International Journal of Engineering Trends and Technology (IJETT) – Volume 18 Number3 - Dec 2014
Safe Driving Using Android Based Device
Shubhangi Pargaonkar#1 ,Diksha Jawalkar#2 ,Reshma Chate#3 ,Shivani Sangle#4 ,M.D.Umale#5, S.S.Awate#6
1234
Student, 56Asst.Prof, Computer Dept., DCOER, Savitribai Phule Pune University, India
Abstract— In the present world we all do see the rise in road
accidents and also experience that very few measures are taken
to avoid such accidents before they occur. Global status report
on Road safety, 2013[5] shows that India is one of the countries
where highest number of road accident cases and injuries are
found. All efforts made so far regarding road safety with the help
of Advance Driver-Assistance Systems (ADASs) has not been
much helpful to make any driver aware of the road conditions.
Mobile phones especially smart phones presently are well
equipped with efficient sensors and other many features that can
together form a portable device to monitor road conditions. In
this paper, we tend to use Android-based Smartphone with its
internal accelerometer to analyze various road conditions and
driving behaviors. With its effective data generation we try to
increase driver awareness to operate any vehicle and also give a
clear idea about the roads they may travel.
Keywords— Mobile phones, Road safety, Accelerometer, Road
anomalies.
I. INTRODUCTION
Seeing today‟s lifestyle, we realize that people
mainly focus on reaching any destination with
quick speed and comfort. However, the world in
rush is moving farther away from „living‟ peace and
being pushed towards peace of graveyard. The
major factors that contribute to maximum road
accidents are hazardous road conditions and sudden
vehicle maneuvers that are unknown to any person
behind the wheel.
Reducing such road accidents is possible by
proper monitoring of road anomalies (potholes,
bumps, smooth, rough and uneven roads) and
studying different driving behaviors (speed and
shifting along with acceleration and deceleration).
Increasing safety and thereby saving lives is one of
the key features in Advance Driver Assistance
System (ADAS) which is an important component
in Intelligent Transportation System (ITS).Sadly
most of the vehicles manufactured with useful
ADAS packages are not any cheap add-ons and
require heavy sensors.
Using an Android-based mobile device for such
purposes can be considered as a new approach that
equally contributes to minimize cost burden. We
will first discuss some prior work done in the field
of road anomalies detection using a mobile phone.
ISSN: 2231-5381
The Nericell [1] is a system whose sensing
functions are supported by participation of a mobile
device; the system includes honk detection and
localization for traffic conditions and examines few
road conditions like bumps, potholes, and vehicle
braking using external sensor. There has been some
previous work on detection of driving behaviour [2]
which compares normal driving behaviour with
typical drunk driving patterns and makes use of
mobile phones as alert giving component. Also
there is a hand-held real-time [3] Lane Departure
Warning System (LDWS) which can easily be
mounted on any vehicle to improve driver safety.
So when we deeply study and observe these
mentioned techniques in this area of road safety we
come to know that they include sensors that record
data appropriately but show less accuracy using
poor warning system for collision avoidance and/or
show no proper generation of recorded data.
We use a multipurpose device that is an android
based smart phone that includes Global Positioning
System (GPS), microphones and accelerometer that
collectively act and help to advise the vehicle
operator on harmful external conditions and road
anomalies that occur due to environmental factors.
From Nericell system we get to understand that the
device location and orientation should be
configured properly inside the car for specific
accurate readings. Google Earth is used to create
maps of road condition using GPS coordinates.
Such pictorial representation of the recorded data
helps to easily understand vehicle operation and
driving behaviour in different situations and gives
awareness regarding the roads or say routes to be
travelled.
II. EXISTING METHODS
A. Nericell System
Nericell word is taken from „Nerisal‟ which is a
Tamil word for defining congestion. This system
gathers information through rich sensing
functionalities and provides solutions for
congestions occurring on roads. It makes use of
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International Journal of Engineering Trends and Technology (IJETT) – Volume 18 Number3 - Dec 2014
external Sparkfun WiTilt accelerometer, relies on
GSM radio based localization that helps to trigger
GPS to obtain accurate locations, microphones or
GPS sensors all together on a smart phone to detect
braking activity, honking and different road
anomalies like bumps and potholes are also
identified. But it fails to notify or detect whether
roads are smooth, uneven or rough. Such additional
complexities of roads are easily classified and
shown by our system. Nericell consists of very
complicated hardware and software setup. Some of
the experiments carried out by Nericell system in
India makes the use of virtual reorientation
algorithm that runs on HP iPAQ Smartphone
running Windows Mobile 5.0.This algorithm is
supported with measurements collected from
external accelerometer via a serial port interface on
its Bluetooth radio. But the energy savings is a
complex issue which depends on specific additional
settings to be made in this system.
Nericell mostly focuses on traffic conditions. The
recorded result in this system is conveyed to a
service in the cloud. This cloud service is for
aggregation and reporting. In this way we
understand that Nericell does not cover all the
necessary aspects for road safety.
B. Mobile Phone Based Drunk Driving Detection Method
Here the posture of the device is decided by the
orientation angles yaw, pitch and roll. This tends to
increase the working and puts a burden on the
functioning of the mobile. Whereas our system only
uses simple three-axis (x, y, and z) accelerometer
for overall recording and generation of different
driving behaviors. Here the mobile device also acts
as the warning component but fails to give any type
of alert if its battery life comes to an end or any
internal error occurs. We realize that the system
totally collapses if any one module stops working.
