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A COMPREHENSIVE STUDY ON IOT
BASED ACCIDENT DETECTION SYSTEMS
FOR SMART VEHICLES
GROUP MEMBERS:
1. PRATHMESH ZENDE
2. SHAHARUKH SHAIKH
3. SHUBHAM BOYALKAR
4. JAYANT NEMADE
SIDDHANT COLLEGE OF
ENGINEERING, SUDUMBRE
DEPARTMENT OF COMPUTER
ENGINEERING
GUIDED BY:
RUPALI PANPALIYA MAM
INTRODUCTION
 Due to rapid growth of world population, the demand for vehicles has increased tremendously,
resultantly problems of traffic congestion and road accidents has also increased. The general
population’s life is under high risk, if any accident occurs there’s a long reaction time which
increments the number of deaths, therefore an automatic accident detection system must exist
to overcome this situation.
 There can be multiple causes of road accidents, some of them are, driver negligence due to
drowsiness , driving while intoxicated over speeding etc. Some studies show that weather
conditions can also contribute towards the severity of an accident such as fog, rain, high winds.
High winds can directly influence the vehicle which may deviate the vehicle from road, or
indirectly due to obstruction dangers present on the roads such as trees, walls etc.
 Road crashes can be seen as a collision between any on road vehicles, obstacles or pedestrians.
The survival rate of victim is highly reliant on how long an ambulance takes to reach the site
of the accident and then carry the patient to the hospital. In most cases of road accidents, the
injuries are not severe and the life of the victim can be rescued, however due to late arrival of
the rescue teams, the injuries turn deadly.
MOTIVATION
 The consequences of road accidents are not just constrained to the loss of human
lives yet, also incorporate the destruction of property, traffic blockages, and immense
economic loss. Thus automatic accident detection systems are the need of time,
which can speed up the rescue operations and limit the causalities after the mishap
and numerous lives can be saved. This paper features existing mechanisms to detect
accidents, its working, and limitations. Furthermore, accident prevention
methodologies, accident contributing factors are highlighted as well.
 This study critically reviews existing literature on accident detection and prevention
techniques, with the objective that smart systems can be developed with improved
accuracy and better strategies to control accident causing factors while watching out
for the existing challenges in the current systems.
PROBLEM DEFINITION
 We have observed that in many severe accident cases immediate help is not send
which leads to death of victims; in some cases, the family members of victims are
informed after a long period of time; and in some rare cases the identity of victims
remain unknown. This problem is usually faced with two wheeler vehicle where
safety measures are not included yet whereas in 4-wheeler vehicle there are certain
safety measures like airbags, emergency breaks,etc, hence, there is requirement for
immediate help for two wheeler vehicle.
LITERATURE SURVEY
Paper Name Auther Name Discription
1.An IOT Based Smart
System for Accident
Prevention and Detection
Samita khairnar,sayanee
nanda
Nowadays, the number of accidents has increased rapidly. About 17 accidents
take place every hour. Bike accidents constitute a major chunk of all accidents;
this is because two-wheelers do not have as many safety parameters which are
included in four-wheelers. Reasons causing it can be due to not wearing a
helmet, feeling drowsy while driving, alcohol consumption, two vehicles
coming into closer proximity without both drivers' notice, breaking of traffic
signals, driving without a valid or no driving license, careless driving,
unintended triggering of the acceleration pedal, etc. The main objective of this
paper is to propose a system which can effectively help in preventing any kind
of mishaps and if such conditions occur then how it detects and informs the
concerned authorities and people, so that the situation can be taken care of
immediately.
2. Vehicle Collision
Detection and Avoidance
with Pollution Monitoring
System Using IoT
Mahesh
A.Rakhonde,S.A.Khoje
The increase in population leads to increment in pollution and accident as well.
Technological development makes great efforts to detect the accident and
monitor the increasing pollution. This paper is based on enhancement in the
smart vehicle system. Different units implemented in this paper which enhance
the vehicular system. The main objective is to detect accidents in real time and
minimize the response time of medical help. For accident avoidance, tire
pressure is measured whereas in accident detection is implemented with the
help of node MCU. MQ7 is used in order to monitor the pollution. The
proposed system is useful in reducing the vehicular accidents and pollution
monitoring will help to know the environmental status.
LITERATURE SURVEY
Paper Name Auther Name Discription
3.IoT based framework
for Vehicle Over-speed
detection
Mohammad Ahmar
Khan,Sarfraz Favaz khan
Automatic vehicle monitoring has turned out to be a very crucial scenario
in the current years. It may develop into possibility by executing the
following technologies. This project targets to propose a system, which
detects speeding vehicles over a specific speed limit and immediately
report to concerned authorities. At present, road accidents rates have
raised so, there is a necessity for developing a system that detects an
over speeding vehicle. The implementation of present Smart Vehicle
Overspeeding Detector using Internet of Things determines all the road
traffic information automatically with intelligence. The smart vehicles are
suitable with overspeeding detector that has capability for recording,
storing and information sharing about the vehicle's speed. The system
contains GPS module, Radar, Google maps and IoT module.
