1. Introduction
With the recent advances in solid-state lighting technology, light emitting diodes
(LEDs) offer greater energy efficiency, a longer lifespan, more durability, and excellent
illumination quality compared to conventional incandescent lamps. Due to these exclusive
properties, LEDs are used for lighting purposes almost everywhere. Additionally, they
are switchable at various light intensity levels at a very fast rate, and thus, data
can be encoded using an LED driver and can be used for information transmission. Recently,
visible light communication (VLC) [1-3] that allows simultaneous communication between any two devices equipped with LEDs
and a photodetector is widely addressed in various research areas.
VLC can achieve data rates up to several gigabits per second, which is comparatively
faster than conventional radio frequency (RF) communications. Although there are several
approaches to efficient resource allocation and reuse of the available RF spectrum,
its limited availability makes it difficult to meet the increasing demands of cellular
traffic in today’s world. Therefore, VLC can be a viable solution to overcome the
drawbacks of RF communication and designing high-capacity wireless networks.
Optical camera communication (OCC) [4,5] is a subset of VLC that operates in the visible light frequency band with an LED
and a camera acting as a transceiver pair. To establish the communication link, OCC
technology can utilize readily available LED lights with off-the-shelf cameras, digital
cameras, rear cameras in vehicles, surveillance cameras, etc. Because OCC is feasible
for a large body of applications, it can be applied to the Internet of Things (IoT),
to indoor localization, and to vehicular communication in an intelligent transport
system (ITS). In an OCC system, information is sent from the transmitter by varying
the intensity of the LED. Likewise, the image sensor used at the receiver captures
the LED state and position, thereby decoding the data from multiple-pixel information
in the captured image.
The massive number of road accidents occurring every day costs thousands of lives,
mostly due to irrelevant and misunderstood information between vehicles. Therefore,
to diminish the number of casualties, there is increasing demand for efficient and
relevant communication between vehicles and road-side units (RSUs) such as digital
signage and display boards. Several vehicular communication technologies, including
conventional RF [6,7] and vehicular ad-hoc networks (VANETs) [8], are widely considered in the ITS field. However, they cannot provide robust solutions
to several complications, such as quality of service (QoS), high connectivity, and
high-bandwidth requirements. Besides, vehicular communication using RF technology
is not feasible in some places, including electromagnetically sensitive areas and
certain architectures, such as tunnels and underground roads.
OCC-based ITS [9,10] takes advantage of LEDs and cameras equipped in both vehicles and road infrastructures.
The headlights and rear lights of a vehicle can be used as data transmitting devices.
Traffic lights and digital signage on the roads can also broadcast OCC data containing
ITS information via the visible light spectrum. Dashboard cameras and surround-view
cameras mounted on vehicles can be used to receive the transmitted OCC data. Likewise,
surveillance cameras used for license plate recognition and lane detection also serve
as a receiver for OCC data. Considering the easy availability of transmitter/receiver
pairs, it is efficient to implement an OCC system for vehicular communications. Besides,
the installation cost is comparatively less than RF, and the OCC system utilizes 300
THz of license-free bandwidth with no network usage cost. This makes a vehicular communication
system with OCC much more reliable, easier, and efficient to implement in the real-world
ITS scenario.
Several challenges must be solved for OCC technology to be used in vehicular communication.
To establish an OCC link, visible lights must be within the field of view (FOV) of
the receiver's camera, and depend not only on the lens but also on the sensor. Visual
information challenges, including the impact of natural conditions, such as snow and
rain [11,12], smoke and fog [13,14], in the optical channel have a negative impact on the performance of a vehicular
communication system. Despite these challenges, OCC-based vehicle communication is
expected to be in high demand, because it reduces the cost of establishing a ubiquitous
and stable communication system for an ITS.
In OCC-based ITS technologies, vehicle-to-vehicle (V2V) communication [15-20] considers information transmission from one vehicle to a nearby following vehicle.
