sensors
Article
Dynamic Vehicle Detection via the Use of Magnetic
Field Sensors
Vytautas Markevicius *, Dangirutis Navikas, Mindaugas Zilys, Darius Andriukaitis,
Algimantas Valinevicius and Mindaugas Cepenas
Received: 12 November 2015; Accepted: 4 January 2016; Published: 19 January 2016
Academic Editor: Andreas Hütten
Department of Electronics Engineering, Kaunas University of Technology, Studentu St. 50–418,
LT-51368 Kaunas, Lithuania; dangirutis.navikas@ktu.lt (D.N.); mindaugas.zilys@ktu.lt (M.Z.);
darius.andriukaitis@ktu.lt (D.A.); algimantas.valinevicius@ktu.lt (A.V.); mindaugas.cepenas@ktu.edu (M.C.)
* Correspondence: vytautas.markevicius@ktu.lt; Tel.: +370-37-300-522
Abstract: The vehicle detection process plays the key role in determining the success of intelligent
transport management system solutions. The measurement of distortions of the Earth’s magnetic field
using magnetic field sensors served as the basis for designing a solution aimed at vehicle detection.
In accordance with the results obtained from research into process modeling and experimentally testing all
the relevant hypotheses an algorithm for vehicle detection using the state criteria was proposed. Aiming
to evaluate all of the possibilities, as well as pros and cons of the use of anisotropic magnetoresistance
(AMR) sensors in the transport flow control process, we have performed a series of experiments with
various vehicles (or different series) from several car manufacturers. A comparison of 12 selected
methods, based on either the process of determining the peak signal values and their concurrence in
time whilst calculating the delay, or by measuring the cross-correlation of these signals, was carried out.
It was established that the relative error can be minimized via the Z component cross-correlation and Kz
criterion cross-correlation methods. The average relative error of vehicle speed determination in the best
case did not exceed 1.5% when the distance between sensors was set to 2 m.
Keywords: magnetic field; AMR sensors; vehicle speed detection
1. Introduction
Intelligent transport [1], smart cities [2,3] and the Internet of Things [4] are terms that surround all
of us nowadays. One of the key factors allowing one to consider intelligent solutions [3–5] specifically
designed for urban areas is the constantly decreasing price of sensors capable of providing information
about traffic flows, environmental conditions and other parameters. The processes of integrating
sensors into the existing infrastructure and forming large sensor networks has thus become increasingly
less complicated. In turn, the physical medium is less disrupted and society gains access to a brand
new quality of life, facilitated by environmentally responsive public services, continual surveillance
and a direct connection with other information users [6].
The capability to detect vehicles is the main element in intelligent transport management systems,
allowing us to gather information about the traffic intensity and vehicle speed. Amongst the most
popular transport sensors are induction loops, commonly used due to their low price and relatively
high signal quality. However, the process of installing such devices normally results in significant
damage to an extensive area of the road surface and the loop itself may suffer from the thermal
expansion of the road pavement. Anisotropic magnetoresistance (AMR) sensors are an alternative to
induction loops capable of measuring even extremely weak magnetic fields. When a vehicle is above
the sensor, its metal construction distorts the Earth’s magnetic field and this allows one to determine
the location of the vehicle.
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Magnetoresistive sensors are small, so their installation and maintenance are much less complex
and pricey in comparison to induction loops. Nevertheless, since their introduction, they have been
subject to little scrutiny or testing in this particular application area.
As the Earth’s magnetic field distortion signal can be used not only for the detection, but also
for the classification and recognition of transport vehicles [7], it is highly important to ensure sensor
effectiveness and the repetition rate of the recognized signal. Various objects and processes taking place
in the surrounding environment have a major impact on the readings of magnetic sensors. The factors
determining the success of locating a vehicle are multiple, therefore, distinguishing the distortions
of the magnetic field caused by the vehicle from those caused by certain environmental changes is a
rather difficult task [8,9]. The accuracy of locating vehicles in the environment with no undesirable
objects affecting the magnetic field would be significantly higher, but, unfortunately, the possibilities
of creating such an environment are slim to none at all. Taking that into account, in comparison to road
induction loops the process of locating vehicles using magnetic fields is way more complicated. When
conducting the task of locating a vehicle using the magnetic field method, these external factors must
be eliminated. The literature analysis reveals that the majority of research in the field of AMR sensors
has been carried out in laboratories, which indicates the lack of practical research activity when it
comes to real transport vehicles and road environment conditions [10].
