http://www.iaeme.com/IJMET/index.asp 493 [email protected]
International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 10, October 2017, pp. 493–501, Article ID: IJMET_08_10_054
Available online at http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=10
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication Scopus Indexed
DESIGN AND DEVELOPMENT OF VEHICLE
MONITORING SYSTEM THROUGH BLACK
BOX IN A CAR
X.Anitha Mary, R.Jegan, Lina Rose and P. Anantha Christu Raj
Department of Electrical Sciences, Karunya University, Coimbatore, India
ABSTRACT
This paper elaborate the collection of the real time data while driving a vehicle
and to check the driving behaviour and a car status, these data will be useful to
analyze the accident and to investigate about the accident. The whole system is vehicle
based devices which collect the data like speed, rash driving of a vehicle, GPS
position, alcohol detection of a driver over a time period. The data recorder will store
the values of all sensors which are interfaced with the controller. Then this
information will be transmitted over the wireless network. These data can be used to
investigate the crime, rescue operation and insurance claims also this data can be
transmitted to the Police station, Insurance Company thorough the GSM network.
Keywords: Event Data Recorder, Vehicle Speed, Rash driving, Alcohol detection,
Global Positioning System, Global System for Mobile.
Cite this Article: X.Anitha Mary, R.Jegan, Lina Rose and P. Anantha Christu Raj,
Design and Development of Vehicle Monitoring System through Black Box in a Car,
International Journal of Mechanical Engineering and Technology 8(10), 2017,
pp. 493–501.
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=10
1. INTRODUCTION
In recent days, improving safety driving is an important objective that has led many
organization and companies like vehicle manufacturers to invest significant amounts of
resources, mainly in improving road infrastructure and to reduce the car crashes [1]. However,
another crucial area of research is on analyzing the driver behavior.
Driver behavior and errors are the main reasons for a majority of car crashes, so
understanding the driver’s behavior is essential in accident analysis, emergency medical
service and insurance settlements. Reasoning-based framework for the monitoring of driving
safety will not be practically possible in real time [2]. Like Black Box of airplane, Car Black
Box (Event Data Recorder) is used to store the driver’s behavior and car status to analyze the
factors for an accident. Recently, Event data recorders (EDR) have emerged as a new system
to collect data on driving behavior and to provide feedback to drivers continuously [3].
Design and Development of Vehicle Monitoring System through Black Box in a Car
http://www.iaeme.com/IJMET/index.asp 494 [email protected]
Peter I.J. Wouters [11] was aimed at assessing the effect of ‘behavioural feedback’ using
data recorders. Event Data Recorder are on-board and integrated with wireless devices that
collect and record the information. It can be used to improve design of vehicle and Roadway
and emergency medical service by government and hospital. Also the stored information is
used to evaluate and improve safety equipment and to investigate crash causes. Oren
Musicant’s work [4] focuses the Segregating diver’s behavior as aggressive and non-
aggressive [14] driving behavior. Several researches are going on the development of systems
that monitor driver’s behavior continuously. This data also will be used for traffic safety
research.
2. BLACK BOX IN A CAR
Event Data Recorder (EDR) of a Car is also known as a Black Box, which is used to record
information related to accidents. Car black box records the important data so that it can be
used to analyse the accident. In addition to the basic function, the car black box equipped with
wireless communication system can send accident location information to central emergency
and disaster server in real-time. Therefore drivers can receive the help quickly by police and
hospital ambulance. But in Bao Rong Chang’s [7] work inter- vehicle communication is
encouraged to avoid the accident, there is no rescue method for the driver who met an
accident. The proposed system consists of data collection devices for collecting the
information about car’s status and the driver's actions, a LabVIEW platform is used to create a
database server and this database collect the sensor values from microcontroller through USB.
Overview of the proposed system can be subdivided into various sections:
2.1 Data collection
• Behaviour of driver: Drowsiness and alcohol detection of a driver will be detected by
using the web camera.
• Driving data: Driving information such as speed, rash driving.
