Real Time Computer Vision for Lane Level Vehicle Navigation

IJSRD - International Journal for Scientific Research & Development| Vol. 1, Issue 7, 2013 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 1423
Abstract— In two days world there is a huge requirement of
intelligent transportation systems (ITS). It requires the
positioning of the vehicles which requires high accuracy and
availability. The poisoning requires real time accesses and
should work in both rural and urban areas. The present days
sensors are very poor in performance especially in urban
areas. The main reason behind this is the large buildings
may block the satellite signals. This paper presents a
technique of computer vision using the pseudo range
differential global positioning system (DGPS) along with
the feature of inertial navigation system (INS) and GSM.
Using GSM immediately information about the position of
the vehicle and if any accidents occurs the information will
be given to the authorized person .It also work efficiently
where the GPS signal are not that much reliable.
Keywords; Effective vehicle control systems, Effective
vehicle safety systems, aided navigation, exact position
determination, inertial navigation, intelligent transportation
systems (ITS), land transportation.
INTRODUCTIONI.
Vehicle positioning is becoming a crucial problem in
intelligent vehicle systems whereas the controlling of the
vehicle and the safety of vehicle is related on many
parameters Such as the positioning of the vehicle, the speed
of the vehicle, the navigation of the vehicle and the
management of the traffic in terms of safety measures.
Irrespective of environmental conditions it requires
continuous assessment of vehicle positioning and navigation
in terms of it speed and directions. It can be achieved only if
huge infrastructure and more number of sensors have been
used. But it requires large amount of resources, which cost
high volume of amount and much time to implement. The
traditional approaches which cost less utilize an inertial
navigation system (INS), will integrate the inertial
measurements with MEMS based unit. It can provide the
assessment in continuity manner and availability will be
more in sensors useful in driving. But it is necessary to
calibrate it periodically by other sensors, as its performance
changes by time.
In some approaches we can use single low bandwidth
sensors such as Global Positioning System (GPS). This type
of single-sensor approaches will provide accuracy in
position estimation but they won’t provide the status of the
entire vehicle. Because these low cost sensors are
complementary, many ITS applications can use the
information obtained from these sensors and combined it to
provide the levels of accuracy, continuity and availability.
Various sensor fusion techniques between GPS and INS
systems for positioning of the vehicle will look at the tightly
coupled and Loosely coupled GPS aided INS systems. All
these approaches which are working in real time are not
reliable in suburban/urban areas, where the GPS receiver
does not have a sufficient diversity of satellite signals.
Sensor fusion between a vision and INS, perform better in
urban areas, because it provides abundance features. All the
previous systems which are working in real time are creating
number of problems. Like they are giving accurate results in
day time only not in night time and they are not
concentrating on land vehicle navigation.
In this paper we are using DGPS, using which the
longitudinal and latitude directions of the vehicle are not
obstructed by buildings and trees and we can reduce any
type of uncertainty in the position of the vehicle’s using
pseudo-range measurements. Using a forward-looking
camera, the mapped traffic lights can be detected. The
approach we are using here is having a good solution to the
positioning problem; it fuses low cost sensors for a positing
system with high degree of accuracy, continuity and
availability. It is also easy to upgrade for the already
existing systems which are using the same type of on-board
sensors.
Fig 1: Block Diagram
DGPS -AIDED INSII.
Sensor fusion between DGPS and INS has been well known
for more than a decade. To correct the INS estimate
whenever measurements from DGPS are available, other
Real Time Computer Vision for Lane Level Vehicle Navigation
M. S. Niranjani1
V. Viswanadha2
1
M. Tech. 2
Associate Professor
1,2
SIETK, Puttur

