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978-1-6654-7886-1/22/$31.00 ©2022 IEEE
Application of IoT and Artificial Intelligence in
Road Safety
1st Srimantini Bhattacharya
Dept. of Civil Engg
NIT Durgapur
Durgapur,India
srimantini2019@gmail.com
2nd Harsh Jha
Dept. of Civil Engineering
NIT,Durgapur
Durgapur, India
harsh002jha@gmail.com
3rd Radhikesh P. Nanda
Dept. of Civil Engg.
NIT Durgapur
Durgapur, India
ORCID-0000-0002-4005-6821
Abstract—This paper explores the advancement of the
Internet of Things (IoT) and Machine Learning in the field of
Road Safety and accident prevention with a state-of-the-art
review of various techniques adopted for implementing an
intelligent road Safety System. In this review, emphasis is given
on the behavior of drivers, the condition of vehicles (two-
wheelers, four-wheelers), health condition of roads and
bridges, and theft-related issues using Radio Frequency
Identification (RFID). It is seen that, with the help of IoT, the
safety system can be updated on a real-time basis which can
help to create a smart, intelligent, and highly efficient Road
Safety system. Artificial Intelligence (AI) is applied to enhance
the technology further for detecting the driver's behavior like
drowsiness with the help of real-time camera feed or high-
resolution images. Additionally, the role of AI in detecting the
condition of roads and bridges in preventing road accidents is
also discussed. Though the paper provides a good insight into
the application of IoT and Machine Learning in the smart
Road safety system, certain limitations are highlighted.
Keywords— Internet of Things (IoT), Artificial Intelligence
(AI), Artificial Neural Network (ANN), Rasberry Pi (RPI), Radio
Frequency identification (RFID), Global Positioning System
(GPS), Intelligent Transportation System (ITS).
I. INTRODUCTION
With the exponential growth of urbanization in India, there
is also an increase in traffic congestion in day-to-day life,
and the traditional traffic management system is ineffective
[1,2]. This makes road safety one of the prime importance
because of the rise in deaths due to crashes or accidents on
the road. With the number of vehicles on our roads
increasing with every passing hour, it's vital to come up with
an optimal solution that can be effective in maintaining the
safety of the people on the road and thereby reducing the
chances of deaths in the country. With the expeditious
growth of technology, various methods of Road Safety have
been developed all across the world using the Internet of
Things and Machine Learning to make the analysis
systematic and efficient [3]. Researchers as stated in [4,5]
have extensively reviewed two main fields to prevent road
crashes–(a) The behavior of driver under various conditions,
(b) pieces of information like driving speed, frequency of
application of brakes, diverse engine and transmission
system parameters under different peripheral conditions.
The majority of the methods developed involves three
segments - Use of Machine Learning Model for analyzing
the behavior of the drivers (First Segment), Collection of
data from multiple sensors and connecting them to various
servers, network devices for processing and transmission of
data using Internet of Things (IoT) (Second Segment),
Development of a user-friendly interface such as Android
App for the users (Third Segment). IoT and Machine
Learning are some of the booming technologies of today,
helping in intelligent monitoring, reporting, and control of
every aspect of our lives.
The traffic safety system of a metropolitan city is a major
parameter for urban mobility. This paper presents a review
of application of IoT and Machine Learning in road safety
and smart road system. Next part of the paper focuses role
of IoT and Artificial Intelligence in detecting drowsiness,
consumption of alcohol, over-speeding or missing safety
gear such as seat belts, helmets by the driver thereby
avoiding deaths or causalities. Finally, the limitations of
such technology in road safety are described. This research
produces a system proposal that can be used in further
research and can be applied in terms of the development of
smart cities, especially smart transportation.
II. LITERATURE SURVEY
With the rapid growth of modernization, there is a havoc
rise of traffic, which increases congestion and leads to
severe crashes and accidents, putting many lives in danger.
Approximately 1.35 million people lose their lives every
year due to accidents, and around 50 million suffer from
severe injuries. With India focussing on building smart
cities, this is a major concern for India to find an effective
solution. The best way to implement IoT and Machine
Learning techniques and make a smart Road safety system
that will analyze the physical parameters of vehicles and
study the drivers' behavior that will efficiently reduce
congestion, reduce the time of travel, and help prevent
crashes.