2. Related Works
Previous research papers mostly addressed the problems related to the detection of vehicles
parked on parking lots or in car parks [11]. The measurement system based on AMR sensors
was thus created for the purpose of locating vehicles using the Earth’s magnetic field distortion
method. It comprises magnetoresistive sensors produced by four different manufacturers and
researchers have focused on analyzing the temperature stability and parameter spread influence
on detection relevant to reliability [12]. Following a lengthy period of observation, the researchers
determined several shortcomings of the method, which were as follows: dependence of the sensor
signal on temperature, residual magnetic moment and diffusion of the sensor parameters during the
manufacturing process [13]. Upon analyzing the impact of the vehicle construction on the magnetic
field changes in the parameters of the mounted sensors, it was determined that said construction plays
a vital role in terms of magnetic field distortion and sensor output signals. Even vehicles of similar
construction impact the magnetic field components differently when entering and exiting a parking
space. The amplitude and form of an AMR sensor signal is also dependent on how the sensors and
vehicles are arranged in terms of their positions magnetic fieldwise [13]. When a vehicle is on top of
the sensor, in some cases one can determine the critical zones which display no change in the magnetic
field. Based on modeling and practical research results, researchers have proposed a location algorithm
reliant on the state criteria. It has been established that the most accurate way of determining the state
of the parking location sensors relies on the use of complex criteria [14]. Via the use of this method the
researchers observed a 93.3% detection effectiveness.
In the case of moving vehicles, the majority of the proposed stationary vehicle detection methods
have been proven unsuitable due to the need to not only record the fact of the vehicle’s presence, but
also measure its speed and the dependence of the magnetic field distortions on the vehicle construction.
It is noteworthy that the majority of publications contain algorithms and methods designed to locate
vehicles and determine their speed via the use of AMR sensors, yet most of these papers lack any
consideration of the fact that small sensors can be passed by vehicles following different trajectories,
which can significantly complicate the task of determining the vehicle type.
The bottom of the vehicle under the engine is commonly magnetically open whilst the engine
section holds a generator and the ignition starter, each of which generate their own operating magnetic
fields. In between the engine department and vehicle interior there is typically a steel (magnetic)
partition, similar to the ones found between multiple interior and baggage compartments. Much of
uncertainty is caused by the aforementioned partitions, knowing that regardless of their distance to
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distance to the AMR sensor, they can act as a vehicle driving nearby, or the one being located above,
distance
to the
sensor, they
can act
a vehicle
driving nearby, or to
thea one
being located
above,of
i.e.,
sum
ofAMR
thethey
magnetic
field
itsas
separate
components,
partition
the vicinity
the the
AMR
sensor,
can act
as and
a vehicle
driving
nearby, or subject
the one being
locatedinabove,
i.e., the
i.e.,
the sum of the magnetic
field and
its separate
components,
subject tonotion
a partition in
vicinity of
the
sensors,
increase
or
decrease.
Therefore,
thethe
Z component
summagneto-resistive
of the magnetic field
andcan
its separate
components,
subject tothe
a partitionthat
in the
vicinity
of the
the
magneto-resistive
sensors,
can increase
or decrease.
Therefore,
the notion
that
the
Z component
of
the
Earth’s
magnetic
field
increases
under
the
vehicle
is
not
necessarily
true
in
all
cases,
and
this
magneto-resistive sensors, can increase or decrease. Therefore, the notion that the Z component
of
ofcan
thesignificantly
Earth’s magnetic
field
increases
under
the
vehicle
is
not
necessarily
true
in
all
cases,
and
this
complicate
the process
of identifying
located in
thein
same
line asand
thethis
sensor.
the Earth’s magnetic
field increases
under
the vehicle aisvehicle
not necessarily
true
all cases,
can
can significantly complicate the process of identifying a vehicle located in the same line as the sensor.
significantly complicate the process of identifying a vehicle located in the same line as the sensor.
3. Experiments
3.3.Experiments
Experiments
An experiment was conducted with 24 various models of cars produced by different
An
was
conducted
with
ofof cars
by
different
An experiment
experiment
was
conducted
with 24
24 various
various
models
cars produced
produced
by sensors
different
manufacturers
seeking
to evaluate
the possibilities
as wellmodels
as
pros and
cons
of
using AMR
in
manufacturers
seeking
to
evaluate
the
possibilities
as
well
as
pros
and
cons
of
using
AMR
sensors
in
manufacturers
seeking to
evaluate
the possibilities
well as pros
and
cons of1)using
AMR sensors
in
the
control of transport
flows.
A brand
new vehicleas
scanning
system
(Figure
was designed,
which
the
transport
flows.
