(PSNR greater than 36 dB). And, between 40 and 50 meters
the PSNR was above 30 dB, which corresponds to acceptable
visual quality.
Another issue addressed in the STS implementation was
the evaluation of the blind spot that this system has. These
blind spots are specially significant for long vehicles. Due to
logistics, we were not able to perform the experimentation with
such long vehicles. Hence, it was used a Volkswagen Trans-
porter, which is a much smaller vision-obstruction equivalent.
We created a scenario with a vision-obstruction vehicle, an
overtaking vehicle and an oncoming vehicle on the opposite
lane. The objective was to observe and detect the possibility
of a blind spot in the STS. Considering that the blind spot
occurs mostly when the distance between vehicles is reduced,
the overtaking vehicle was placed a few meters behind the
preceding vehicle. In the Figure 9, we can depict a small blind
spot, where the oncoming vehicle does not appear on the video
Fig. 8. PSNR versus distance.
streaming, and we can only see part of it. This blind spot
would be more significant, if a longer vehicle was used as
the preceding vehicle. With such vehicle, for instance a semi-
trailer truck, we would not be able to see the oncoming vehicle
both on the video streaming and the point of view of the driver.
Fig. 9. Blindspot generated by the STS.
The implementation and testing of the STS in a realistic
environment showed that this cooperative ADAS system per-
forms as expected and it meets tight safety requirements. We
showed that the latency introduced by the system is already
quite low even when considering that several components
are software-based and were not especifically designed to
provide very low latency. We also showed that the augmented
reality aspect of the STS is indeed representative of the
overtaking scenario physical characteristics, in order to provide
an intuitive driver assistance system with an actual depiction of
the blind-spots. Finally, we are able to validate the STS and its
implementation as a valid ADAS system that takes advantage
of V2V communications to improve the safety of overtaking
maneuvers by improving the visibility in the presence of large
and vision-obstructing vehicles.
Page 7
[1] “Global status report on road safety, 2009,
[2] AASHTO, “Strategic Highway Safety Plan: A Comprehensive Plan
to Substantially Reduce Vehicle-Related Fatalities and Injuries on the
Nations Highways, American Association of State Highway and Trans-
portation Officials, Washington, DC, 2005.
[3] T. Neuman, R. Pfefer, K. Slack, K. Hardy, H. McGee, L. Prothe, K. Ec-
cles, and F. Council, “NCHRP Report 500: Guidance for Implementation
of the AASHTO Strategic Highway Safety Plan. Volume 4: A Guide for
Addressing Head-On Collisions, Transportation Research Board of the
National Academies, Washington, DC, 2003.
[4] C. Olaverri-Monreal, P. Gomes, R. Fernandes, F. Vieira, and M. Ferreira,
“The see-through system: A vanet-enabled assistant for overtaking
maneuvers, in Intelligent Vehicles Symposium (IV), 2010 IEEE, june
2010, pp. 123 –128.
[5] P. Gomes, C. Olaverri-Monreal, and M. Ferreira, “Making vehicles trans-
parent through v2v video streaming, IEEE Transactions on Intelligent
Transportation Systems, vol. 13, no. 2, p. 930, 2012.
[6] M. Bertozzi, A. Broggi, and A. Fascioli, “Vision-based intelligent
vehicles: State of the art and perspectives, Robotics and Autonomous
systems, vol. 32, no. 1, pp. 1–16, 2000.
[7] M. Ferreira, R. Fernandes, H. Conceic¸
ao, W. Viriyasitavat, and
O. Tonguz, “Self-organized traffic control,” in Proceedings of the seventh
ACM international workshop on VehiculAr InterNETworking. ACM,
2010, pp. 85–90.
[8] C. Olaverri-Monreal, P. Gomes, M. Kruger Silv
eria, and M. Ferreira, “In-
vehicle virtual traffic lights: a graphical user interface, in CISTI’2012.
IEEE, 2012.
[9] “Gstreamer open source multimedia framework,
[10] C. Ameixieira, J. Matos, R. Moreira, A. Cardote, A. Oliveira, and S. Sar-
gento, “An ieee 802.11p/wave implementation with synchronous channel
switching for seamless dual-channel access, in Vehicular Networking
Conference (VNC), 2011 IEEE, nov. 2011, pp. 214 –221.
