Nenns

1. Guided by
P.MATHESWARAN ,B.E,M.E
Ass HOD CSE department
Submitted By
TAMILMANNAVAN S
THANGASELVAN M
JAYA SURYA S
CSE-B
FINAL YEAR
IOT BASED DATA LOGGER FOR MONITORING
AND CONTROLLING OF VEHICLE ACCIDENT

2. ABSTRACT
 Traffic in our country is increasing day by day. Many people are not giving a good
response for the traffic rules in many places.
 Mainly accidents happen due to over speed and careless driving. Especially, in the school
and the college zone, people are hesitating for decreasing the speed to its limit.
 This is embedded project to indicate the over speed and to control the vehicle in the over
speed condition. This is constructed with the wireless communication.
 We are using PIC16F877A which is Programmable IC microcontroller.
 In our system accident information system will alert vehicle owner relative or nearby
hospital through IoT with the accident location using GPS.
 If the accident is a minor one then driver can press the reset switch and drive normally.
 Accelerator, brake clutch and steering position sensor indicate the position of accelerator,
brake clutch steering respectively. We can monitor and control all with the help of IoT
module.

3. AIM & OBJECTIVE
 To monitor the accident and provide suitable information through
Internet of things.
 To record black box like investigation system in automobiles.
 To alert nearby people for inform to hospital, Police and relations
 To analyse the cause of accident and provide suitable information
for future investigations.
 To alert the occurrence of incident and thereby providing timely
help.
 To alert the status of the vehicle based on the maintenance via
sensors thereby preventing life loss.

4. INTRODUCTION
 Nowadays, the rate of accidents has increased rapidly. Due to employment, the usage of vehicles like
cars, bikes have increased, because of this reason the accidents can happen due to over speed.
 People are going under risk because of their over speed, due to unavailability of advanced techniques, the
rate of accidents can’t be decreased.
 To reduce the accident rate in the country this paper introduces a solution. Automatic accident
detection and alert systems are introduced. The main objective is to control the accidents by send a alert
to the hospital and police station using wireless communications techniques.
 When an accident occurs in a city or any place, the data is send to the IoT through cloud. PIC
Microcntroller is the heart of the system which helps in transferring the data to different devices in the
system.
 Vibration sensor will be activated when the accident occurs and the information is transferred to the IoT
through cloud. The GPS system will help in finding the location of the accident spot.
 The proposed system will check whether an accident has occurred and notify nearest medical centers
about the place of accident using GPS modules.
 The location can be sent through a tracking system to cover the geographical coordinates over the area.
The accident can be detected by a vibration sensor which is used as a major module in the system.

5. PROBLEM IDENTIFICATION
 Whenever accident being met, the nearby people call the
ambulance.
 The problem associated with this is that the victims depend on
the mercy of nearby people.
 There is a chance that there are no people nearby the accident
spot or people who are around neglects the accident.
 This is the flaw in the manual system.

6. S.N
o
PAPER TITLE
AUTHOR YEAR
OF PUB
JOURNAL METHODOLOGY
1 IoT Based Intelligent
System For Vehicle
Accident Prevention
And Detection At Real
Time
Vivek
Kinage; Piyus
h Patil
2019 Third International
conference on I-SMAC
(IoT in Social, Mobile,
Analytics and Cloud) (I-
SMAC)
In this paper is provides an efficient, cost-effective and real-time
solution to prevent vehicle accident. When reading goes beyond
predefined threshold values, an alert gets generated and if a driver
does not take some action in specified time then the system will
handle the situation by cutting the fuel supply. Our proposed system
uses a microcontroller named Arduino along with MQ-3 sensor,
infrared sensor, accelerometer, and webcam. Arduino is used to
regulate all these sensors.
2
Smart Characterization
of Vehicle Impact and
Accident Reporting
System
M Navin
Kumar; S
Pravin Kumar
2021
7th International
Conference on Advanced
Computing and
Communication Systems
(ICACCS)
The existing system uses four vibration sensors and a microcontroller
to detect the impact and identify if it is an accident or a minor
collision. When an accident occurs, this system determines whether
the accident is a rear-end collision, head-on collision, rollover, t-bone
impact or sandwich accident. Depending on the type of accident, the
number of ambulances required is decided. Then the accident
location is acquired using GPS module and SMS warning is sent to
the hospital using GSM modem. The SMS is composed of the
location, type of accident and required number of ambulances. Our
system facilitates urgent emergency assistance to all accident victims
in time.
6
Literature Survey