AAbrand
scanning
system
(Figure
1)1)was
which
thecontrol
control
transport
flows.
brandnew
new
vehicle
scanning
system
(Figure
was
designed,Vehicle
which
allowed
usofofto
measure
the distortion
of vehicle
the
Earth’s
magnetic
field
caused
by designed,
vehicles.
allowed
to measure
measure
the
distortion
of the
Earth’s
field
caused
by measurement
vehicles.
Vehicle
allowed us
uswas
to
the
distortion
the
Earth’s
magnetic
field caused
by
vehicles.
Vehicle detection
detection
conducted
using
twoof
sensors
located
atmagnetic
a distance
of 30
cm.
The
was
detection
was
conducted
using
two
sensors
located
at
a
distance
of
30
cm.
The
measurement
was
was conducted
using
twoalong
sensors
a distance
of 30capturing
cm. The measurement
was carried
carried
out every
1 cm
thelocated
X axis at
direction
whilst
the vehicle position
and out
the
carried
every
1the
cmX along
the X along
axis
direction
whilst
capturing
therepeated
vehicle
position
andalong
the
every 1out
cm
along
axis
direction
whilst
the
vehicle
and the
magnetic
field
magnetic
field
parameters.
Scanning
thecapturing
direction
of
the
X axis position
was
every
20 cm
magnetic
fieldScanning
parameters.
Scanning
alongof
the
of the
X axisevery
was
every
cm alongof
parameters.
along
the direction
thedirection
X axissensors
was
repeated
20 cm along
the20direction
the
direction
of
the Y axis
whilst
moving
both
AMR
(Figure
2). repeated
the
of themoving
Y axis whilst
moving
both(Figure
AMR sensors
(Figure 2).
thedirection
Y axis whilst
both AMR
sensors
2).
RS485
RS485
RS485
RS485
I2C
I2C
I2C
I2C
Distance
Distance
meter
meter
MicroMicrocontroller
controller
MSP430
MSP430
Figure 1. Experiment structure.
Figure1.1.Experiment
Experimentstructure.
structure.
Figure
20cm
20cm
Y
Y
Y
AMR
AMR
X
X
Y
X
X
AMR
AMR
40cm
40cm
Figure 2.
2. Vehicle
Vehicle scanning
scanning scheme.
scheme.
Figure
Figure 2. Vehicle scanning scheme.
For the purpose of gathering data a digital distance measuring instrument was used, which
For the purpose of gathering data a digital distance measuring instrument was used, which
transferred
all the data
via a RS 485
communication
port to a computer where
it was
then
For the all
purpose
of gathering
dataserial
a digital
distance measuring
was used,
which
transferred
the data
via a RS 485
serial
communication
port to ainstrument
computer where
it was
then
processed with
the specifically
designed
software.
The data acquired
from thewhere
sensor
the
RS
transferred
all the
via a RSdesigned
485 serial
communication
to a from
computer
it via
wasRS
then
processed with
thedata
specifically
software.
The data port
acquired
the sensor via
the
485
485 port. with
The process
of transferring
data
and reading
theacquired
data from
the the
AMR
sensors
via RS
the485
I2C
processed
the
specifically
designed
software.
The
data
from
sensor
via
the
port. The process of transferring data and reading the data from the AMR sensors via the I2C interface
interfaceprocess
was carried
out using data
a low-power
MSP430
microcontroller.
port.
of transferring
and reading
the data
from the AMR sensors via the I2C interface
was The
carried out using
a low-power MSP430
microcontroller.
The Earth’s
magnetic
field distortions
were detected on different vehicles. A sample set of the
was carried
out
using
a
low-power
MSP430
microcontroller.
The Earth’s magnetic field distortions were detected on different vehicles. A sample set of the
Earth’s
magnetic
field distortions
data caused
by detected
several of them
is provided
in Figure
3. The
research
The
Earth’s magnetic
field distortions
were
different
vehicles.
A sample
setresearch
of the
Earth’s magnetic
field distortions
data caused
by several ofon
them
is provided
in Figure
3. The
results
indicate
that
the
change
in
the
magnetic
field
modulus
and
separate
components
when
the
Earth’s
fieldthe
distortions
by several
of them isand
provided
in Figure
3. The when
research
resultsmagnetic
indicate that
change data
in thecaused
magnetic
field modulus
separate
components
the
vehicle is driven
on top change
of the magneto-resistive
sensor
is different
and
depends
on the vehicle
brand
results
that
in the magnetic field
modulus
andand
separate
components
when
the
vehicleindicate
is driven
on the
top of the magneto-resistive
sensor
is different
depends
on the vehicle
brand
vehicle is driven on top of the magneto-resistive sensor is different and depends on the vehicle brand
Sensors 2016, 16, 78
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andthe
theposition
positionofofsensors
sensorswith
withrespect
respecttotoa aparticular
particular
vehicle
(Figure
This
reason
behind
and
vehicle
(Figure
3).3).