4.A Comprehensive Study
on IoT Based Accident
Detection Systems for
Smart Vehicles
Unaiza Alvi,Muazzam
A.khan khattak
With population growth, the demand for vehicles has increased tremendously,
which has created an alarming situation in terms of traffic hazards and road
accidents. The road accidents percentage is growing exponentially and so are the
fatalities caused due to accidents. However, the primary cause of the increased
rate of fatalities is due to the delay in emergency services. Many lives could be
saved with efficient rescue services. The delay happens due to traffic congestion
or unstable communication to the medical units. The implementation of
automatic road accident detection systems to provide timely aid is crucial. Many
solutions have been proposed in the literature for automatic accident detection.
The techniques include crash prediction using smartphones, vehicular ad-hoc
networks, GPS/GSM based systems, and various machine learning techniques.
With such high rates of deaths associated with road accidents, road safety is the
most critical sector that demands significant exploration. In this paper, we
present a critical analysis of various existing methodologies used for predicting
and preventing road accidents, highlighting their strengths, limitations, and
challenges that need to be addressed to ensure road safety and save valuable
lives.
SOFTWARE REQUIREMENT
SPECIFICATION
 RAM : 8 GB
 Hard Disk : 40 GB
 Processor : Intel i5 Processor
 IDE : Spyder, Pycharm
 Coding Language : Python Version 3.8
 Operating System : Windows 10
HARDWARE REQUIREMENT
SPECIFICATION
 Processor : Pentium-IV
 Speed : 1.1 GHz
 RAM :512 MB(min)
 Hard Disk : 40 GB
 Key Board : Standard Windows Keyboard
 Mouse : Two or Three Button Mouse
 Monitor : LCD/LED
SYSTEM ARCHITECTURE
PROJECT SCOPE
 Any automobile (2-wheeler) organization can use this system.
 Any emergency vehicle can reach the accident location on time.
 Any user information can be feeded to the system.
 Exact coordinates is send to the relatives and nearby ambulance.
 Provides network between the accident victim and hospital.
RASPBERRY PI
 With an in-built uadcore processor, Raspberry Pi can serve as the “Internet
Gateway” for IoT devices. Powered by a cloud network, Pi acts as a web server for
uploading and transiting sensor data on IoT platforms.
 Custom code, an operating system, a Python library, and a cloud network are all it
takes to use Pi Computer as a web server.
 The Raspberry Pi is a low cost, credit-card sized computer that plugs into a computer
monitor or TV, and uses a standard keyboard and mouse. It is a capable little device
that enables people of all ages to explore computing, and to learn how to program in
languages like Scratch and Python.
PROJECT TIMELINE CHART
N
O
TASK DURATION(DA
YS)
1 Group Formation 4
2 Decide Area Of Interest 4
3 Search Topic 5
4 Topic Selection 5
5 Sanction Topic 5
6 Search Related Information 12
7 Understanding Concept 7
8 Search Essential
Document(IEEE & White
Paper, Software)
6
9 Problem Definition 2
10 Literature Survey 5
11 SRS 14
12 Project Planning 2
ASSUMPTION AND DEPENDENCIES
Using Python language
Input as video
CONCLUSION
A system to process real-time CCTV footage to detect any Anomaly activity will help to
create better security and less human intervention. Great strides have been made in the
field of human anomaly Activity, which enables us to better serve the myriad
applications that are possible with it. Moreover, research in related fields such as
Activity Tracking can greatly enhance its productive utilization in several fields.
REFERENCES
 M. Omidyeganeh, A. Javadtalab , and S. Shirmohammadi . Intelligent Driver
Drowsiness Detection through Fusion of Yawning and Eye Closure . In 2011
IEEE conference .
 Fang Zhang, Fang Zhang , Lei Geng , and Zhitao Xiao. Driver Fatigue Detec-
tion based on Eye State Recog- nition. In 2017 International Conference on
Machine Vision and Information Technology .
 Christian Szegedy, Scott Reed, Dumitru Erhan, Dragomir Anguelov, and
Sergey loffe. Scalable High Quality Object Detection. In arXiv:1412.1441v3
[cs.CV], 9 Dec 2015
 Dinalankara. Face Detection Face Recogni- tion using Open Computer Vision
Classifires. In IEEE conference.