Similarly, in vehicle-to-infrastructure (V2I) communication [10, 21-27, 35], RSUs
and LED traffic lights transmit information to an onboard receiver camera equipped
on a vehicle. In vehicle-to-everything (V2X) communication [28-33], the vehicle communicates with every digital entity with a camera, such as drones
and pedestrians wearing smart glasses.
In this paper, we summarize the recent vehicular communication technologies using
OCC. The contributions of our paper are as follows.
· We explain the various types of recent technologies explored in the OCC-based
vehicular communication model.
· We explore the various OCC system designs and the impact of the transmission
channel on system performance.
The rest of the paper is organized as follows. In Section 2, the system model
for OCC-based vehicular communication is discussed. Section 3 presents various types
of vehicular communication technologies that use an LED and a camera for data communication.
In Section 4, we discuss the channel characteristics and design challenges of the
system. Finally, we present some suggested future work in Section 5, followed by the
conclusion in Section 6.
2. System Model
The concept of OCC-based vehicular communication is illustrated in Fig. 1. The candidate for the transmitter can be traffic LED lights, vehicle tail lights,
or any commercial digital display unit on the road. Likewise, the receiver can be
a dashboard camera mounted on the front of the vehicle. In V2V communication, vehicles
exchange driving information like speed, braking conditions, and upcoming traffic
information. Likewise, in V2I communication, roadside infrastructure with LEDs, such
as traffic lights and digital signage, transmit road-state information, such as public
broadcasts and accident warning messages, to vehicles nearby. And, in the V2X scenario,
the vehicle communicates with every digital entity in the surrounding area to send
and acquire road safety information.
The system architecture for OCC-based vehicular communication is presented in
Fig. 2. The input data to be transmitted are in binary form, are subjected to channel coding,
and are followed by modulation to convert them into optical signals. The optical signals
are transmitted from vehicular LEDs, and the visible light spectrum of license-free
bandwidth carries OCC information with no network usage costs. When the transmitted
signal passes through the optical channel, several types of noise occur, such as shot
noise from photon emissions of the LEDs and additive white Gaussian noise (AWGN).
Besides, various natural conditions, such as smoke, fog, and rain, impair the transmitted
OCC signal. At the receiver, the image region that includes the OCC signal is extracted
from the received image through image processing. Then, the received signal is demodulated,
followed by channel decoding to retrieve the originally transmitted binary data.
Fig. 1. Vehicular communication using OCC.
Fig. 2. The system architecture of OCC technology.
3. Intelligent Transport Systems (ITS) using OCC
The concept of the ITS is to introduce a cooperative driving environment with
exchanges of many kinds of vehicular and roadside information via wireless channels
between vehicles and the nearby digital infrastructure. The goal of this system is
to provide real-time interaction with drivers, providing information from surrounding
RSUs to solve traffic congestion problems, reduce road fatalities, and increase driving
safety.
For efficient management of the road transportation system, RF-based communication
technologies such as dedicated short-range communication (DSRC), wireless access in
vehicular environments (WAVE), and continuous air interface, long and medium range
(CALM), now referred to as communications access for land mobiles, have been considered.
In [6], state-of-the-art VANET technology was discussed, including the detailed architecture
and network topology modeling for an ITS. Here, various routing protocols that can
withstand the variable and dynamic VANET topology were discussed. Similarly, Kosmanos
et al. [7] proposed the relative speed estimation algorithm (RSEA) for a moving vehicle that
approaches a transmitter/receiver pair. However, these RF-based approaches lead to
problems related to spectrum scarcity, and they result in high computational costs,
signal latency in a dense crowd, and signal degradation due to multipath propagation,
which are severe detriments.
Given the limitations of RF technology and the associated inconvenience in congested
traffic scenarios, OCC systems can serve as a viable solution to overcome these shortcomings
of the ITS. Using the vast and freely available visible light spectrum, an OCC-based
ITS solves the problem of channel congestion, and allows handling multiple users at
the same time.