This
is is
thethe
reason
behind
the
the that
fact
that
when
a
vehicle
passes
the
sensor
in
different
places
(with
respect
to
the
Y
axis),
very
fact
when
a
vehicle
passes
the
sensor
in
different
places
(with
respect
to
the
Y
axis),
very
diverse
Sensors 2016, 16, 78
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diverse magnetic
field are
profiles
are recorded.
As the
a result,
the based
methods
based on measuring
and
magnetic
field profiles
recorded.
As a result,
methods
on measuring
and comparing
and
the
position
of
sensors
with
respect
to
a
particular
vehicle
(Figure
3).
This
is
the
reason
behind
comparing
absolute
values arefor
unsuitable
detecting
the
type of
and
speed of vehicles.
absolute
values
are unsuitable
detectingfor
the
type and
speed
vehicles.
the factcompleting
that when athe
vehicle
passes the sensor
different data,
places (with
the magnetic
Y axis), very
Upon
interpolation
of acquired
the inacquired
fullofrespect
view
ofto the
field
Upon
completing the
interpolation
of the
data, a full aview
the magnetic
field distortion
diverse
magnetic
field
profiles
are
recorded.
As
a
result,
the
methods
based
on
measuring
andare
distortion
caused
by
a
vehicle
was
acquired
(Figure
4).
From
the
data
it
can
be
noticed
that
there
caused by a vehicle was acquired (Figure 4). From the data it can be noticed that there are a number
comparing absolute values are unsuitable for detecting the type and speed of vehicles.
a areas
number
of areas
as “Dead
zone”)
under the
vehicle
where the magnetic
field
remains
of
(marked
as (marked
“Dead the
zone”)
under
the
where
the magnetic
remains
undistorted
Upon completing
interpolation
ofvehicle
the acquired
data,
a full viewfield
of the
magnetic
field
undistorted
(as
if
the
vehicle
was
not
present
at
all).
Such
situations
are
critical
when
using
AMR
(as if the
vehicle
was by
nota vehicle
presentwas
at all).
Such
situations
arethe
critical
sensors
distortion
caused
acquired
(Figure
4). From
data itwhen
can be using
noticedAMR
that there
are for
sensors
for
determining
the
presence
of
a
vehicle
in
a
static
state.
determining
theofpresence
of a vehicle
in azone”)
static state.
a number
areas (marked
as “Dead
under the vehicle where the magnetic field remains
1000
Y1
400
600
800
1000
-200
-600
0
-200
400
600
800
1000
200200
400
600
Z1
800
1000
1200
M ag m etic field , m G
200
400
200
0
200
400
0
200
400
600
800
600 cm
X position,
800
400
1000
800
1000
1200
Y2
400
600
800
1000
1200
X position, cm
400
600
Z2 800
1000
1200
X position, cm
Z2
800
600
600
400
400
200
200
0
200
0
1200
1000
Y2
600
X position, cm
800
600
1200
-200
M ag m etic field , m G
M ag m etic field , m G
400
1000
0
-400
200
Z1
800
600
800
X position, cm
-200
X position, cm
800
600
0
-400
200
1200
400
200
X position, cm
-600
200
-600
-800
1200
Y1
-400
-800
200
1200
X position, cm
-400
-600
200
M ag m etic field , m G
800
200
M ag m e tic fie ld , m G
-400
600
-4000
X position, cm
0
-200
400
-600
-800
200
200
M ag m e tic fie ld , m G
-400
X2
200
-200
M ag m e tic field , m G
-800
200
0
-200
M ag m e tic fie ld , m G
-600
0
M ag m e tic fie ld , m G
-400
X1
200
-200
M ag m e tic field , m G
0
M ag m e tic field , m G
M ag m e tic field , m G
undistorted (as if the vehicle was not present at all). Such situations are critical when using AMR
X1
X2
200sensors for determining the presence of a vehicle in a static
200 state.