• Car status data: The car positions checked in real time by GPS.
2.2 Report Generation
Analyse the accident easily and to handle many problems related to car accident like crash
litigation, insurance settlements. The database is maintained at the LabVIEW platform and the
data will be report to the police station /insurance company through the GSM network. So this
system resists the fake data to be record in the police station or in insurance settlement [16]
[18].
3. PROPOSED SYSTEM MODEL
Figure 1 Transmitter End section of Black box in a car
X.Anitha Mary, R.Jegan, Lina Rose and P. Anantha Christu Raj
http://www.iaeme.com/IJMET/index.asp 495 [email protected]
Figure 2 Receiver end section of Black box in a car
Figure 1 and Figure 2 represents the block diagram of a transmitter and receiver section of
our car Black box respectively. In the proposed system we divide the whole system as two
section, they are
1. Event Data Recorder section (Transmitter)
2. Reporting section (Receiver)
EDR section deals with the sensors, which is interfaced with the ATmega16
microcontroller. By using the sensors like Hall Effect, Accelerometer and MQ3 gas sensors
we are detecting speed of the vehicle, rash driving status and driver’s alcohol detecting status.
The status of these sensors will be displayed on the 2*16 LCD, also these data will be sent to
the database of LabVIEW environment. The data transfer between the microcontroller and
LabVIEW environment process through the USB cable. A new breath alcohol detector [5] for
a driver has been introduced in this proposed system. A mouthpiece is not required for the
detection because driver’s breath sample is captured by an electric suction fan of the detector.
Yulan Liang’s [6] work focuses on the detection of driver’s distraction using the eye
movement of a driver. In proposed system driver’s drowsiness will be detected by using the
video processing algorithm called Viola-Jones algorithm. Webcam is used to monitor the
actions of a driver and Viola-Jones algorithm will keep tracking our eyes. If a driver’s eye still
closes after 5 to 7 seconds, the algorithm will concluded that the driver is sleeping. So this
Drowsiness status and sensor status like speed, rash driving and alcohol detection will be
updated in a database which is created at the LabVIEW platform. In addition, the proposed
system records the current latitude and longitude of our vehicle using the GPS module [18]. If
any severe accident or crash occurs, suddenly the status of a car and the driver will be send to
the reporting section through GSM module [17].
Reporting section like Police station, Insurance Company, Hospital will receive the
driver’s behaviour and car’s status after an accident occurred. This section consists of a
mobile and GSM receiver. The necessary action will be taken by the receiver according to its
received data through the GSM network.
4. EXPERIMENTAL DESCRIPTION
4.1 ATmega16 Controller
ATmega16 is an 8-bit high performance microcontroller with low power consumption. This
microcontroller will operate at 16MHz operating frequency. ATmega16 is a 40 pin
microcontroller. And 32 I/O lines are available, which are divided into four 8-bit ports as
PORTA, PORTB, PORTC and PORTD. It has various peripherals like UART, ADC, SPI,
I2C, etc. Every pin has an alternative function related to in-built peripherals. This controller is
used in the proposed system to acquire the necessary signals from the different sensors like
Hall Effect, accelerometer, and alcohol detector.
Design and Development of Vehicle Monitoring System through Black Box in a Car
http://www.iaeme.com/IJMET/index.asp 496 [email protected]
4.2 Speed detection
The Hall Effect is an ideal sensing technology. When the sensor experienced the magnetic
field, it provides an output voltage proportional to the magnetic field strength. The ATmega16
is then programmed to get the input signal on T0 pin. The pulse will be given to the counter
input. So the Input Counter is incremented for each time a pulse is detected on a particular
pin. In order to find the Revolution per Minute (RPM) of a wheel, we must count the pulses
for one minute which is raised from the Hall Effect sensor. Software interrupt is created for
one minute and the pulses are counted at that period. Then the product of RPM and wheel
circumferences will give the speed of a vehicle in meter per seconds. Every three seconds a
new value is displayed on the LCD.