There are various scenarios for vehicular communication in an ITS, where vehicles
communicate with each other and with RSUs. In V2V communication, only the vehicles
communicate with each other to share useful traffic information. In V2I, communications
are held between vehicles and the road infrastructure. Similarly, in V2X scenarios,
vehicles exchange information between, for example, pedestrians wearing smart glasses,
bicycles, and unmanned aerial vehicles (UAVs).
3.1 Vehicle-to-vehicle
In an OCC-based V2V communication system, information is exchanged between any
two vehicles, where the preceding vehicle transmits information from its LED rear
lights, and the following vehicle receives that information with an onboard camera.
The common types of information usually shared in V2V communication scenarios are
speed, braking conditions, lane transfer information, etc. In V2V communication, the
two vehicles communicating with each other should be within range, which is determined
by the type of camera and the illumination properties of the LED light source. Fig. 3 presents a diagram of a typical V2V scenario where three vehicles on the road communicate
with each other. The two preceding vehicles, B and C, with different field-of-view
(FOV) angles ${\theta}$$_{1}$ and ${\theta}$$_{2}$ (where, ${\theta}$$_{2}$> ${\theta}$$_{1}$),
are at distance $\textit{d}$$_{1}$ and $\textit{d}$$_{2}$ ($\textit{d}$$_{1}$ < $\textit{d}$$_{2}$),
respectively, from the following vehicle, A. Although vehicle C is at shorter distance
from vehicle A, its communication performance is degraded slightly due to the small
FOV angle. And although vehicles A and B are in good FOV alignment, their performance
is altered due to the long transmission distance. Therefore, in an OCC-based V2V environment,
angular alignment and communication distance between the following and preceding vehicles
are crucial factors to be considered for robust communication performance.
In [15], a real-world driving experiment was conducted using OCC, where two vehicles were
driven a total of 108~km. By utilizing different software and hardware techniques
with orthogonal frequency division multiplexing (OFDM), the proposed system reliably
achieved a working range of 45 meters. Through extensive experiments, the study showed
that multipath propagation had little effect on error performance. Instead, the transmission
distance and angle were the main factors determining received power.
A low-complexity and high-reliability prototype for vehicular communication was
designed where the receiver in the following vehicle can detect hard braking of a
preceding vehicle from a distance of 20 meters [16]. Visible light signals from a brake light are used when emergency messages for hard
braking need to be transmitted. Whenever hard braking is detected, the system immediately
notifies a vehicle following at speeds of up to 80 km/h, and reduces the chance of
an accident.
In real-world vehicular communication, vehicles might suffer dynamic and uneven
motion due to erratic road conditions. This might adversely affect the overall communication
performance of the V2V scenario. To examine this issue, Ashraf et al. [17] presented a mobility characterization study, where they set up a V2V communication
scenario using a constantly illuminated transmitter on the preceding vehicle and a
multi-camera setup on the following vehicle. Through extensive analysis of the experimental
data, they found that the typical range of horizontal and vertical motion of vehicles
is on the order of 40 pixels in a 1920 ${\times}$ 1080 resolution video at 30 frames
per second (fps).
In [18], a mirrorless, vehicle-based autonomous driving concept for V2V communication was
investigated. In this scenario, the camera sensor is located in the rear-view mirror
in the leading vehicle and can provide a much broader view for improving driving safety.
Here, a flicker-free LED was used in the following vehicle for simultaneous illumination
and data transmission. With its headlight LEDs, the following vehicle can transmit
future-action data, such as overtaking, cutting in, caution signals, and vehicle type.
The lead vehicle receives the OCC data by using high-speed cameras, and can determine
the safest driving route depending on the future-action data transmitted from the
following vehicle. With extensive experiments, the proposed system proved to have
robust V2V communication performance during both daytime and night-time.