IoT and Machine Learning are both the most booming
technologies of today. IoT describes communication
between various physical objects, embedded with sensors,
software for analyzing and transmitting information between
systems with the help of the internet. IoT makes it possible
for users to collect and analyze data with minimum human
intervention making the process cost-effective due to easily
available sensors and efficiency. Devices and sensors are
connected to an IoT Platform that integrates data and applies
analytics to give the most valuable information. These IoT
platforms make it easy to transmit useful pieces of
information that can be used for detection and make
recommendations for a particular problem. Another growing
2022 Interdisciplinary Research in Technology and Management (IRTM) | 978-1-6654-7886-1/22/$31.00 ©2022 IEEE | DOI: 10.1109/IRTM54583.2022.9791529
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technology is Machine Learning, a branch of artificial and
computer science that focuses on using data and algorithms
the way humans learn with accuracy. Machine Learning
uses data, various algorithms, and statistical methods to
understand, recommend, classify and detect different
situations with high accuracy. Thus, we can use these
booming technologies to make a highly efficient and user-
friendly road safety system.
For example, as discussed in [6], to tackle the problem of
traffic congestion leading to crashes and accidents due to the
increase in population during the 2022 World Cup in Qatar,
the naturalistic driver behavior is utilized to collect and
analyze the data for traffic planning to maintain safety. An
IoT-based solution is implemented, which collects vehicle
data like - time of the trip, GPS location, maximum,
minimum, and average speed to study the data and predict
accidents and road infrastructure development to prevent
crashes. The driver's behavior is also considered, including
the driver's drowsiness level. The figure 1 from [7]
describes the alarming situation in Road Safety due to
Driver drowsiness. A framework and a deep learning-based
application are implemented to check the drowsiness level
of the driver to an accuracy of 82%.
Fig-1 Accidents due to drowsiness
In [8], a cost-effective, novel IoT architecture is made with
robust computational methods in assessing road safety. This
safety system approach to road safety is now adopted
worldwide. However, the considerations are made for the
medium-to-long term. This work implements the approach
for a short-to-medium-term dynamic assessment of road
safety. In addition, the use of machine learning in the design
of the computational core is show-cased by using an
application of Hidden Markov Models. The proposed
architecture is demonstrated through an application to
safety-based route planning.
Machine Learning also plays a vital role in predicting
accidents. Prediction of the automotive accident severity is
important for an intelligent traffic system. In [9] some
classification models like Logistic Regression, Artificial
Neural network, Decision Tree, K-Nearest Neighbors, and
Random Forest have been implemented to predict accident
severity. A secure communication architecture model has
been explained to exchange information among the devices,
and a web-based alarm system is introduced that can alert
the user for any accident. However, these techniques require
a wide range of land data as input, reducing to a low
resolution and losing detail. So, to solve this problem, a
solution as stated in [10] has been proposed to use a high-
resolution data framework and the application of IoT is the
deep learning architecture of Convolution Neural Network
(CNN). It is found that high resolution of data provides a
result with more accuracy.
Several solutions have been presented to solve the problem
of tackling traffic congestion [11]. One of these methods is
to solve this problem through Artificial Neural Networks
(ANN). Modeling Smart Road Traffic Congestion Control
using Artificial Back Propagation Neural Networks
(MSR2C-ABPNN) is proposed [12] with the help of ANN
for road traffic regulation and control. The proposed
MSR2C-ABPNN based model predicts the point of
congestion using neural network and backpropagation
algorithm. It is further divided into two sub-layers The first
layer, i.e., prediction layer, backpropagation, is used to
detect the occupancy. In contrast, the second layer, i.e., the
performance layer, evaluates the prediction layer
performance in terms of RMSE, Accuracy, and Miss Rate.
Tab-1 from [13] shows the statistics of law-breakers in two-
wheelers and four-wheelers which is a major reason behind
accidents. In [14], smart accident prevention and detection is
proposed using V2V communication technologies,
Raspberry Pi, various sensors, MEC architecture, etc. for the
prevention of two-wheeler accidents. This system detects
accidents by vibration sensors, accelerometers. For
detection, the GPS and GSM modules locate the accident
site and correspondingly inform the person’s nearest ones
and nearby hospitals through a text message. The system
also requires the person that will be riding the bike to have a
valid driving license using the already embedded RFID on
the driving license. The RFID reader on the bike will have at
most ten registered users, hence handling theft-related
issues. The system is efficient in terms of both the
parameters and performance.