1200
200
400
400
X position, cm
600
800
1000
600 X position,800
cm
1000
1200
X position, cm
Figure
3.3.The
of
magnetic
measured
by
twodistinct
distinctsensors
sensors
(plots
ofmagnetic
magnetic
Figure
The
distribution
of the
the
field
distinct
sensors
(plots
Figure
3.distribution
The distribution
of the
magnetic
fieldmeasured
measured by
by two
two
(plots
of of
magnetic
field
X1,
and
X2,
Y2,
Z2
ofZ2
sensors
1 and
2 respectively).
PlotPlot
curves
areare
color
coded
fieldcomponents
components
X1,Y1,
Y1,Z1
Z1
and
X2,
Y2,
Z2
ofofsensors
11 and
22 respectively).
respectively).
Plotcurves
curves
are
color
field
components
X1,
Y1,
Z1
and
X2,
Y2,
sensors
and
color
by
sensor
along
latitudinal
vehiclevehicle
line.
coded
by position
sensor
position
along
latitudinal
coded
by sensor
position
along
latitudinal
vehicleline.
line.
VehicleNo.
No.11
Vehicle
Z1
Z1
Z1
Z1
800
800
700
Dead zone
700
Dead zone
200
Magmetic field, mG
400
600
Magmetic field, mG
600
Magmetic field, mG
Magmetic field, mG
600
400
200
0
0
200
200
Yp
Yp
150
os
iti o
n
150
os
iti o
n
100
,c
m
100
,c
m
50
200
0
50
0
200
400
600
800
800
, cm
600
osition
400 X p
m
c
,
n
io
X posit
1000
1000
600
500
500
400
400
300
300
200
200
100
1000
0
-100
-100
Figure 4. Cont.
Figure 4. Cont.
Figure 4. Cont.
0
0
200
200
400
600
X position, cm
400
600
X position, cm
800
800
1000
1000
1200
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Vehicle No 2
Z1
Z1
700
Magmetic field, mG
Magmetic field, mG
600
600
500
400
300
200
200
Yp
150
100
os
iti o
n,
cm
50
400
200
0
600
X positi
800
1000
500
Dead zone
400
300
200
on, cm
100
0
200
400
600
X position, cm
800
1000
800
1000
Vehicle No. 3
Z1
Z1
650
600
Magmetic field, mG
Magmetic field, mG
550
600
500
400
300
200
200
Y p 150
100
os
iti o
n,
cm
50
0
200
400
600
800
ion, cm
X posit
1000
500
Dead zone
450
400
350
300
250
200
150
0
200
400
600
X position, cm
FigureFigure
4. The4. distribution
of the
magnetic
field
component)caused
caused
three
different
The distribution
of the
magnetic
fielddistortion
distortion (Z component)
byby
three
different
vehicles
(different
color means
different
position
of sensor
with respect
Y axis—across
the
vehicles
(different
color means
different
position
of sensor
with respect
to thetoY the
axis—across
the vehicle).
vehicle).
4. Analysis of Vehicle Detection Methods
4. Analysis of Vehicle Detection Methods
The key
must must
be addressed
whenwhen
designing
the system
for detecting
speed
andand
vehicle
Thetask
keywhich
task which
be addressed
designing
the system
for detecting
speed
type isvehicle
relatedtype
to the
accuracy
of
determining
the
duration
between
signals
from
separate
sensors.
Once
is related to the accuracy of determining the duration between signals from separate
established
the
stable
signal discretization
andfrequency
completing
matching
both signals,
sensors.
Once
established
the stable signalfrequency
discretization
and the
completing
the of
matching
of
it becomes
possible
to calculate
the delay
between
signals
and the
vehicle
both signals,
it becomes
possible
to calculate
the delay
between
signals
andspeed.
the vehicle speed.
Seeking
to evaluate
the accuracy
of determining
the vehicle
speed
using
AMR
sensors,
Seeking
to evaluate
the accuracy
of determining
the vehicle
speed
using
AMR
sensors,
we we
turned
turned
to
the
data
acquired
from
conducting
the
experiment
and
carried
out
a
comparison
12
to the data acquired from conducting the experiment and carried out a comparison of 12 of
selected
selected
methods.
The
list
of
these
methods
is
provided
in
Table
1.
methods. The list of these methods is provided in Table 1.
Table 1. List of methods.
Table 1. List of methods.
No.
1
No.