We also understand that the system may fail to
avoid accidents if no positive response is given by
the driver on time.
C. Lane Departure Warning System
Lane Departure Warning System (LDWS)
provides results for improving driving safety. The
system is an embedded real-time LDWS which is
based on ARM (Advanced RISC Machines). It
helps to show two situations one is when the driver
gets too close towards the lane boundaries or
secondly when the respective vehicle approaches
any lane boundaries in too fast manner. The main
error of this system is that it focuses only on lanes
and not road conditions like sharp tortuous paths or
other such non-flat roads which can be equally
dangerous.
In this system the Lane Departure Detection is
based on Spatial and Temporal mechanism which
requires many formulas. It also requires
maintenance of hundreds of frames via video clips
from Liquid-Crystal Display (LCD), since it uses a
Frame-based Day Time and Night Time evaluation.
For its hardware there is different core architecture
and software setup of the system has different
input/output module, storage and power supply
module. This adds too many limitations and does
not allow the system to present a single portable
device. The system performs poorly at night time
due to lack of sufficient illumination.
This particular system mainly aims at early
detection of dangerous driving habits typically
related to the drunk drivers and it accurately alerts
such drivers of making dangerous moves or at times
makes a call to the police for help. For experimental
results this system has tested and categorized
regular and drunk driving behaviors. The
experiments of this system require a vast range of
test sets to be carried out for both types of
behaviors. The maintenance and processing of such
large information requires lots of time and
complicates the analysis. It makes use of Android
G1 phone for implementation. There is a detection
algorithm applied to the mobile device which is
based on different latitude and longitude
III. PROPOSED WORK
accelerations, it uses additional calibration
procedure to solve the problem related to position
By our proposed system we try to present an easy
of the device. Both orientation sensor and
remedy
for uprooting some of the above drawbacks
accelerometer are used from the mobile device.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 18 Number3 - Dec 2014
of the previous systems. Our effort is to introduce a
widely used, easy to handle, real-time portable
device which helps to minimize the costs and
improve the road safety.
A. System Overview
Proposed system requires dedicated sensors
present on smart phones. If any movement is
detected it is numerically analysed and can be
shown in specific directions. Driving maneuvers are
found and differentiated by use of each individual
axis in the embedded accelerometer. When such
values are recorded, further we need to extract the
location. This is done by the GPS latitude and
longitude coordinates which are then sent to the
respective server. Maps are easy to read and can be
understood by any type of driver before beginning
the journey or on the way. After getting the road
conditions, exact GPS based location can be found
out and simultaneously can be mapped on the
Google Earth. So for any user travelling through a
new route such representation of recorded data can
help to improve safety.
Program Interface (API) contains many functions
and classes to control the cellular devices. In
general the phone and its (x,y,z) axes could be in an
arbitrary orientation with respect to the vehicle and
its (X,Y,Z) axes. We take the accelerometer value
for a single GPS value and denote the
accelerometer value as a segment of a particular
area. The orientation of the phone is fixed in
specific positions. Experiments carried out for this
suggest, for road condition analysis it must be
firmly secured to the floor board of the front
passenger section. There were 1-5 locations in
which the phone was tested to measure driving
maneuvers.
In an embedded accelerometer the x-axis
identifies the left/right directions which can be used
to indicate turning/lane changes, similarly the yaxis identifies front/rear directions in a car to detect
acceleration and braking and z-axis detecting
up/down directions to identify sharp vibrations or
different road anomalies on the roads. We also
make use of Java which is well suited to help
software developers to meet the challenges and
seize opportunities, because java was mainly
designed for networks. Differentiating a pothole
from a bump or any such comparison of the
different road anomalies is possible by setting
specific threshold values for each accelerometer
axis.
Fig. 2 Phone orientation and location inside a car [4]
Fig. 1 System Overview for Safe driving using android-based device
The device should have Wi-Fi through which it
will be possible to connect to the server. Network
We include the usage of an android-based device
gives the functionality to login and registration
Smartphone which is easy to handle by all users and
facility. With the use of Wi-Fi network we plan to
consists of some effective sensors which helps to
create our own communication protocol. Google
reduce the cost burden. The Android Application
Map Handler will help to generate road map for
B. Overall Setup
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International Journal of Engineering Trends and Technology (IJETT) – Volume 18 Number3 - Dec 2014
safety. We make use of a color code technique
which is assigned to certain interpolated values for
segments. For example Red color can be used to
represent bumps, blue color to represent pothole,
smooth road can be shown by green color and so on.
One of the experiments carried out obtained 85.6%
accuracy for overall road anomaly classification
system. The user of this product/system can be any
citizen who has an android phone.
bumps, potholes, smooth, uneven, rough roads with
the help of simple embedded accelerometer. Along
with these findings we also try to advice on safe
acceleration and sudden lane changes. Visual
representation of all such road situations is made
easy by usage of Google maps which are widely
used. This helps to easily improve overall driving
experience in today‟s rushing lifestyle. In future
scope we plan to extend our work to auditory alerts
of these factors.
IV. CONCLUSION AND FUTURE WORK
REFERENCES
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It helps to educate the driver and make aware about
new routes with its overall integrity. Our work
reveals the roads to be more complex .We identify
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