Table-1 Statistics of Law Breakers on two and four-wheelers
Law breakers Two-wheelers Four-wheelers
Signal jump 2,20,859 1,46,945
Drunken drive 36,727 17,237
Furthermore, to prevent accidents, an alerting system has
been set up as proposed in the [15]. The prototype is
designed using Raspberry Pi, Pi Camera, sensors for
monitoring driver's eye movements, detecting yawning,
detecting toxic gases, and alcohol consumption to prevent
accidents and to provide safety assistance to the drivers.
Thereafter the Internet of Things (IoT) and Machine
Learning (ML) enabled system is implemented in vehicles
for transmitting the behavior of the driver and his driving
pattern to the cloud to take quick response under
emergencies. Cloud services and machine learning are
employed in identifying the fatigue of the drivers through
the collected dataset. Various Machine Learning and IoT
based algorithms work simultaneously to smoothly execute
all the different prevention methods. The general
architecture model is also shown below in the fig 2[13]. The
device is experimentally tested, and the results show its
efficiency and effectiveness.
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Fig-2 General architecture of the model
A model was proposed [16] to incorporate ITS context
incorporating speed and pollution adaptive traffic control
systems with weather advisories. The Scalable Enhanced
Road Side Unit (SERSU) was developed for use as part of a
comprehensive Intelligent Transportation System based on
the Internet of Things (IoT) concept. It collects information
using a variety of sensors and an onboard camera. The
collected data can then be uploaded to a central server for
speed limit adjustment, metering routes to reduce vehicle
congestion and emissions, and issuing weather advisory
warnings. Wireless cellular networks and RF modules are
used for communication with all the devices.
To prevent accidents on the road, Smart IoT cars are
proposed in [17] in which we can control distinctive things
or keep track of a vehicle for security, solace, and
proficiency. Technologies such as ease liquor sensor, safety
belt consisting of an inbuilt heartbeat sensor, and edge
restrict are designed to prevent mishaps from occurring. All
controls are accessible in auto proprietors' dashboard
accessible both in auto and in a versatile application.
Another prominent factor that contributes to fatal accidents
is the behavior of drivers. Often due to driver drowsiness or
drunken driving, major accidents are caused, which
contributes to a large number of deaths. So it is of grave
importance to solve this problem. With the help of the Q-
135 sensor [18], alcohol can be detected from the air, so
whether the driver is drunk or not can be checked. The
sensor sends the signal to ARM-7. If the driver is drunk,
then vehicle ignition will not start until the driver is not
changed. In case the car is already in driving condition, then
the system alerts the driver using a buzzer. Another human
behavior that contributes to road accidents is microsleep.
This is due to the current trend of life like heavy workloads,
long working hours, excess consumption of caffeine, and
many others causing massive fatal accidents. The creation of
SMART Vehicles in the IoT increases the technology
capabilities in reducing the number of crashes on the roads.
An integration with Artificial Intelligent (AI) [19] can be
used to detect microsleep. While the image processing can
see the face changes from normal to microsleep symptoms
by tracking the eye degree, the head motion, and the mouth
yawning.
Another critical aspect leading to road accidents is low
visibility [20]. It can also be caused by other accidents,
work-in-progress on roads, excessive motorized vehicles,
especially at peak times, and so on. Fixed traffic sensors are
installed on roads that interact with drivers' mobile apps
through the 4G network to solve this problem. This,
however is not feasible on all roads. To make it work for
non-highway roads, mobile traffic sensors can be installed
in private and public transportation and volunteer vehicles.
An IoT Cloud system based on OpenGTS and MongoDB is
proposed to solve this problem and assist critical rescue
vehicles such as ambulances, etc. It is also shown below in
fig 3. Experiments run on this application show-case that
this system shows the acceptable response time to be sent to
drivers to mitigate this problem by avoiding accidents.
Fig-3 IoT Cloud system for traffic monitoring and alert notification.