(IJSRD/Vol. 1/Issue 7/2013/0012)
Bayesian estimation frameworks have also been suggested;
such as the unscented Kalman filter the particle filter
estimated position and orientation are computed in a global
coordinate reference frame such as Earth-centered Earth-
fixed (ECEF) or a fixed local tangent frame. These fusion
techniques are divided into the following two types: 1)
loosely coupled and 2) tightly coupled.
A. Loosely Coupled DGPS-Aided INS
For loosely coupled sensor integration, the DGPS receiver
computes the antenna’s position and velocity in an absolute
reference frame, and the measurements used by the
correction framework include the processed absolute
position and velocity of the DGPS receiver. When the
number of visible DGPS satellites is less than four, the
correction step cannot be carried out, because the DGPS
receiver needs to track at least four satellites to compute the
absolute position and velocity. This solution is considered
less robust compared to the tightly coupled solution, because
it requires the line-of-sight to at least four satellites. This
approach also yields suboptimal state estimation, because
the DGPS receiver does not provide the cross correlation
between the estimated positions.
B. Tightly Coupled DGPS-Aided INS
In the tightly coupled arrangement, raw DGPS
measurements (pseudo range, Doppler shift, and carrier
phase) from the receiver, for each satellite, are used to
correct the INS. The correction step is useful whenever the
receiver tracks at least two satellites. Although the position
is not observable when the number of tracked satellites is
less than four, the DGPS pseudo range, Doppler, and
carrier-phase (if available) measurements will provide
information to correct certain portions of the state vector
whenever such measurement information is available. The
solution computed from the tightly coupled framework
provides a comparable or better result than the loosely
coupled framework.
PROTOTYPE OF REALTIME NAVIGATIONIII.
SYSTEM
This section describes our sensor setup, and frame
definitions. It will give us a detail description of the vehicle
navigation. The position of the vehicle and the state of the
vehicle can be get to know using the real time navigation
system, which also gives alerts to the appropriate authority
about the abnormal conditions with exact location of the
vehicle
A. Sensor Setup
The sensor shape up on the roof of the vehicle consists of
the following sensors:
1 a six- degree-of- freedom Micro Electro Mechanical
Systems IMU .
2 DGPS receiver which utilizes pseudo range values,
Doppler and camera-phase values for measurements; and
also
3 A colour camera that can be of both hardware/ software
triggered, this entire setting of sensors is shown in Fig.3.
The arrangements also include accelerometers and
gyroscope accelerations and rotational rates including the
orthogonal axes u, v and w .These three orthogonal axes
coincide with the IMU orthogonal axes which are inside the
black box.
Fig. 2: Real Time Navigation System
The IMU position is close to the DGPS receiver antenna’s
phase centre to interpret the inertial measurements from the
IMU, as it from the inertial measurement readings from the
origin of {B}, here we are ignoring the lever arm effect. The
colour camera used here is a standard digital camera to
communicate over IEEE 1394 or USB2.0. To trigger the
colour camera we can use either external transistor or
transistor logic signal. In another way it can be triggered
using software, which is by the application programming
interface.
Fig. 3: Sensors are mounted on the top of the vehicle. The
DGPS receiver and IMU is on the middle of the black box.
B. Frame Definitions
The following frames are illustrated in Fig. 4.
1 A navigation frame denoted by {N } is defined to
coincide with a local tangent frame, with the axes
pointing along the north, east, and down
directions.
2 The body frame {B} is defined at the GPS
antenna’s phase centre, which maps the IMU’s u-,
v-, and w-axes along the X, Y and z –axes