In [19], an optical V2V communication system where the receiver camera employed a special
complementary metal oxide semiconductor (CMOS) image sensor called an optical communication
image (OCI) sensor was introduced, that consists of pixel arrays suitable for imaging
and communication [26]. By using the OCI-based camera, the receiver obtained a 10~Mbps optical signal, and
was capable of accurately detecting the LEDs using photodetector (PD) pixels. Also,
they achieved real-time LED detection in outdoor lighting conditions by using a flag
image for LED detection. The use of a flag image effectively eliminates unnecessary
objects in the captured image. Likewise, in [20], the authors presented test results for OCC-based V2V and V2I communication. Flicker-free
LEDs were used as light-emitting sources, with high-speed cameras at the receiver.
In real-world testing, they obtained successful reception of absolute location data
sent by the tail lights of the forward vehicle.
Fig. 3. Vehicle-to-vehicle communication.
3.2 Vehicle-to-infrastructure
In V2I communication, vehicles communicate with nearby infrastructure to exchange
information. In OCC-based V2I communication, the transmitter candidates can be vehicular
LED lights, digital signage along the road, LED traffic lights, digital lane markings,
street lamps, parking meters, digital milestones, etc. Receivers, on the other hand,
are cameras installed on the dashboard of the vehicle, surveillance cameras, and special-purpose
cameras for lane tracking or license plate number detection.
In either V2I or infrastructure-to-vehicle (I2V) communication, information such
as public safety messages about construction work ahead, emergency weather alerts,
availability of parking spaces, road accident warnings, and vehicular traffic information
are transmitted. The vast number of RSUs with LEDs for displaying vehicular safety
information can serve as OCC transmitters.
Fig. 4 shows an OCC-based V2I communication scenario where three vehicles are communicating
independently with digital signage and traffic lights on either side of the road.
Real-time communication is influenced by various factors, such as vehicular speed,
angle between transmitter and receiver, and the range between them. The speed at which
a vehicle is approaching a digital RSU also determines performance in OCC-based V2I
communication. For a slow vehicle approaching the receiver, it is relatively easy
to capture the LED lights of the transmitter. However, for a high-speed vehicle, a
high-speed camera capable of capturing multiple frames per second is required to effectively
capture the transmitted content.
In [10], a V2I broadcast system that integrates traffic lights with the existing ITS architecture
was presented. Here, a prototype with a 200 mm custom-designed LED traffic lighting
system was used. A robust modulation technique based on direct sequence spread spectrum
(DSSS) and sequence inverse keying (SIK) was used to minimize the effect of noise.
They compared their scheme with existing modulation schemes, such as L pulse position
modulation (L-PPM), inverted L-PPM, and on-off keying (OOK). They found that their
scheme provided better performance than conventional ones in terms of BER and maximum
achievable data rate. Furthermore, through extensive experiments under bright sunlight
during the daytime, they demonstrated robust communication performance at a transmission
range of more than 40 m.
Note that the capture rate of the camera plays a significant role in efficient
and fast V2I communication. In general, there is a mismatch between the transmit frame
rate and the capture rate of the camera, and thus, synchronization techniques [34] should be considered for robust V2I communication. Usually, LEDs are capable of transmitting
data at a very high frame rate. Therefore, a moving vehicle in V2I communication with
a stationary infrastructure requires very high-speed cameras with high capture rates
of more than 30 fps to achieve good data transmission speed. In [21], a V2I experiment on the road was conducted to address the issue of mismatch frames
by using high frame-rate cameras at the receiver. An LED array with an emission rate
of 500 Hz was used in the transmitter. At the receiver, the emission patterns as a
receiving data were captured with an on-vehicle high-speed camera at a capture rate
of 1000 fps. The captured images were processed to find the transmitter, to track
that transmitter, and to obtain its LED lighting pattern. Extensive experiments were
conducted by increasing the transmission distance from 20 m to 70 m. For distances
as low as 20-30 m, no errors were observed. However, upon increasing the transmission
distance, an error of up to 10$^{-1}$ was incurred.