2
31
4
52
63
74
85
9
6
10
7
11
8
12
Method
Z component peak detection
Method
Z component
cross-correlation
Module
detection
Z componentpeak
peak
detection
Module cross-correlation
Z component
cross-correlation
Vectorial
deviation
peaks (Equation (1))
Modulecross-correlation
peak detection(Equation (1))
Vectorial deviation
Combined vectorial
Module deviation—peaks
cross-correlation(Equation (2))
Combined
vectorial
deviationpeaks
cross-correlation
(2))
Vectorial
deviation
(Equation (Equation
(1))
Kz criterion peaks
Vectorial deviation cross-correlation (Equation (1))
Kz criterion cross-correlation
Combined
vectorial
deviation—peaks
(Equation (2))
Z peaks
±50 readings
of cross-correlation
CombinedModule
vectorial
deviation
cross-correlation
(Equation (2))
peaks
±50 readings
of cross-correlation
9
10
11
12
Kz criterion peaks
Kz criterion cross-correlation
Z peaks ˘50 readings of cross-correlation
Module peaks ˘50 readings of cross-correlation
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methods are based on either the process of finding the peak signal values or6 their
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superposition in time whilst calculating the delay, or via calculation of the cross-correlation of these
signals and determining the lag.
The
selected
methods
based
on either
the process
Vectorial
deviation
is a are
simple
criterion
defined
as [11]:of finding the peak signal values or their
superposition in time whilst calculating the delay, or via calculation of the cross-correlation of these
K | cos   cos  0  cos   cos  0  cos  cos  0 |
(1)
signals and determining the lag.
Vectorial
deviation between
is a simple
criterionfield
defined
asand
[11]:x, y, z axis respectively.
where
α, β, γ—angle
magnetic
vector
Combined vectorial deviation
criterion
compiled
K “|cosα is
´ acosα
´ cosβfrom
cosγ“square”
´ cosγ0 |and vectorial deviations.
(1)
0 | ` | cosβ
0 | ` |the
According to the fact that the Z component increases when a vehicle is moving over and decreases
where α, β, γ—angle between magnetic field vector and x, y, z axis respectively.
nearby the vehicle, it can be used to increase the sensor’s sensitivity when a vehicle is moving:
Combined vectorial deviation is a criterion compiled from the “square” and vectorial deviations.
K Z| cos
  cos0 increases
 cos  when
cos 0a| vehicle
 ( Bz / Bis
1)
According to the fact that the
component
over and decreases
(2)
z 0 moving
nearby the vehicle, it can be used to increase the sensor’s sensitivity when a vehicle is moving:
where cos α, cos β—cosines of magnetic field vector’s angles on the influence of vehicle, cos α0,
cosβ ´ cosβ
K “|cosα
´ cosα
(2)
z {B
0 | ` | without
0 | ` pBof
z0 ´ 1q B —magnetic induction
cos β0—cosines of magnetic
field vector
angles
influence
vehicle,
z
on thecos
influence
of vehicle, ofBzmagnetic
value without
influenceof
ofvehicle,
vehicle. cos α0 , cos
where
α, cos β—cosines
fieldinduction
vector’s angles
on the influence
0 —magnetic
β0 —cosines
of
magnetic
field
vector
angles
without
influence
of
vehicle,
B
—magnetic
induction on
z
Kz criterion:
the influence of vehicle, Bz0 —magnetic induction value without influence of vehicle.
B
K z  z 1
Kz criterion:
(3)
BB
z z0
Kz “
´1
(3)
Bz0
where B —magnetic induction value without influence
of vehicle.
z0
whereThe
Bz0 —magnetic
inductiondata
value
without
influence
vehicle. which are interpreted in search for
view of the gathered
when
using
differentofmethods
The
view
of
the
gathered
data
when
using
different
methods
which are interpreted in search for
signal matching, is presented in Figure 5.
signal matching, is presented in Figure 5.
900
Magmetic field, mG
800
700
600
500
400
300
200
100
0
0
200
400
1
600
2
Data
3
800
4
1000
1200
5
Figure 5. The gathered data when using different methods (1—Z component, 2—Module, 3—Vectorial
deviation,
deviation,
Figure 5.4—Combined
The gatheredvectorial
data when
using 5—K
different
methods (1—Z component, 2—Module, 3—
z criterium).
Vectorial deviation, 4—Combined vectorial deviation, 5—Kz criterium).
In Figure 6 a view of the data samples from two matching sensors via the use of Z component
cross-correlation
mismatch
data
samplessensors
from the
two
be due
In Figure 6isa presented.
view of theThe
data
samplesbetween
from two
matching
via
thesensors
use of could
Z component
tocross-correlation
the different sensitivity
or
positioning
of
sensors.
Such
a
mismatch
could
be
eliminated
by
using
is presented. The mismatch between data samples from the two sensors could
be
data
techniques.
due normalization
to the different
sensitivity or positioning of sensors. Such a mismatch could be eliminated by
using data normalization techniques.