 Thomas Brandt, Ralf Stemmer, and Germany Andry Rakotonirainy. Afford-
able Visual Driver Monitoring System for Fatigue and Monotony. In IEEE
conference.
THANK YOU

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accident Detection.pptx

  • 1. A COMPREHENSIVE STUDY ON IOT BASED ACCIDENT DETECTION SYSTEMS FOR SMART VEHICLES GROUP MEMBERS: 1. PRATHMESH ZENDE 2. SHAHARUKH SHAIKH 3. SHUBHAM BOYALKAR 4. JAYANT NEMADE
  • 2. SIDDHANT COLLEGE OF ENGINEERING, SUDUMBRE DEPARTMENT OF COMPUTER ENGINEERING GUIDED BY: RUPALI PANPALIYA MAM
  • 3. INTRODUCTION  Due to rapid growth of world population, the demand for vehicles has increased tremendously, resultantly problems of traffic congestion and road accidents has also increased. The general population’s life is under high risk, if any accident occurs there’s a long reaction time which increments the number of deaths, therefore an automatic accident detection system must exist to overcome this situation.  There can be multiple causes of road accidents, some of them are, driver negligence due to drowsiness , driving while intoxicated over speeding etc. Some studies show that weather conditions can also contribute towards the severity of an accident such as fog, rain, high winds. High winds can directly influence the vehicle which may deviate the vehicle from road, or indirectly due to obstruction dangers present on the roads such as trees, walls etc.  Road crashes can be seen as a collision between any on road vehicles, obstacles or pedestrians. The survival rate of victim is highly reliant on how long an ambulance takes to reach the site of the accident and then carry the patient to the hospital. In most cases of road accidents, the injuries are not severe and the life of the victim can be rescued, however due to late arrival of the rescue teams, the injuries turn deadly.
  • 4. MOTIVATION  The consequences of road accidents are not just constrained to the loss of human lives yet, also incorporate the destruction of property, traffic blockages, and immense economic loss. Thus automatic accident detection systems are the need of time, which can speed up the rescue operations and limit the causalities after the mishap and numerous lives can be saved. This paper features existing mechanisms to detect accidents, its working, and limitations. Furthermore, accident prevention methodologies, accident contributing factors are highlighted as well.  This study critically reviews existing literature on accident detection and prevention techniques, with the objective that smart systems can be developed with improved accuracy and better strategies to control accident causing factors while watching out for the existing challenges in the current systems.
  • 5. PROBLEM DEFINITION  We have observed that in many severe accident cases immediate help is not send which leads to death of victims; in some cases, the family members of victims are informed after a long period of time; and in some rare cases the identity of victims remain unknown. This problem is usually faced with two wheeler vehicle where safety measures are not included yet whereas in 4-wheeler vehicle there are certain safety measures like airbags, emergency breaks,etc, hence, there is requirement for immediate help for two wheeler vehicle.
  • 6. LITERATURE SURVEY Paper Name Auther Name Discription 1.An IOT Based Smart System for Accident Prevention and Detection Samita khairnar,sayanee nanda Nowadays, the number of accidents has increased rapidly. About 17 accidents take place every hour. Bike accidents constitute a major chunk of all accidents; this is because two-wheelers do not have as many safety parameters which are included in four-wheelers. Reasons causing it can be due to not wearing a helmet, feeling drowsy while driving, alcohol consumption, two vehicles coming into closer proximity without both drivers' notice, breaking of traffic signals, driving without a valid or no driving license, careless driving, unintended triggering of the acceleration pedal, etc. The main objective of this paper is to propose a system which can effectively help in preventing any kind of mishaps and if such conditions occur then how it detects and informs the concerned authorities and people, so that the situation can be taken care of immediately. 2. Vehicle Collision Detection and Avoidance with Pollution Monitoring System Using IoT Mahesh A.Rakhonde,S.A.Khoje The increase in population leads to increment in pollution and accident as well. Technological development makes great efforts to detect the accident and monitor the increasing pollution. This paper is based on enhancement in the smart vehicle system. Different units implemented in this paper which enhance the vehicular system. The main objective is to detect accidents in real time and minimize the response time of medical help. For accident avoidance, tire pressure is measured whereas in accident detection is implemented with the help of node MCU. MQ7 is used in order to monitor the pollution. The proposed system is useful in reducing the vehicular accidents and pollution monitoring will help to know the environmental status.