[11] “IEEE 802.11p-2010: Wireless LAN Medium Access Control (MAC)
and Physical Layer (PHY) Specifications Amendment 6: Wireless Ac-
cess in Vehicular Environments, IEEE Standards Association, 2010. ,
[12] “Opencv - open source computer vision library,
Page 8
  • Source
    • "[6], we used a transparent LCD installed on the windshield as the HCI architecture that provides the optical AR needed to implement STS. We observed some problems using the transparent LCD technology installed on the windshield. "
    [Show abstract] [Hide abstract] ABSTRACT: The confined space of a car and the configuration of its controls and displays towards the driver, offer significant advantages for Augmented Reality (AR) systems in terms of the immersion level provided to the user. In addition, the inherent mobility and virtually unlimited power autonomy transform cars into perfect mobile computing platforms. However, the limited network connectivity that is currently available in automobiles leads to the design of Advanced Driver Assistance Systems (ADAS) that create AR objects based only on the information generated by on-board sensors, stored maps and databases, and eventually high-latency online content for Internet-enabled vehicles. By combining the new paradigm of Vehicular Ad Hoc Networking (VANET) with AR human machine interfaces, we show that it is possible to design novel cooperative ADAS, that base the creation of AR content on the information collected from neighbouring vehicles or roadside infrastructures. We provide a prototype implementation of a visual AR system that can significantly improve the driving experience.
    Full-text · Conference Paper · Oct 2013
  • Source
    • "Optical see-through AR can also be implemented through transparent Liquid Crystal Displays (LCD), embedded in the windshield. In [20] we have implemented an overtaking assistant, known as the See-Through System (STS) [19], which combines such AR technology with low-latency video streaming transmitted between two vehicles using Dedicated Short-Range Communications (DSRC). The result is the transformation of long and vision-obstructing vehicles into see-through tubular objects, which greatly facilitates the overtaking manoeuvre.Figure 1C shows a snapshot of the functioning of STS implemented using a transparent LCD. "
    [Show abstract] [Hide abstract] ABSTRACT: With the advent of vehicular communications and new developments in enhanced reality for Advanced Driver Assistance Systems (ADAS), we present in this position paper a new highway revenue model based on digital advertising super-imposed on physical billboards. We show that there is a strong correlation between economic development and an extensive road network. However, the public support for maintaining and expanding the road network is diminishing since these investments mainly rely on taxation. The common approach is to introduce tolls as a revenue generating method for supporting these investments. We argue that virtual billboards could provide an alternative revenue stream and avoid tolls in highways with high volumes of traffic. The key technologies are vehicular-to-infrastructure (V2I) communications and virtual windshields that already provide enhanced reality for ADAS. The physical billboards would transmit targeted advertising to approaching vehicles that would overlay it on top of the billboard sign. We demonstrate that it is already possible to implement this system with existing technologies. These digital ads would combine the flexibility of Internet advertisement with the high exposure of highway billboards and the willingness to receive them as an alternative to paying tolls. Finally, we show that it is viable to provide advertisement sponsored highway segments with very low cost-per-view values, especially in suburban highways that have a high volume of traffic.
    Full-text · Conference Paper · Jan 2013
  • [Show abstract] [Hide abstract] ABSTRACT: Visual obstruction caused by a preceding vehicle is one of the key factors threatening driving safety. One possible solution is to share the first-person-view of the preceding vehicle to unveil the blocked field-of-view of the following vehicle. However, the geometric inconsistency caused by the camera-eye discrepancy renders view sharing between different cars a very challenging task. In this paper, we present a first-person-perspective image rendering algorithm to solve this problem. Firstly, we contour unobstructed view as the transferred region, then by iteratively estimating local homography transformations and performing perspective-adaptive warping using the estimated transformations, we are able to locally adjust the shape of the unobstructed view so that its perspective and boundary could be matched to that of the occluded region. Thus, the composited view is seamless in both the perceived perspective and photometric appearance, creating an impression as if the preceding vehicle is transparent. Our system improves the driver's visibility and thus relieves the burden on the driver, which in turn increases comfort. We demonstrate the usability and stability of our system by performing its evaluation with several challenging data sets collected from real-world driving scenarios.
    No preview · Article · Oct 2014 · Computer Graphics Forum
Show more