V = C * rpm(V=speed) (1)
C=2*pi*r (C=circumferences of wheel) (2)
V=pi*d*rpm (m/s) (3)
4.3 Rash driving detection
An Accelerometer is a device that measures the acceleration as “G-force”. Accelerometer at
rest on the surface of the Earth will measure an acceleration g= 9.81 m/s2. Accelerometers
have different applications in industry products and research. Highly sensitive accelerometers
are components of navigation systems for aircraft and missiles [10]. The proposed system is
developed to detect the Rash driving of a vehicle by using the MPU6050 accelerometer [9],
which is interfaced with ATmega16 microcontroller. The controller continuously reads the x,
y and z axis values of a car. These values are recorded and the vehicle orientation is tracked
on every minute [19]. The proposed system will judge the rash driving status of a vehicle
from these x, y, and z-axis values [20].
4.4 Driver’s drunkenness detection
In this proposed system driver’s breathalyser is very essential. Most of an accident happens
because of the driver who consumes alcohol [17]. In accident analysis driver’s drunkenness is
the important factor for the insurance market and the police record. MQ3 sensor is an alcohol
sensor, which detects ethanol in the air. It is one of the gas sensors so it works almost the
same way with other gas sensors. Typically, it is used as part of the breathalysers for the
detection of ethanol in the human breath. From the analysis of ethanol concentration in
human’s breath, the drunkenness status can be determined.
4.5 Drowsiness detection
Driver’s drowsiness status also should be taking into the consideration while recording the
driver’s behavior [5]. Driver cannot control the vehicle if he falls on sleep. So the drowsiness
detection of a driver can be determined based on the pattern matching algorithm. Yulan
Liang’s [7] work focuses the support vector machines (SVMs), which is a data mining
method, to develop a real-time approach for detecting cognitive distraction using drivers’ eye
movements and driving performance data.The real time image is acquired by a camera
through the vision acquisition tool on LabVIEW. Then vision assistant tool in a LabVIEW is
used to detect the drowsiness of a driver by applying the pattern matching algorithm over an
real time image. Here Region of Interest is taken to track the driver’s eye over an real time
image. Before storing or training the template for pattern matching, we should apply filter and
proper resolution on the template which is going to compare with the real time image. This
system will continuously acquire the real time image of driver from a web camera and it will
compare with the template, if the real time image doesn’t match with the template, then the
system will conclude whether the person is sleeping or not.
X.Anitha Mary, R.Jegan, Lina Rose and P. Anantha Christu Raj
http://www.iaeme.com/IJMET/index.asp 497 [email protected]
5. RESULTS AND DISCUSSIONS
Prototype of the system for detecting the driver’s behavior was developed and the data
transferred to the system was done through the serial protocol. Further prototype should be
equip with the database which is created on LabVIEW platform and it should store and update
all the accumulated the data like drowsiness detection, speed, rash driving, GPS location and
alcohol detection. Figure4 shows the experimental setup to measure the RPM of a vehicle.
This system is constructed by using the Hall Effect sensor and ATmega16 Microcontroller.
Figure 3 shows the flowchart for drowsiness detection.
Figure 3 Flow chart of drowsiness detection
Figure 4 shows the interfacing of Hall Effect sensor with microcontroller and figure 5
shows the interfacing of Alcohol detection sensor with microcontroller. Figure 6 shows the
interfacing of Accelerometer sensor with microcontroller.
Figure 4 Interfacing Hall effect sensor with Microcontroller
Design and Development of Vehicle Monitoring System through Black Box in a Car
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Driver’s alcohol consumption can be detected by using the system which is shown in
below as Figure5. This system is also called as Breath analyzer and it is constructed by MQ3
sensor and Microcontroller.
Figure 5 Alcohol detection sensor with ATmega16 Microcontoller
Rash driving of a car is predicted by using the MPU6050 accelerometer. While driving a
car, we can read the values of the x, y and z axis values from the accelerometer. From these
values we can conclude the rash driving status of a car. Rash driving is depending on the
environment and the traffic rules which is formulated by the corporation. Accelerometer
interface with the controller is shown in below as figure6. This system is handling the rash
driving related status.