In [22], a transmitter detection algorithm was proposed for visible-light road-to-vehicle
communication. The transmitter and receiver candidates were LED traffic lights at
a crossroad and an on-vehicle high-speed camera, respectively. The frame subtraction
technique was used to detect the approximate position of the transmitter, which reduced
the data by cutting out detected areas. The exact portion of the transmitter was detected
by template matching, and data were acquired from the image. Through the experiments,
a vehicle at a speed of 30 km/h was able to correctly detect most of the frames, and
achieved good communication with high accuracy at distances of less than 35 m.
A study in [23] investigated the I2V and V2I concept for connected vehicles at road intersections
with traffic lights. They proposed a smart-vehicle lighting system capable of combining
multiple functions, such as illumination, signaling, and positioning. To transmit
data, on-off code was used where the encoded data contained a unique emitter ID. At
the receiver, the message was decoded and resent to either another vehicle or to the
traffic light within the intersection.
Similarly, in [24], a robust range estimation technique for I2V communication was presented by using
an LED array and a high-speed camera as transmitter and receiver, respectively. To
avoid vehicular vibrations from irregularities in the road, range estimation was achieved
by using the phase-only correlation (POC) technique. This technique is a pattern-matching
algorithm where the amplitude components of the Fourier-transformed images are replaced
with a constant value. They used this technique for position displacement detection
of the same object, and achieved a range accuracy of 0.3 meters with a measuring time
of two milliseconds on a rough road.
Other studies [25-27] addressed the use of OCI for V2I communication. Yamazato et al. [25] proposed an OCI-based automotive system for both V2V and V2I applications. The system
uses an LED array as a transmitter and a high frame-rate image sensor as a receiver.
The LED array used as an LED traffic light is set on horizontal ground, and that a
high-speed camera is mounted on the dashboard of the vehicle. Multiple field trials
were conducted by driving a vehicle at speeds of 30~km/h from distances of 70 m and
30 m towards the receiver. Through the experiment, a data rate of 32 kbps was achieved
for real-time audio signal transmission consisting of driving safety information.
Similarly, in [26], an OCC system was developed for automotive applications targeting both V2V and V2I
communications by using an OCI image sensor. This system incorporated the OCI chip
for high-speed signal reception and accurate LED detection. The OCI chip consists
of a specialized communication pixel, which is capable of prompt responses to variations
in optical intensity. This automotive application-friendly OCC system achieved a data
rate of 15 Mbps, and 20 Mbps, with and without real-time LED detection, respectively·
Similarly, in [27], the authors proposed an advanced OCI-based OCC system in order to reach the IEEE
standardized maximum data rate of 54~Mbps for vehicular communication. Here, an OFDM-based
V2I communication system was presented, and the frequency response characteristics
and induced circuit noise were analyzed for enhancing the signaling design. This OCI
sensor-based vehicular communication system achieved a BER of 10$^{-5}$ and a data
rate of up to 55 Mbps when tested at a fixed distance of 1.5 m between the transmitter
and receiver.
In [35], a cooperative system using both I2V and V2V communications was proposed, where a
commercial LED-based traffic light was used as the RSU. The proposed system consists
of a commercial LED traffic light as a transmitter, a transceiver system for receiving
the message and retransmitting it to the vehicle behind it, and a second receiver
for receiving the message from the transceiver pair. With extensive experiments, the
proposed system achieved a BER of 10$^{-7}$ without using any complex error correction
codes. The experimental results proved that RSUs can communicate with vehicles outside
the service area by practicing multi-hop connections.
Fig. 4. Vehicle -to-infrastructure communication.
3.3 Vehicle-to-everything
In V2X communication, vehicles can communicate with any nearby digital entity,
including cyclists, motorcyclists, and pedestrians, which are all termed vulnerable
road users (VRUs) [36]. Along with the safety concerns of four-wheeled vehicles, safety requirements for
VRUs are also an essential part of an ITS. To enhance the safety of VRUs, any vehicle
on the road must be promptly available to communicate with them. Owing to the evolution
of smart, wearable glasses with cameras and augmented reality (AR) features, pedestrians
can now communicate with vehicular LED lights that provide information to alert people
about accidents in real time. Fig. 5 depicts a simple V2X environment where all digital entities equipped with either
an LED or a camera communicate with each other. The UAV has a camera as a receiver
to detect vehicular data, which can be a convenient way of communicating with vehicles
in areas with an absence of road-side infrastructure. Similarly, the smartphone camera
can be used by the pedestrian to retrieve vehicular information instantly from nearby
vehicular LEDs. Therefore, in OCC-based V2X, for vehicles to communicate with every
digital entity, they must be equipped with LEDs and cameras.