III. SIGNIFICANCE OF SMART ROAD SAFETY SYSTEM
As per the United Nations 2030 plan, there is a requirement
for an enhanced transportation system with highly effective
road safety. This can be accomplished with the assistance of
IoT and Artificial Intelligence. The primary objective of IoT
and Artificial Intelligence to be used in Road Safety is to
connect the traffic system using real-time data, which helps
all the traffic information be updated efficiently and
simultaneously. IoT can play a significant role in collecting
all the necessary data from the vehicle, such as trip details,
speed limit, GPS, etc. This can help prevent crashes and
enhance safety very efficiently. Similarly, with the help of
Artificial Intelligence, we can track the driver's behavior and
the pedestrian that can help us prevent accidents. These
methods can be highly efficient for maintaining an advanced
traffic system with effective road safety. In [21], a
discussion is made on the effective implementation of these
budding technologies to provide a reliable, intelligent, and
smart experience to the users traveling along the highways.
The study is divided into five categories- smart highway
lighting system, smart traffic and emergency management
system, renewable energy sources on highways, smart
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display, and Artificial Intelligence in Highways. This study
has also discussed the enhancement of Road safety and
sustainable experience for travellers by integrating deep
learning techniques in the edge-based vision node for
studying the pattern of traffic flow, road condition, and road
safety.
Apart from these Internet of Things and Artificial
Intelligence can also play an important role in detecting
drowsiness, the behavior of drivers after consumption of
alcohol, over speeding or missing safety gear such as seat
belts, helmets can help us to avoid crashes and thereby
avoid any serious deaths or injuries. Below are some reasons
that cause crashes that have been discussed which can be
optimised with the help of IoT and Machine Learning.
Over Speeding:
This is one of the common reasons behind crashes. Humans
tend to Overspeed and often lose control, leading to fatal
accidents. So to tackle this problem, a model can be used as
stated in [22]. A micro-controller and a radio frequency
identification system are used to limit the overspeeding of
vehicles. Automatic speed reduction can be achieved with
the help of the transmitter and receiver modules when the
vehicles are in an accident-prone region.
Driver's behavior:
Another important reason behind fatal road accidents is the
distraction of drivers or drowsiness. This problem can be
tackled by a novel ensemble approach based on deep
learning, as stated in [23], which can detect a distracted
driver by detecting the objects involved in distracting the
driver using Faster-RCNN. This has achieved a validation
accuracy of 97.7%. Moreover, to reduce any false alarms,
the pose points of the driver are also taken into
consideration to make sure only those objects are detected
which are directly associated with the driver's distraction.
The proposed method is not only time-efficient, robust but
cost-efficient as well. Apart from this, with the help of Deep
Learning, we can also detect the drowsiness of driver as
stated in [24] with the help of an architecture consisting of 4
deep learning layers - AlexNet, VGG-FaceNet,
FlowImageNet, and ResNet, which uses videos of drivers as
input and detect drowsiness. Using a Soft-Max classifier, it
also considers hand gestures, facial expressions, behavioral
features, and head movements to detect drowsiness with
almost 85% accuracy.
Lack of Safety Measures:
With the increase in the number of vehicles on the road and
traffic congestion, the absence of safety gears like helmets
and seat belts have become a prominent reason for an
accident. The best way we can tackle this is with the help of
IoT and machine learning, like a smart helmet system [25]
in which IR and alcohol detection sensors are used to detect
the presence or absence of a helmet. Similarly, a seat belt
detection system [26], with the help of the YOLOv3 target
detection algorithm and the lightweight network structure,
can be used to prevent road accidents and thus decrease
deaths.
IV. ADVANTAGES OF IOT AND ARTIFICIAL INTELLIGENCE IN
ROAD SAFETY
The transportation authority is associated with a high
maintenance cost, fatal accidents, injuries, and loss of life
every year. For a developing nation like India, it is vital to
find a solution. The best way to tackle this problem is with
the help of the booming technologies of today like Machine
Learning and artificial intelligence. These technologies can
help optimize the maintenance cost and prevent fatal
accidents, thus saving the lives of civilians.