Figure 6 Accelerometer sensor with ATmega16
The vehicle section is shown in Figure7. Here all the sensors Hall Effect, alcohol detector,
accelerometer are interface with the microcontroller and this system is placed on the toy car,
which is modeled for the prototype of the real car.
Figure 7 Vehicle section with controller and sensors
X.Anitha Mary, R.Jegan, Lina Rose and P. Anantha Christu Raj
http://www.iaeme.com/IJMET/index.asp 499 [email protected]
Overall data received from the sensor and microcontroller is transmitted to the computer
by serially through the USB. These data further going to be stored and updated every second
in the database, which is created in LabVIEW environment. Figure 8 shows the overall
database on LabVIEW environment.
Figure 8 Database on LabVIEW
Figure 9 and Figure10 shows the driver’s drowsiness status on the LabVIEW front panel.
It shows the result by using the pattern matching algorithm.
Figure 9 Drowsiness status front panel on LabVIEW for Opened Eye
Figure10 Drowsiness status front panel on LabVIEW for Closed Eye
Design and Development of Vehicle Monitoring System through Black Box in a Car
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6. CONCLUSIONS
Event Data Recorder (EDR) systems provides useful information to analyze the driver’s
behavior in real driving situations. This system has been effectively constructed by using the
different type of sensors like Hall Effect, Accelerometer, Alcohol detector, etc. ATmega16
microcontroller is used to acquire the signals from these sensors. These data are
stored/updated in memory card which is interfaced with the controller. Through GSM, only
the critical information are send to the reporting sections like insurance companies, hospital
and police station. The memory card database is created with LabVIEW for experimental
purpose. This paper also detects the drowsiness of the driver. This system ensures safe driving
and avoids fake insurance claim.
REFERENCES
[1] Antonio perez, M.Isabel Garcia, Manuel Nieto, Jose L. Pedraza, Santiago Rodriguez, and
Juan Zamorango Argos: An Advanced In-Vehicle Data Recorder on a Massively
Sensorized Vehicle for Car Driver Behaviour Experiment IEEE Transactions on
Intelligent transportation Systems,11(2), June 2010.
[2] Bing-Fei Wu, Fellow, IEEE, Ying-Han Chen, Chung-Hsuan Yeh, and Yen-Feng Li
Reasoning-Based Framework for Driving Safety Monitoring Using Driving Event
Recognition, IEEE Transactions On Intelligent Transportation Systems.
[3] Tomer Toledo, Oren Musicant, Tsippy Lotan In-vehicle data recorders for monitoring and
feedback on drivers behavior Science direct Transportation Research Part C 16 (2008)
320–331.
[4] Oren Musicant, Hillel Bar-Gera, Edna Schechtmanm Electronic records of undesirable
driving events Science direct Transportation Research Part F 13 (2010) 71–79.
[5] Luis M. Bergasa, Jes´us Nuevo, Miguel A. Sotelo, Rafael Barea, and Elena Lopez, Visual
Monitoring of Driver Inattention Springer Comput. Intel. in Automotive Applications, SCI
132, pp. 25–51.
[6] Kiyomi Sakakibara, Toshiyuki Taguchi, Atsushi Nakashima and Toshihiro Wakita,
Toyota Central R&D Labs.,Inc. Shohei Yabu and Bunji Atsumi, Toyota Motor
Corporation Development of a New Breath Alcohol Detector without Mouthpiece to
Prevent Alcohol-Impaired Driving Proceedings of the 2008 IEEE International
Conference on Vehicular Electronics and Safety Columbus, OH, USA. September 22-24,
2008.
[7] Yulan Liang, Michelle L. Reyes, and John D. Lee Real-Time Detection of Driver
Cognitive Distraction Using Support Vector Machines IEEE Transactions on Intelligent
Transportation systems, Vol. 8, NO. 2, June 2007.