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300
1 Sensor
2 Sensor
100
0
Magnetic field, mG
Magnetic field, mG
200
-100
-200
300
1 Sensor
2 Sensor
200
100
0
-100
-300
0-200
100
200
-300
0
100
300
200
400
300
500
X position
a)
400
500
X position
300
700
600
700
800
900
800
900
1000
1000
a)
1 Sensor
2 Sensor
300
1 Sensor
2 Sensor
100 200
Magnetic field, mG
Magnetic field, mG
200
600
0 100
-100
0
-200 -100
-300 -200
0
100
200
-300
0
100
300
200
400
300
400
500
X position
600
500
b)
X position
600
700
700
800
900
800
900
1000
1000
b)
Figure
sensors
(a)(a)
and
samples
matching
(b) (b)
via via
the the
use use
of the
Figure 6.
6. A
A view
view of
ofthe
thedata
datasamples
samplesfrom
fromtwo
two
sensors
and
samples
matching
of
Figure 6. A view of the data samples from two sensors (a) and samples matching (b) via the use of
Z
component.
the Z component.
the Z component.
Upon conducting the analysis and processing of the data collected during the experiment using
Upon conducting the analysis and processing of the data collected during the experiment using
Uponsignal
conducting
the analysis
and
of the data
the experiment
using
the selected
matching
methods,
weprocessing
have determined
thecollected
averageduring
and maximum
relative
errors
the selected signal matching methods, we have determined the average and maximum relative errors
the
selected
signal
matching
methods,
we
have
determined
the
average
and
maximum
relative
errors
when the distance between sensors was considered to be equal to 2 m. The results are displayed
when
thethe
distance between sensors was considered to be equal to 2 m. The results are displayed in
when
in Table
2. distance between sensors was considered to be equal to 2 m. The results are displayed in
Table
2.
Table
The 2.
analysis of the acquired results has revealed that the smallest average relative errors are
TheThe
analysis
of of
thetheacquired
that the
the smallest
smallestaverage
averagerelative
relative
errors
analysis
acquiredresults
resultshas
has revealed
revealed that
errors
areare
obtained with the second and the tenth methods (the maximum errors do not exceed 6.5%, Figure 7).
obtained
with
the
second
and
the
tenth
methods
(the
maximum
errors
do
not
exceed
6.5%,
Figure
obtained with the second and the tenth methods (the maximum errors do not exceed 6.5%, Figure 7). 7).
The dispersion of errors in the case of using these methods is shown in Figures 8 and 9.
TheThe
dispersion
of of
errors
ininthe
is shown
shownininFigures
Figures8 8and
and
dispersion
errors
thecase
caseofofusing
usingthese
these methods
methods is
9. 9.
Table 2. Method errors.
Table2.
2.Method
Method errors.
Table
errors.
Method No.
Method
No.
Relative
Error
Relative
Error
(%)(%)
Relative
Error
(%) 1 11 2 22
3
4
5
6
7
88
999
10
33
44
55
66
77
10 11
10
1111
Average
3.6
1.5
2.8
3.3
3.7
6.7
3.3
2.6
3.9
1.5
Average
3.63.6 1.51.5 2.82.8 3.3
3.3
2.6
3.9
1.5 3.1
3.1
Average
3.3 3.7
3.7 6.7
6.7
3.3
2.6
3.9
1.5
3.1
Maximum
28.5
6.0
14.5
15.0
15.0
23.5
Maximum
28.5 6.0
6.0 14.5
14.5 15.0
15.0 15.0
15.0 23.5
23.5 12.5
12.5 12.5
31.0
6.5
15.5
Maximum
28.5
12.5
12.5 31.0
31.0 6.5
6.5 15.5
15.5
16
16
14
14
12
Relative error %
Relative error %
12
10
10
8
8
6
6
4
4
2
2
0
1
0
1
2
2
3
3
4
4
5
5
6
7
8
Method No.
6
7
8
9
9
10
10
11
12
11
12
Method No.
Figure 7. Box plot of the methods’
(Table 1) relative errors.
Figure 7. Box plot of the methods’ (Table 1) relative errors.
Figure 7. Box plot of the methods’ (Table 1) relative errors.
12
1212
2.52.5
2.5
11.0
11.0
11.0
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6
64
Relative
error %
Relative
error %
42
20
0
50
100
150
200
0-2
0
50
100
150
200
-2-4
Distance (cm)
-4-6
Distance (cm)
-6
Figure
8. The
dependence of
of error
position
of sensors
with respect
vehicles
the second
Figure
8. The
dependence
errorononthethe
position
of sensors
with to
respect
tousing
vehicles
using the
method.
second method.