Fig. 4: Relationship between frames illustrated.
3 A camera calibration object that could be a 3-D
object or a planar pattern, with frame {G} attached
to the object, where identifiable features on the
object are defined with respect {G}’origin
EVALUATION OF MESUREMENTSIV.
This section describes the evaluation of real time which has
been measured from the DGPS and AOA measurements
from a camera. In addition measurements can be combined
to resolve the state estimation problem in urban areas.
A. DGPS measurements
This section summarizes the DGPS observables. The
measurement equations for each satellite vehicle are
described, and the pseudo range measurement is illustrated
to correct the rover’s longitudinal position in an urban
setting. Satellite geometry can affect the quality of GPS
signals and accuracy of receiver trilateration.
Fig. 5: Measurements of Real Time Differential GPS
Dilution of Precision (DOP) reflects each satellite’s position
relative to the other satellites being accessed by a receiver.
Position Dilution of Precision (PDOP) is the DOP value
used most commonly in GPS to determine the quality of a
receiver’s position. It’s usually up to the GPS receiver to
pick satellites which provide the best position triangulation.
The basic working principle here is the finding the vehicle
positioning and coordinates. The distance and direction
between any two way points , or a position and waypoint.
Travel progress reports. So that accurate time measurements
can be known using the true coordinates, by doing
corrections using DGPS. We will get true coordinates by
comparing values with the reference station. So that we can
reduce the calculation time and achieve accurate results.
EXPERIMENTAL RESULTSV.
This section will give in detail results, to support the
concepts which have been discussed in previous sections.
The proto type which is placed on the roof of the vehicles is
shown in fig 2. The u-axis shows the front panel of the
vehicle. The DGPS receiver along with the IMU is placed
inside the black box which is in the middle. It will receives
the signal from the satellite, measures the position of the
vehicle and according to the root given by the persons it will
change it position and navigate towards the path. If any
obstacles occurs the IR Sensors will automatically detects
that obstacles and take a back step for two seconds and
automatically takes a right movement.
We have used IR sensors in three directions, so that in those
directions it will navigate according to the obstacle free
route. Suddenly if the vehicle stops because of any abnormal
condition occurs or if any accidents taken place then
immediately message will be given to the concerned
authority. It is pre requisite to give the details of the
authority, so that the longitude and latitude position of the
vehicle will be messaged to that authority. It gives minute
meters accurate results.
A. DGPS Aided INS Results
This section emphasizes the location of the vehicle using the
INS/DGPS state estimate during the time when both of the
values from the GPS and camera visuals are available. In the
first phase we are using only the pseudo range
measurements not entire output part of the vehicle. The
pictures are drawn for the traffic light images, are shown in
Fig 5. The first column in it shows maximized pictures in
both the rows and columns. The pictures were not having
the mean of zero before implementing the kalman’s filter.
When the vehicle is stationary some of the drift errors have
occurred.
The position of the vehicle and the standard deviation which
is computed using EKF are shown in Fig 6.with the top of
the three rows with blue plots. These plots shows the minute
uncertainty results while using only DGPS aided INS in
calculating the position of the vehicle in Lane Level
navigation. But it is tolerable error which is less than 1.2m.
If we observe carefully the accuracy of error in down
component is less when compared to east and north
component.
This phenomenon is quite normal in DGPS measurements.
There is one more approach while using only pseudo range
of measurements in DGPS to make error free with Inertial
Navigation System is that when vehicle is in steady state we
cannot observe yaw. This thing is shown in Fig 6 in the

fourth row. The angle of yaw with the DGPS aided solution
drifts and when the vehicle suddenly halts at red lights the
value of standard deviation raises for both the pseudo range
and carrier aiding.
Fig.5: .Results in the first column shown are only using
DGPS with INS methodology. Next column shows images y
using vision/DGPS aided INS that is visually pseudo range
Fig.6. In the beginning three rows of the begging columns
shows the positional errors between DGPS and because of
vision/DGPS aiding where CDGPS aiding used as the
reference path. The beginning three rows of the second
columns show the positional standard deviation calculations.
The last row shows the comparative results of angle of yaw
and standard deviation calculations
CONCLUSIONVI.
In real time vehicle navigation it requires the exact
positioning of the vehicles and the availability of the system

should be high in major of the ITS applications. In particular
applications like controlling and safety and also traffic
managements. Many research works have been done on
these type of applications by using Global Navigation
Satellites systems to aid an INS. The results are also
appreciable when we apply it in rural areas and in day time
and in clear weather but not in urban areas. Some
applications have been available in urban areas too but in
aircraft/spacecraft applications. They are not implemented it
on road transportations.
This paper has taken the application for vehicle navigation
on surface in urban areas in real time environments. For this
we have used tightly coupled DGPS aided INS. It combines
the DGPS measurements with the visuals from the traffic
lights so that the exact positions can be computed by getting
longitude and latitude values. So the navigation can be done
with resolving positional errors and with extreme accuracy.
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Real Time Computer Vision for Lane Level Vehicle Navigation

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Real Time Computer Vision for Lane Level Vehicle Navigation