[8] Bao Rong Chang, Hsiu Fen Tsai, Chung-Ping Young Intelligent data fusion system for
predicting vehicle collision warning using vision/GPS sensing Science direct Expert
Systems with Applications 37 (2010) 2439–2450.
[9] Oscar S. Siordia, Isaac Martin de Diego, Cristina Conde, and Enrique Cabello Wireless
In-vehicle Complaint Black Box, IEEE vehicular technology magazine September 2012.
[10] Channabasavaiah M S, Shrinivas Mayya D Automated Control System for Emission
Level & Rash Driving Detection in Vehicles International Journal of Electrical and
Electronics Research ISSN 2348 6988 (online) Vol. 2, Issue 2, pp: (96-99), Month: April -
June 2014.
[11] Peter I.J. Wouters, John M.J. Bos Traffic accident reduction by monitoring driver
behavior with in-car data recorders PERGAMON Accident Analysis and Prevention 32
(2000) 643–650.
X.Anitha Mary, R.Jegan, Lina Rose and P. Anantha Christu Raj
http://www.iaeme.com/IJMET/index.asp 501 [email protected]
[12] Johnson, D.A., Trivedi, M.M. Driving Style Recognition using a smartphone as a sensor
platform, IEEE 14th International Conference on Intelligent Transportation system,
October(2011).
[13] Dai, J., Tang, J., Bai, X., Shen, Z., Xuan, D. Mobile phone based drunk driving Detection
Proc. 4th Int. Conf. Pervasive Health no Permissions, pp. 18 (2010).
[14] Zhang, Y., Lin, W., Chin, Y.K. A pattern-recognition approach for driving skill
Characterization IEEE Trans. Intell. Transp. Syst., vol. 11, no. 4, pp. 905916 (2010).
[15] Sathyanarayana, A., Boyraz, P., Hansen, J.H.L. Driver behavior analysis and route
recognition by hidden Markov models Vehicular Electronics and Safety, ICVES,IEEE
International Conference on IEEE (2008).
[16] Chigurupa, S., Polavarap, S., Kancherla,Y., Nikhath, K.A, Integrated Computing System
for measuring Driver Safety Index, International Journal of Emerging Technology and
Advanced Engineering, ISSN 2250-2459, Volume 2 (2012).
[17] Lilia Filipova-Neumann, Peter Welzel Reducing asymmetric information in insurance
markets: Cars with black boxes Science direct Telematics and Informatics, 27, (2010),
pp. 394-403.
[18] Amit vinchurkar, Jayesh Bramhankar, Krunal Tupkar, and Rahul Nichal, Rajni Rajak,
Rashmi Deshmukh, January 2014, Vehicle Security System Using GSM Modem And
Alcohol Detection System, International Journal of Computer Science and Management
Research,vol.3, Issue 1, pp. 3780-3782.
[19] Nirav thakor, Tanmay vyas, Divyang shah, December 2013, Automatic Vehicle Accident
Detection System Based on ARM &GPS, International Journal for Research in
Technological Studies, (online),Vol. 2, Issue 1, pp. 17-19.
[20] Md Ateeq ur Raheman, K. Jyotsna, V. Naveen Kumar, P. Sai Kumar, Driver Alert System
Using Accelerometer and MSP430, International Journal For Advance Research In
Engineering And Technology Vol. 1, Issue IX, Oct.2013 ISSN 2320-6802.
[21] Gezim Hoxha, Ahmet Shala, Ramë Likaj, Pedestrian Crash Model for Vehicle Speed
Calculation at Road Accident. International Journal of Civil Engineering and Technology,
8(9), 2017, pp. 1093–1099.
[22] E.T. Mukti, A. Sjafruddin, A. Kusumawati and S. S. Wibowo, Analysis Model of Vehicle
Operation Speed Prediction of Light Vehicle On 2/2 UD Interurban Road In Indonesia,
International Journal of Civil Engineering and Technology, 8(10), 2017, pp. 735–742.
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