Abstract— driving has become most common habit between

Abstract— Recently,
 many interesting
systems on mobile phone are introduced to prevent the dangerous activity of
texting while driving (T&D). However these systems require user’s manual
input to block the texting activity or some extra devices are used to identify
user’s location and also some localization techniques are used. Here the idea
is to propose a method which is able to detect T&D without any user
intervention. To implement this idea various smart phone embedded sensors such
as gyroscopes, accelerometers and GPS are used. They are used to collect the
information such as touch strokes, holding orientation and vehicle speed. Using
these patterns the whether the T&D is existing or not identified.

words— Vehicles, Smart
phones, Global Positioning System, Accelerometers.

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phones are small computers which uses android as operating system. Data
communication between the people/user has become faster using smart phones.
Calling and texting otherwise called as messaging is the most important and
basic activity that has been done using smart phones. But texting while driving
has become most common habit between the drivers. This is one of the most
hazardous thing performed by the users of smart phone. This act becomes major
distraction from driving. This leads to road accidents and causes damage to the
drivers and also other common people. Usage of mobile phones while driving can
affect your alertness of surroundings, which leads to accidents. This term in
short is referred to as T&D that is Texting while Driving. This is
considered as one of the top most dangerous activity done by the drivers. A
research says that, the drivers who send messages while driving are 23 times
more likely to experience a crash on others when compared to other drivers who
are dialing, talking or listening. Even though, many laws are imposed to avoid
T&D, the users are involving in this activity. To stop these activity of
drivers and to identify the T&D, many mobile apps are now developed and
practised between the users.

system does not require any manual input from the driver. This acts as the
major advantage of this over other T&D system. In other detection system
the major problem is they require user input or activation or it either
disables all the mobile phones in the user location (such as car) , this causes
unnecessary inconvenience to the users. The key challenge is detecting whether
the mobile phone belongs to driver or passenger. For this purpose, some systems
uses camera to monitor driver’s activities. But this method is infrastructure
heavy and required high hardware facilities to implement real time video
processing. Some of the works are done for detecting T based
localization. Here some extra devices in combination with mobile phone work
collaboratively to determine the user location. To enlighten this idea most
successfully a method which does not require any extra devices except user’s
mobile phone was needed.  Here are some
patters that is used to distinguish between the driver and the passenger.

Editing messages after the car speed is decreased.

2: Stop editing when the car is taking turns.

3: Holding the mobile phone uprightly while editing the messages.

by using this system the T&D patterns are identified by the data collected
the smart phones and the other embedded sensors like accelerometers, gyroscopes
and GPS. The other important factor is that, this system does not try to read
the content of the messages. Therefore privacy is preserved here. 


existing system to prevent T&D there are two categories, the first one is
which simply blocks all the messages from the user end. The best example for
this is driver mode or bike mode available in the mobile phones. But this
existing system does not identify the drivers  mobile with the passengers. 

next category is identifying whether the mobile is used drivers or passengers
and it blocks the driver’s mobile only. In this method identifying the location
of the mobile phone in the vehicle is important. For this localization
techniques are used to identify the driver’s location. However this approach
has some drawbacks that identifying the driver’s location requires some special
sensors and required high deployment cost. Identification of driver’s location
and driving patters in also infrastructure heavy and intrusive.

for overcoming these drawbacks infrastructure-less approach is required. That
is identifying the driver’s usage of mobile without any extra devises except
his/her own mobile phone.


Here the touch strokes
are identified by using the gyroscope data. Using the training data set a touch
stroke template is constructed. On this gyroscope data the template is utilized
as wavelet basis and they carry out wavelet transform. According to the
occurrence of touch strokes the significant peaks and the location of the peaks
are selected.


1SAFECAM: Analyzing
intersection related driver behaviours using multi sensor smart phones

uses embedded sensors on the phone .It is used in tracking different driving
conditions. It also makes use of vision based algorithms to detect critical
driving behaviours including taking unsafe turns. The results demonstrate that
the safe cam is effective in detecting real-road driving environments and alert
drivers during dangerous situation.

2Leveraging smart phones
cameras for collaborative road advisories

provides a effective driver assistance services using ever richer set of
sensors. It provides assistance for the drivers in services like traffic
advisory and road condition monitoring .This paper introduce a sensing platform
called as windshield-mounted smart phones. It uses a prototype called as signal
guru, it predicts the schedule of traffic signals and also enabling Green Light
Optimal Speed Advisory (GLOSA)

3Driving style recognition
using smart phone as a sensor platform

this system, a novel method that uses dynamic time warping (DTW) and smart
phone based sensor-fusion is used. They detect and recognize the driver actions
without external processing. It performs the pattern recognition research by
combining the inter-axial data from multiple sensors into a single one that is
single classifier.