Several wireless technologies, such as Bluetooth, Wi-Fi, and near field communication
(NFC) [37,38], play a non-trivial role in establishing reliable communication in the V2X scenario.
These systems can establish communication with almost everything under IoT, such as
vehicle-to-home (V2H) communication [39] to control home appliances, vehicle-to-broadband (V2B), and vehicle-to-cloud (V2C)
communication [40]. Nevertheless, there are several problems associated with these technologies, such
as security concerns, module installation costs, and high power consumption, which
create hindrances to using them efficiently in daily life. Therefore, OCC can be a
feasible solution to accomplishing efficient and reliable communication in a V2X scenario.
In [29], a V2X prototype was presented that uses a 32$\times $ 32 LED array as a transmitter
and a high-speed image sensor as a receiver capable of capturing images at 1000~fps.
Here, pulse width modulation (PWM) was used to modulate the LEDs by decreasing the
transmission distance from 70 m to 30 m. In the experiment, error-free data transmission
was achieved at up to 55 Mbps, which is faster than the RF-based DSRC technique. Similarly,
in [30], a color-independent, color space-based visual MIMO was proposed for V2X communication.
The objective of the proposed system is to maintain the original color and brightness
while sustaining seamless communication at the same time. Two scenarios were considered:
multipath transmission with visual MIMO, and multi-node V2X communication. By dividing
the communication area on a node basis, simultaneous multi-node communication was
achieved without any interference. Also, the authors demonstrated numerical improvements
in the symbol error rate (SER) through noise compensation where the reference colors
are sent in the transmitted networking information.
Existing studies regarding vehicular communication with UAVs or drones [28,31] fall under V2X communication. In [28], an approach combining OCC with UAVs was proposed to solve the problem of power minimization
under communication and illumination constraints. The problems are modeled as the
smallest enclosing disk problem and a minimum-size clustering problem. To solve these
problems and obtain the optimal location of the UAVs, a randomized incremental construction
technique was applied, and a greedy method was applied for obtaining a sub-optimal
cell association. The obtained UAV locations and cell associations were iteratively
optimized several times to reduce power consumption. The numerical results from the
experiments showed more than a 53.8% improvement in power efficiency, compared to
conventional algorithms. A small-scale air-to-ground communication channel based on
OCC was considered in [31]. That study was based on an investigation of achievable capacity on the downlink
channel, where UAVs provided multiple access to a cluster of vehicles on the ground.
The camera attached to the UAV communicates with the vehicles where a line-of-sight
(LOS) channel is provided between them.
In [32], an artificial intelligence (AI)-based vehicular OCC system was presented that effectively
employs a You Only Look Once (YOLO) object detection algorithm for region-of-interest
(ROI) tracking under city and highway night-driving conditions. YOLO is capable of
tracking multiple light sources on vehicular surroundings from possible transmitters
that include nearby vehicles and VRUs. After detecting the objects, the camera can
extract essential ROI information from them. With the integration of a neural network-based
data decoding system, and AI-based error correction techniques, the proposed technique
effectively improved the data decoding rate in terms of BER by up to 10$^{-4}$. Likewise,
a study in [33] proposed a new system integrating OCC and convolutional neural networks (CNNs) [46] for an intelligent internet of vehicles (IoV). In the IoV platform, a vehicle can
receive ITS information from any entity connected to the internet. In this research,
the purpose of using the CNN is for precisely detecting and recognizing the LED patterns,
regardless of a long transmission distance and weather extremities. Here, the authors
proposed an algorithm for extracting the region of interest from the captured LED
patterns, and they applied stereo vision techniques to find the desired targets in
the distance. The simulation results on a real-time video yielded good BER performance,
and demonstrated the efficiency of the algorithm for automotive applications.