7. S.N
o
PAPER TITLE
AUTHOR YEAR
OF PUB
JOURNAL METHODOLOGY
4 DeepCrash: A Deep
Learning-Based
Internet of Vehicles
System for Head-On
and Single-Vehicle
Accident Detection
With Emergency
Notification
Wan-Jung
Chang
2019 IEEE Access This paper a deep learning-based Internet of Vehicles (IoV) system called
DeepCrash, which includes an in-vehicle infotainment (IVI) telematics
platform with a vehicle self-collision detection sensor and a front camera, a
cloud-based deep learning server, and a cloud-based management platform.
When a head-on or single-vehicle collision is detected, accident detection
information is uploaded to the cloud-based database server for self-collision
vehicle accident recognition, and a related emergency notification is
provided. The experimental results show that the accuracy of traffic
collision detection can reach 96% and that the average response time for
emergency-related announcements is approximately 7 s.
5
SUAV Image Mosaic
Based on
Rectification for Use
in Traffic Accident
Scene Diagramming
Qiang
Chen; Da
Li; Bin
Huang
2020
IEEE 3rd International
Conference of Safe
Production and
Informatization
(IICSPI)
In the system accident scene images collected by SUAV are preprocessed
and rectified through plane homograph. Then, two images are stitched into a
large view image through feature point matching and image fusion which
contains Harris corner detection and SIFT feature matching algorithm.
Finally, taken the accident mosaic image as background, the accident scene
map is drawn. Using this method, the scene information can be displayed
correctly, which is convenient for the traffic accident scene investigators to
draw the accident scene map, and ensure the complete panoramic
information collection of the whole scene.
6
Modeling of a Road
Traffic Accident
Using Multivariate
Analysis of Injuries
in a Two-Wheeled
Vehicle Collision
with a Car
S. A.
Evtyukov; I.
S. Brylev;
2021
Systems of Signals
Generating and
Processing in the Field
of on Board
Communications
In this study is to study the kinematics of the road accidents movement and
the factors influencing the degree of injury from collisions of a two-wheeled
vehicle with a car by modeling a road traffic accident. The analysis of the
effect of vehicle speed on the degree of injury for a certain moment of
collision has been carried out. A quantitative assessment is given to the
factors influencing the degree of injury from road accidents. Depending on
the vehicle speed, collision time, graphs were built and regression equations
were obtained.
7

8. EXISTING SYSTEM
 To this end, this paper deeply analyzed the risk propagation mechanism between vehicles
and proposed a novel secondary rear-end collision accident risk propagation model, which
could real-time evaluate vehicle rear-end collision risk.
 Then, the risk eld force is converted into risk probability through the hyperbolic tangent
function, and the vehicle operation interaction risk model is obtained.
 In addition, a risk propagation framework based on dynamic Bayesian network is
constructed to describe the propagation process of rear-end collision risk from the accident
vehicle to following vehicles.
 Finally, according to the probabilistic reasoning process of the framework, combined with
the accident vehicle risk, a secondary rear-end collision accident risk propagation model is
established.
 Simulation experiments show that the paper model can describe the evolution trend of rear-
end risk and the risk assessment results are more accurate. And after the accident, the rear-
end collision risk propagation speed will increase with the increase of trafc ow, and the
number of secondary rear-end vehicles will also increase with the increase of trafc speed.
DRAWBACKS
 Does not include real-time monitoring.
 Designed mainly for four wheelers

9. PROPOSED SYSEM
 In this project we are using accident detection unit which fitted the vibration sensor
in the vehicle.
 In case of accident, occurs if the vehicle is hit to some other vehicle or an object it
create some vibration in that case then the vibration sensor will detect the vibrating
signal and it pass the data to the PIC controller.
 PIC (16F877A) Microcntroller is used as a Central Processing Unit (CPU) of our
project.
 When the PIC (16F877A) Microcontroller receives a signal from vibration sensor it
immediately pass the data to IoT through the cloud.
 In this project we used reset button it will be used by the driver if the accident is
very normal in case the driver hit the wall in some situation like parking then the
driver will press the reset button this will inform the PIC controller will not send
GPS location.