4Drive safe: an app for
alerting inattentive drivers and scoring driving behaviours

application uses computer vision and pattern recognition techniques to alert
during their unsafe driving behaviours. The inbuilt sensors present are able to
detect in attentive driving behaviours and also evaluating the quality of
driving at the same time. It produces sound alarms in case of unsafe driving.
It becomes the first application for smart phones to detect the driving

5Detection of dangerous cornering
in GNSS –Data driven insurance telematics

application introduces loss functions designed for applications. It mainly aims
to minimize the number of missed detections and false alarms, this estimates
the risk level in each turning and cornering event. This estimation only uses
GNSS (Global Navigation Satellite System) measurements. It also supports
real-time value added services.


6Safe driving using mobile

this paper the system available makes use of three axis accelerometer of
android based smart phone. It records and analyzes various driver behaviours.
It also alerts about the external road conditions that would be hazardous for
the drivers. By using the real-time analysis and auditory, we can increase the
driver’s overall awareness.

7Sensing driver phone use with
acoustic ranging through car speakers

this system the fundamentally addressed problem is differentiating driver’s and
passenger’s mobile phone. In this system, it leverages the stereo
infrastructure, particularly speakers and Bluetooth. Using car stereo, a customized
high frequency beeps are produced. Sequential change-point detection scheme is
used to time the arrival and phone’s distance from the car’s centre is

8Driver behaviour analyses for
safe driving

paper provides some well-established techniques for driver’s inattention
monitoring and recent solutions for exploiting mobile technologies such as
smart phone and wearable devices .It primary aim is safe driving and uses
active systems for car-to-car communication to support vehicle adhoc network VANET

9Determining driver phone use
by exploiting smart phone integrated sensors

system uses embedded sensors in smart phones for capturing the centripetal
acceleration. Their differences are obtained. This is a low infrastructure
approach. It has different turn size and difference in the driving speed. This
method is adopted in many traffic related safety applications.

10Detecting driver’s smart
phone usage via non intrusively sensing driving dynamics

in this paper TEXIVE is implemented
which detects the texting operations during driving. It also notices the
irregularities and rich micro-movements of the users. TEXIVE is very accurate
that the dangerous driving behaviours are identified without any extra devices
rather than smart phone.

11Automatic identification of
driver’s smart phone exploiting common vehicle riding actions

paper gives the solution to the problem of distracted driving by giving a
event-driven solution called as Automatic Identification of Driver’s Smart
phone (AIDS).It makes use of features that are available for identifying the
driver’s phone. It uses electromagnetic field spikes to differentiate driver’s
phone from the passengers in the car.

12Improved vehicle steering
pattern recognition by using selected sensor data

this paper recognition accuracy of vehicle steering patterns are improved. This
paper presents a new method to reduce both energy and computation complexity.
Different statistical modes are identified and statistical sensor features are
reflected. Real-time accuracy is produced in steering modes. Different machine
learning modes are compared to produce improved classifier training.

13Full auto-Calibration of a
smart phone on board a vehicle using IMU and GPS embedded sensors

a study is conducted, to identify how powerful some of the low cost sensors
life IMU (Inertial Measurement Unit) and GPS are useful for intelligent
vehicles. The relation between the smart phone reference system and vehicle
reference system are identified by accelerometer and gyroscope. Based on
longitudinal vertical acceleration on automatic method is proposed to calibrate
a smart phone using IMU and GPS. Here filter algorithm is used to decrease the
impact of IMU noise.

14Smart phone-based sensor
fusion for improved vehicular navigation

fusion sensor data on smart phone a system for car navigation is proposed here.
To support GPS, both the internal sensor and care sensors are used. To fuse the
data from different sensor’s streams strap down algorithm and Kalman filters
are used. The speed information of the car is give by car sensors and gyroscope
act as internal sensor.

15Smart phone-based adaptive
driving maneuver detection: A large-scale evaluation study

paper, builds a statistical model of the driver, vehicle and the smart phone
and an adaptive driving maneuver detection methods is proposed. This
combination is done using multivariate normal model. To detect risky driving
behaviours a mechanism is know as training mechanism is adopted. I adapts
profiling model to the driver and road topology.

16Detecting driver distraction
using smart phone

this paper a ubiquitous camera feature is used to differentiated driver’s phone
from the passengers. Here the key factor is non-intrusive detection of the
smart phone. No manual input from the driver phone is needed. It does not rely
on any other hardware devices. This method provides a very accurate
localization of the driver’s phone from the other users in the car.

17Senspeed: sensing driving
conditions to estimate vehicle speed in urban environments

this paper, a system is proposed that the smart phone sensors are used to
estimate the vehicle speed.

system is mainly useful when GPS is unavailable or irregular in Urban areas.
The estimation of acceleration errors and large deviations are found here. And
deviation between the estimated and the real one are calculated. The changes in
the acceleration errors are corrected when needed. These points are called as
reference points.