Fig. 5. Vehicle -to-everything communication.
4. Channel Characteristics and Challenges
In a vehicular communication environment with OCC, the optical wireless network
topology is related to various factors, including FOV angle and the distance between
transmitter and receiver. Since communication is completely outdoors, the performance
will be influenced by several natural factors, such as rain, smoke, fog, etc. Additionally,
the communication link between two vehicles should be held in the presence of ambient
light sources such as sunlight during the daytime. Lights from roadside infrastructure
and from other vehicles during night-time can have a particularly great influence
on communication performance. The presence of ambient light overexposes the image
sensor of the receiver camera, and hence, it disturbs capturing complete information
from the LED transmitter. Therefore, the performance of OCC-based vehicular communication
is dynamic, and greatly depends upon the channel characteristics.
Unlike RF, visible light does not suffer from multipath propagation. In contrast,
it undergoes a narrow beam path of LED lighting, providing a very high signal-to-noise
ratio (SNR). Besides, the OCC channel is secure and not affected by electromagnetic
interference. Because OCC operates on an optical wireless channel, it provides a lot
of bandwidth for data transmission, which is entirely free of cost. On the other hand,
when the transmission rate of the LED is higher than the capture rate of the camera,
the synchronization problem [41] affects the performance of an OCC-based vehicular system. Typically, the capture
rate of the commercial image sensor is 30 fps. Although that is enough for capturing
pictures and videos, it cannot achieve high data-rate communication. Flickering [42] is another challenge in OCC, which arises when the transmission rate of the LED transmitter
is significantly low. When the transmission rate is not fast enough, the light signals
are easily detected by the human eye as flickering.
LED lighting is vulnerable to natural factors, such as snow, fog, smoke, and rain,
which create a severe performance drop in the OCC system, even within a short transmission
distance. Fog contains small water droplets that disturb the passage of light due
to reflection, refraction, or scattering [13]. Furthermore, in snowy and rainy weather, the moisture in the air reduces visibility
and creates disturbances while capturing data with the receiver camera. A study in
[14] addressed this issue by simulating these environmental factors under LOS or non-LOS
(NLOS) conditions with respect to distance and angular variations. Extensive experiments
showed reliable data transmission and a fair SNR in the presence of different environmental
deterrents. In [43], an OCC system was emulated inside a laboratory to model atmospheric turbulence by
using heat-induced turbulence. They found that turbulence does not significantly affect
the system, but rather, the attenuation caused by fog was responsible for degrading
the signal quality. Therefore, by increasing the gain in the receiver camera by 16
dB, the optical power loss and quantization noise were reduced.
Vehicular communication with OCC is also subjected to multiple types of interference,
including shot noise emitted by photons, natural lighting conditions, and ambient
lighting, which influence the communication channel. Extreme exposure of the receiver
camera to sunlight creates high saturation problems, and eventually blinds the photoelement,
so the camera cannot capture accurate positioning of the LEDs. Likewise, thermal noise
and shot noise associated with photon emissions from LEDs also attenuate the signal
strength of vehicular communication. To study issues concerning the negative effects
of noise on the transmitted signal, Cailean et al. [44] proposed the use of Manchester and Miller coding approaches with OOK. Here, the effect
of noise on the pulse width of the data for both those coding approaches was analyzed.
With extensive simulations, by encoding messages with two different codes for different
noise levels, they found that the Miller-coded pulse is less affected by distortions
caused by noise.