Detecting human behavior:
One of the major reasons that contribute to fatal accidents is
human behavior and their negligence to follow the traffic
rules. IoT can play a proactive role in helping drivers adopt
safety rules. With the help of IoT, the traffic management
system gets updated with real-time data, thus increasing the
efficiency of the safety system. With the help of Machine
Learning and IoT, we can judge the driver's behavior and
alert them simultaneously. For example, in [27], a deep
learning model is proposed to explore the complex
interactions between the driver's behavior and the road
environment. The proposed model consists of an
unsupervised Denoising Stacked Autoencoder (SDAE) that
will provide output layers in RGB colors. The SDAE model
is also shown below in the figure. With the help of the
graphical outcome, the patterns of simple driving behavior
and road environment complexity can be detected with
better efficiency.
Detecting Condition of Roads and bridges
Another factor that influences road accidents is the poor
condition of roads. With the help of IoT, we can
continuously monitor the condition of roads so that
maintenance can be done accordingly, which will help us
prevent crashes. With the help of Machine Learning, we can
identify cracks in bridges and that can help prevent any
traumatic disasters. One such system can be an Intelligent
speed breaker system as proposed in [28], which helps
detect speed breakers that might get neglected by drivers
due to bad visibility or over-speeding. In this system, an RF
module is used that warns the driver about the existence of a
speed breaker in proximity and also assists in automatically
reducing the vehicle’s speed if no action is taken in time.
Through the IoT, GPS location of speed breaker can be sent
to the cloud using GPS and stored on the cloud to use it for
future. Additionally, with the help of ML, we can also
perform pothole detection as shown in [29], in which
accelerator and gyroscope, which are usually available in all
smartphones, are used to collect road status information and
use Transfer learning for detecting the potholes.
Shortly, IoT and Machine Learning can greatly assist people
in preventing road accidents. These technologies will help
develop a smart, efficient, and intelligent traffic system that
will help the countries reduce their mortality rate due to road
accidents and help the transportation authority reduce their
maintenance cost and help them prevent disaster. Thus these
technologies prove to be a boon in the field of road safety.
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V. LIMITATIONS OF IOT AND ARTIFICIAL INTELLIGENCE IN
ROAD SAFETY
Although the concepts of IoT applying to road safety sound
exciting and very much increase road safety, these
applications are still not in use. This can be accredited for
several reasons. The most important is the effectiveness [30]
of the application not being considered. Most of the studies
evaluating IoT technologies to prevent accidents on
construction sites have not investigated their effects, which
presumably prevents construction companies from adopting
IoT technologies. The current body of knowledge lacks a
method for quantitatively evaluating the effectiveness of
such technologies, which probably is one reason why they
have not been deployed widely. Secondly [31],
infrastructural, issues such as road zoning, planning, & other
construction-related concerns become significant problems
for implementing this technology. Thirdly, high-speed
Internet-oriented data transfer media is required to apply the
IoT-based technology. In a country like India, the network
unavailability or instability for any reason may disrupt the
entire traffic control system. Therefore, overall data
management would be a big challenge for this traffic
management system. Lastly, many devices are accessing the
central network, which increases the chance of hacking or
malfunctioning the system. Hence, a top-notch security
layer is necessary for making an impenetrable and hack-safe
smart traffic application. In addition to this, [32] there is a
loss of data when huge unconnected vehicles come between
the connected vehicles and at building intersections.
Because of more moving vehicles, Doppler effects,
shadows, and multiple paths fading occur.
VI. CONCLUSION
The present study focuses on all the various researches
made in road safety with the help of IoT and Machine
Learning. It is seen that, with the help of IoT, the safety
system can be updated on a real-time basis which can help
to create a smart, intelligent, and highly efficient road safety
system. AI is applied to enhance the technology further for
detecting the driver's behavior like drowsiness with the help
of real-time camera feed or high-resolution images. The true
power of IoT in ensuring safe driving continues to be
unleashed as cars move towards becoming fully autonomous
and start interacting with their environment and making
decisions on their own. This will prevent drivers from
entering hazardous areas, assisting in avoiding collisions,
selecting detours, and avoiding traffic congestions. Though
research on IoT and ML has not yielded much development
in Civil Engineering sector, present study definitely gives a
major breakthrough in transportation Engineering
particularly for road safety measures.
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