18Body sensor networks for
driver distraction identification

this paper, Controller Area Network (CAN) is used in this system for detecting
driver distractions. It mainly focus on leg and head movements of the drivers.
It detects upto a high accuracy of distraction over 90%. Using this highly
reliable reduces the density of the accidents that are caused using driver

19Detecting driver phone calls
in a moving vehicle based on voice features

this paper, a system is proposed to monitor the smart phone activities and it
also blocks the calls that are coming to the driver while driving. It also
concentrates on phone position on the vehicle. It blocks the calls from the
phone that comes to the driver’s location. This method is position-independent.
It also reduces the noise that is produced from in and around the vehicle’s

20Development of the
eco-driving and safe-driving comp0nents using vehicle information

some components which can obtain and access the eco-driving and safe driving,
the vehicle information is obtained through CAN (Controller Area Network). It
is inner vehicle network protocol. It uses Bluetooth network to transfer the
information between the modules.


of the applications are listed are listed and their techniques are examined and
compared in this paper. Majority of the systems uses mobile phone in-built
sensors like GPS, accelerometers and gyroscopes sensors are used to identify
the location of the driver’s mobile and find their pattern of driving.


this paper, to detect T&D we are submitting a novel method to make it
simple. Here we  using the authority
method with some patterns that will guide us how smart phones are used in
moving vehicles. The associated information about some build in sensors in the
smart phones are collected and these sensors are analysed with hypothesis
testing and checked for T&D patterns match. The outcome of this approach
will achieve good detection accuracy. This proposed T&D method could be
appropriate for usage based insurance. Many anti T&D mobile phone
applications are sustain with this method.




1.Sensing Driver Phone Use with
Acoustic Ranging through Car Speakers. Jie Yang, Student Member, IEEE, Simon
Sidhom, Gayathri Chandrasekaran, Tam Vu, Hongbo Liu, Student Member, IEEE,
Nicolae Cecan, Yingying Chen, Senior Member, IEEE, Marco Gruteser, and Richard
P. Martin, Member, IEEE.

2. Detection
of Dangerous Cornering in GNSS-Data-Driven Insurance Telematics Johan
Wahlström, Isaac Skog, Member, IEEE, and Peter Händel, Senior Member, IEEE.

3. Driver Behavior
Analysis for Safe Driving: A Survey Sinan Kaplan, Mehmet Amac Guvensan,Member,
IEEE, Ali Gokhan Yavuz,and Yasin Karalurt.

4.Determining Driver Phone Use by
Exploiting Smartphone Integrated Sensors.
Yan Wang, Yingying (Jennifer) Chen, Jie Yang, Marco Gruteser, Richard P.
Martin, Hongbo Liu, Luyang Liu, and Cagdas Karatas

5.Detecting Driver’s Smartphone
Usage via Nonintrusively Sensing Driving Dynamics. Cheng Bo, Xuesi Jian, Taeho
Jung, Junze Han, Xiang-Yang Li, Fellow, IEEE, Xufei Mao, Member, IEEE, and Yu
Wang, Senior Member, IEEE.

6.Automatic Identi?cation of
Driver’s Smartphone Exploiting Common Vehicle-Riding Actions. Homin Park,
DaeHan Ahn, Taejoon Park , Member, IEEE, and Kang G. Shin, Fellow, IEEE

7.Improved Vehicle Steering Pattern
Recognition by Using Selected Sensor Data. Zhenchao Ouyang, Jianwei Niu,
Member, IEEE, Mohsen Guizani, Fellow, IEEE.

8. Full auto-calibration
of a smartphone on board a vehicle using IMU and GPS embedded sensors.    Javier Almaz´an, Luis M. Bergasa, J. Javier
Yebes, Rafael Barea and Roberto Arroyo.

9. DriveSafe: an App for
Alerting Inattentive Drivers and Scoring Driving Behaviors. Luis M. Bergasa,
Daniel Almería, Javier Almazán, J. Javier Yebes, Roberto Arroyo.

Vehicular Speed Variations bDriving Style Recognition Using a Smartphone as a
Sensor Platform

11.  Driving Style Recognition Using a Smartphone
as a Sensor Platform. Derick A. Johnson and Mohan M. ignal Strengths.

Are You a
Safe Driver.  Lonnie Langle, Ram Dantu

Sensor Fusion for Improved Vehicular Navigation. Oliver Walter, Joerg
Schmalenstroeer, Andreas Engler, Reinhold Haeb-Umbach

Sensor Networks for Driver Distraction Identification. Amardeep Sathyanarayana,
Sandhya Nageswaren, Hassan Ghasemzadeh, Member, IEEE, Roozbeh Jafari, Member,
IEEE, John H.L. Hansen, Fellow, IEEE.

Driver Phone Calls in a Moving Vehicle Based on Voice Features. Tianyi Song1,
Xiuzhen Cheng1, Hongjuan Li1, Jiguo Yu2, Shengling Wang3, Rongfang Bie3