Figure 8. The dependence of error on the position of sensors with respect to vehicles using the second
method.
8
Relative
error %
Relative
error %
6
8
4
6
2
4
0
2
0
50
100
150
200
-2
0
-4
-2
-6
-4
0
50
100
150
200
Distance (cm)
Figure 9. The dependence of error on the position of sensors with respect Distance
to vehicles (cm)
using the tenth method.
-6
Figure
9. The dependence
of error on the centerline
position of sensors
respect
to vehicles
using8the
tenth
Sensor
placement
near the longitudinal
of the with
passing
vehicle
(Figures
and
9) increases
method.
the precision
of the speed detection. The analysis of the obtained results indicates that if the right
Figure 9. The
dependence
of error
the position
of sensors with
to vehicles
using
the tenth error) to
signal matching
method
of AMR
dataonsamples
is selected,
it is respect
possible
(with an
acceptable
Sensor placement near the longitudinal centerline of the passing vehicle (Figures 8 and 9)
method.
determine the speed and type of the vehicle (considering the length of vehicle). The benefits of such a
increases the precision of the speed detection. The analysis of the obtained results indicates that if the
system
aresignal
as follows:
right
matching method of AMR data samples is selected, it is possible (with an acceptable
1.
2.
3.
Sensor placement near the longitudinal centerline of the passing vehicle (Figures 8 and 9)
error)
to the
determine
and type
of
the The
vehicle
(considering
theclassification
length
of vehicle).
The
benefits
increases
precision
ofspeed
the speed
detection.
analysis
of the obtained
results
indicates
that
ifaccording
the
The
detection
of thethe
vehicle
location,
measurement
of speed
and
of vehicles
of
such
a
system
are
as
follows:
right
signal
matching method of AMR data samples is selected, it is possible (with an acceptable
to their
type.
error)
toinfrastructure
determine
and type
of the vehicle
(considering
the length
of vehicle). The
1. The
detectionthe
of speed
the vehicle
location,
measurement
of speed
and classification
of benefits
vehicles
Lower
costs.
of such
a
system
are
as
follows:
according to their type.
Relatively modest amount of data to be transferred due to the fact that signal processing can be
2. The
Lower
infrastructure
1.
detection
of the costs.
vehicle location, measurement of speed and classification of vehicles
performed
in
the sensor
itself.
3. according
Relativelytomodest
amount of data to be transferred due to the fact that signal processing can be
their type.
performed
inResearch
the sensor
itself.
2. Lower
infrastructure
costs.
5. Ideas
for
Further
3. Relatively modest amount of data to be transferred due to the fact that signal processing can be
There
is great in
potential
for
further research into the use of AMR sensors in the presence of a
performed
the sensor
itself.
different Z component angles (i.e. at different locations across the Earth) and for analyzing different
types of signal processing methods for vehicle speed detection. Moreover, there is still a knowledge
gap in terms of researching vehicle detection using the magnetic vehicle signature features.
6. Conclusions
By experimental research it has been established that AMR sensors can be used for the purposes of
vehicle speed control and vehicle classification. Upon conducting research with regard to the different
Sensors 2016, 16, 78
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impact of distinct vehicle types on the Earth’s magnetic field it has been established that different
vehicles have distinct magnetic signatures which can be used for identifying the vehicle type.
According to the research results it can be concluded that is sufficient to use only one AMR Z
component for vehicle speed detection. Upon analyzing the 12 methods found suitable for the purpose
of speed control, it has been established that the minimum errors are obtained using the Z component
for methods 2 and 10 (the Z component cross-correlation and Kz cross-correlation criterion methods).
When using the proposed methods the average relative error of speed determinations in the case of
applying the most suitable method does not exceed 1.5% when the distance between sensors is 2 m.
It should be noted however that the vehicles can affect measurements by adding additional errors.
These can be eliminated through other magnetic field components.
Author Contributions: V.M. supervised the project direction. V.M., M.C. and A.V. designed the hardware. D.N.,
D.A. and M.Z. implemented the hardware. M.Z., D.N. and D.A. evaluated the system by developing software.
D.N. developed the final software. V.M and M.C. analyzed the data. V.M., D.N. and M.C. wrote the initial
manuscript; M.Z., D.A. and A.V. participated actively in the development, revision and proofreading of the
final manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
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© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons by Attribution
(CC-BY) license (http://creativecommons.org/licenses/by/4.0/).