10. Contd…..
 But if the driver is not in a situation to press the switch or if the
accident is really a major accident then the driver will not press the
reset button and then the system will send the current location of
vehicle to the IoT.
 Here, we use GPS modem to send location of vehicle to the family
members and the rescue team. Buzzer is also used to indicate as a
accident has been occurred which will create a beep sound.
 Thus the life of a person who met with an accident has been
identified and save their life too.

11. 1.Sensor selection: The first step is to identify the sensors required for monitoring the vehicle's speed,
driving behavior, and detecting accidents. This may include GPS, accelerometer, gyroscope, proximity
sensors, and others.
2.Hardware selection: Once the sensors have been identified, the next step is to select the appropriate
hardware for the data logger, including a microcontroller, wireless communication module, power
source, and memory storage.
3.Software development: The software development process involves creating the algorithms for
analyzing the data collected from the sensors and generating alerts or taking corrective actions. This may
involve machine learning or other advanced analytics techniques.
4.Data transmission: The data logger needs to be designed to transmit data wirelessly to a cloud-based
server, where it can be analyzed and used to improve driving behavior or take corrective action in the
event of an accident.
5.Alert generation and response: The data logger needs to be programmed to generate alerts if it detects
unsafe driving behavior or an accident. The system should also be designed to take corrective action
automatically, such as slowing down the vehicle or applying the brakes in the event of an accident.
6.User interface: A user interface is required for drivers to access information about their driving
behavior and receive alerts. This may include a mobile app or a web-based dashboard.
Algorithm

12. 1.Assemble the hardware components, including the sensors, microcontroller,
wireless communication module, and power source.
2.Develop the software, including algorithms for data analysis and alert generation.
3.Integrate the hardware and software.
4.Test the system to ensure that it is working as expected and refine
it based on the test results.
5.Deploy the system in the vehicles, including installing the hardware and training
the drivers on how to use the data logger and respond to alerts.
6.Monitor the system performance and make improvements as necessary to ensure
that it is effective in reducing accidents and improving driving behavior.
Implementation

13. ARCHITECTURE DIAGRAM
Micro controller
PIC (16F877A)
GPS Module
LCD Display
IoT MODULE
POWER SUPPLY FOR ALL UNITS
ACCIDENT SENSOR
RESET SWITCH
BUZZER

14. APPLICATION
Stolen Vehicle Recovery: Both consumer and commercial vehicles can be outfitted with RF or GPS units to allow police to do
tracking and recovery. In the case of LoJack, the police can activate the tracking unit in the vehicle directly and follow
tracking signals.
Fleet Management: When managing a fleet of vehicles, knowing the real-time location of all drivers allows management to
meet customer needs more efficiently. Whether it is delivery, service or other multi-vehicle enterprises, drivers now only
need a mobile phone with telephony or Internet connection to be inexpensively tracked by and dispatched efficiently.
Asset Tracking: Companies needing to track valuable assets for insurance or other monitoring purposes can now plot the real-
time asset location on a map and closely monitor movement and operating status.
Field Sales: Mobile sales professionals can access real-time locations. For example, in unfamiliar areas, they can locate
themselves as well as customers and prospects, get driving directions and add nearby last-minute appointments to
itineraries. Benefits include increased productivity, reduced driving time and increased time spent with customers and
prospects.
Transit Tracking: This is the temporary tracking of assets or cargoes from one point to another. Users will ensure that the
assets do not stop on route or do a U-Turn in order to ensure the security of the assets.