  • 7. LITERATURE SURVEY Paper Name Auther Name Discription 3.IoT based framework for Vehicle Over-speed detection Mohammad Ahmar Khan,Sarfraz Favaz khan Automatic vehicle monitoring has turned out to be a very crucial scenario in the current years. It may develop into possibility by executing the following technologies. This project targets to propose a system, which detects speeding vehicles over a specific speed limit and immediately report to concerned authorities. At present, road accidents rates have raised so, there is a necessity for developing a system that detects an over speeding vehicle. The implementation of present Smart Vehicle Overspeeding Detector using Internet of Things determines all the road traffic information automatically with intelligence. The smart vehicles are suitable with overspeeding detector that has capability for recording, storing and information sharing about the vehicle's speed. The system contains GPS module, Radar, Google maps and IoT module. 4.A Comprehensive Study on IoT Based Accident Detection Systems for Smart Vehicles Unaiza Alvi,Muazzam A.khan khattak With population growth, the demand for vehicles has increased tremendously, which has created an alarming situation in terms of traffic hazards and road accidents. The road accidents percentage is growing exponentially and so are the fatalities caused due to accidents. However, the primary cause of the increased rate of fatalities is due to the delay in emergency services. Many lives could be saved with efficient rescue services. The delay happens due to traffic congestion or unstable communication to the medical units. The implementation of automatic road accident detection systems to provide timely aid is crucial. Many solutions have been proposed in the literature for automatic accident detection. The techniques include crash prediction using smartphones, vehicular ad-hoc networks, GPS/GSM based systems, and various machine learning techniques. With such high rates of deaths associated with road accidents, road safety is the most critical sector that demands significant exploration. In this paper, we present a critical analysis of various existing methodologies used for predicting and preventing road accidents, highlighting their strengths, limitations, and challenges that need to be addressed to ensure road safety and save valuable lives.
  • 8. SOFTWARE REQUIREMENT SPECIFICATION  RAM : 8 GB  Hard Disk : 40 GB  Processor : Intel i5 Processor  IDE : Spyder, Pycharm  Coding Language : Python Version 3.8  Operating System : Windows 10
  • 9. HARDWARE REQUIREMENT SPECIFICATION  Processor : Pentium-IV  Speed : 1.1 GHz  RAM :512 MB(min)  Hard Disk : 40 GB  Key Board : Standard Windows Keyboard  Mouse : Two or Three Button Mouse  Monitor : LCD/LED
  • 11. PROJECT SCOPE  Any automobile (2-wheeler) organization can use this system.  Any emergency vehicle can reach the accident location on time.  Any user information can be feeded to the system.  Exact coordinates is send to the relatives and nearby ambulance.  Provides network between the accident victim and hospital.
  • 12. RASPBERRY PI  With an in-built uadcore processor, Raspberry Pi can serve as the “Internet Gateway” for IoT devices. Powered by a cloud network, Pi acts as a web server for uploading and transiting sensor data on IoT platforms.  Custom code, an operating system, a Python library, and a cloud network are all it takes to use Pi Computer as a web server.  The Raspberry Pi is a low cost, credit-card sized computer that plugs into a computer monitor or TV, and uses a standard keyboard and mouse. It is a capable little device that enables people of all ages to explore computing, and to learn how to program in languages like Scratch and Python.
  • 13. PROJECT TIMELINE CHART N O TASK DURATION(DA YS) 1 Group Formation 4 2 Decide Area Of Interest 4 3 Search Topic 5 4 Topic Selection 5 5 Sanction Topic 5 6 Search Related Information 12 7 Understanding Concept 7 8 Search Essential Document(IEEE & White Paper, Software) 6 9 Problem Definition 2 10 Literature Survey 5 11 SRS 14 12 Project Planning 2
  • 14. ASSUMPTION AND DEPENDENCIES Using Python language Input as video
  • 15. CONCLUSION A system to process real-time CCTV footage to detect any Anomaly activity will help to create better security and less human intervention. Great strides have been made in the field of human anomaly Activity, which enables us to better serve the myriad applications that are possible with it. Moreover, research in related fields such as Activity Tracking can greatly enhance its productive utilization in several fields.
  • 16. REFERENCES  M. Omidyeganeh, A. Javadtalab , and S. Shirmohammadi . Intelligent Driver Drowsiness Detection through Fusion of Yawning and Eye Closure . In 2011 IEEE conference .  Fang Zhang, Fang Zhang , Lei Geng , and Zhitao Xiao. Driver Fatigue Detec- tion based on Eye State Recog- nition. In 2017 International Conference on Machine Vision and Information Technology .  Christian Szegedy, Scott Reed, Dumitru Erhan, Dragomir Anguelov, and Sergey loffe. Scalable High Quality Object Detection. In arXiv:1412.1441v3 [cs.CV], 9 Dec 2015  Dinalankara. Face Detection Face Recogni- tion using Open Computer Vision Classifires. In IEEE conference.  Thomas Brandt, Ralf Stemmer, and Germany Andry Rakotonirainy. Afford- able Visual Driver Monitoring System for Fatigue and Monotony. In IEEE conference.