5. Future Work
Optimization of the vehicular communication model with OCC in terms of transmission
range and accuracy is still a field of study to explore. To achieve higher data rates,
using high frame-rate cameras is usually costly to implement. With the prevalence
of AI, we can adopt several machine learning technologies [45] for signal modulation and coding, which will help to maintain a reasonable data rate,
despite a low frames-per-second capture rate in the camera. CNNs, a deep learning
(DL) framework have the ability to extract features and patterns, such as lines and
gradients, by processing structured arrays of data such as images. Therefore, they
can be employed for classification and segmentation, where ROI segmentation of the
captured raw image from LED light is required. Normally, for a long transmission distance
in OCC-based vehicular communication, the LED patterns of the transmitter cannot be
correctly identified, and require a high-end CMOS image sensor camera with complex
image processing. In that scenario, a multi-layer deep CNN with the capability of
extracting vital and more significant features from captured data can be used for
accurately identifying the LED patterns, even for long-distance transmission. Similarly,
in an ITS, an image consisting of unrecognizable LED positions captured from a very
small (or large) FOV can be enhanced by using high-end image processing techniques
with computer vision algorithms [47]. Furthermore, in OCC-based vehicular communication, the signal power at the receiver
is usually attenuated due to several noise factors present on the transmission channel.
Therefore, unsupervised learning techniques such as autoencoders [48], which learn efficient data representations by training the network to ignore signal
noise, can be suitable for SNR enhancement.
A lot of research based on DL approaches [49,50] has been conducted for error rate decrement, for reducing flicker, to sustain constant
illumination levels, for signal detection, and for channel estimation in VLC systems.
These types of techniques can be adopted in ITS applications for OCC systems. Also,
DL-based, end-to-end, multi-colored VLC transceivers [51], and binary VLC transceivers [52] that support universal dimming constraints, can be adopted to design end-to-end transceiver
networks in OCC systems for vehicular communication. Besides, in an OCC-based ITS,
the information for vehicle safety is publicly broadcasted, and there is the chance
of a privacy leak, of eavesdropping, and of unauthorized access to transmitted information,
which is a threat to security [53]. Likewise, Rohner et al. [54] studied various techniques for physical layer security
in visible light communication. However, this has not been widely explored in the
OCC-based vehicular communication environment yet. Therefore, these aspects can be
included as research of interest, and can be properly explored as upcoming work in
the area of vehicular communication with OCC.
6. Conclusion
In this paper, we reviewed recent OCC-based vehicular communication technologies
in the ITS. Different types of OCC scenarios in an ITS were addressed, including V2V,
V2I, and V2X. The OCC channel characteristics and their effect on the performance
of vehicular communication were also explored. We identified possible future work
for the advancement of vehicular communication in an ITS. We believe that OCC with
high bandwidth and information-carrying capacities will make a crucial contribution
to the future of autonomous vehicles and smart traffic environments, offering cheap,
reliable, and efficient communications.
ACKNOWLEDGMENTS
This research was supported by a National Research Foundation of Korea (NRF) grant
funded by the Korean government (NRF-2019R1A2C4069822).
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Author
Lakpa Dorje Tamang received his B.Eng. in Electrical and Electronics Engineering
from Kathmandu Univer-sity, Nepal, in 2018. He is currently an MSc student in Information
and Communication Engineering at Changwon National University, Changwon-si, South
Korea. His research interests include optical camera communication, display-to-camera
communication, digital image processing, deep learning, and artificial intelligence.
Byung Wook Kim received his BSc from the School of Electrical Engi-neering, Pusan
National University, Pusan, South Korea, in 2005, and an MS and a PhD from the Department
of Electrical Engineering, KAIST, Daejeon, South Korea, in 2007 and 2012, respectively.
He was a Senior Engineer with the Korea Electrotechnology Research Institute, Changwon-si,
South Korea, from 2012 to 2013. He was an Assistant Professor with the School of Electrical
and Railway Engineering, Kyungil University, Gyeongsan-si, South Korea, from 2013
to 2016. He was an Assistant Professor with the Department of ICT Automotive Engineering,
Hoseo University, from 2016 to 2019. He is currently an Assistant Professor with the
Department of Information and Communication Engineering, Changwon National University,
Changwon-si, South Korea. His research interests include wireless communications,
visible light communications, machine learning, and deep learning.