Use of RFID Tags as GPS Alternative in Autonomous Vehicles: A Study

This report proposes an RFID-based positioning system as an alternative to GPS for autonomous vehicles, particularly in areas with poor or unavailable GPS signals. The system utilizes RFID tags installed on roads and readers on vehicles to determine location. Information from the tags, along with a kinematic integration algorithm, is used for vehicle positioning. The report addresses potential errors from acceleration and deceleration through a calibration algorithm. It includes an introduction covering background information, aims, objectives, and research scope. A literature review covers GPS limitations, related studies, and self-driving locations. The methodology section details the RFID-based positioning system's working, experimental design, data collection methods, and analysis. Experiments are conducted using radar, photoelectric switches, and ns-2 simulation software to verify the system. The results section presents experimental findings, followed by a discussion. Finally, the conclusion summarizes the findings, recommendations, and potential future work, highlighting RFID's potential in autonomous vehicle navigation.

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USE OF RFID TAGS AS AN ALTERNATIVE
TO GPS IN AUTONOMOUS VEHICLE

ABSTRACT
This report proposes the usage of an RFID based positioning system which will be used as an
alternative means for GPS where the GPS services are not available or signals are poor. Using
this approach of positioning for the vehicles, the RFID tags will be installed at the surface of the
roads and the RFID tag readers will be installed at the base of the car. When the vehicle passes
over the tag, the reader which is located at the bottom of the car activates the tag which shared or
rather detects the tag with its details. In the tag, there are some information which can be read by
the reader. The tag information details are the information related to the position of the tag which
further translates to the position of the vehicle. In order to fill the gap which exists between the
tag, estimation technique has to be employed on the basis of the latest position which is updated
in the tag, this estimation can be best served using the kinematic integration algorithm. When the
vehicles decelerate or accelerates, the speed obviously will have to change, this change in speed
due to acceleration and deceleration will affect the accuracy of the method of estimation. This
error which exist can be removed or reduced by the calibration algorithm which is proposed in
this particular paper.

Table of Contents
ABSTRACT.....................................................................................................................................1
Table of Figures................................................................................................................................4
List Of Tables...................................................................................................................................4
CHAPTER ONE...............................................................................................................................5
1.0 INTRODUCTION......................................................................................................................5
1.1 Background Information.........................................................................................................5
1.2 Aim and Objectives................................................................................................................7
1.3 Research Brief.........................................................................................................................7
1.3.1 Project Overview..............................................................................................................7
1.3.2 Motivations......................................................................................................................7
1.3.3 Ideas and Research Priority..............................................................................................8
1.4 Project scope...........................................................................................................................8
1.5 Contribution............................................................................................................................8
1.6 Report Structure......................................................................................................................9
CHAPTER TWO............................................................................................................................11
2.0 LITERATURE REVIEW.........................................................................................................11
2.1 Introduction...........................................................................................................................11
2.2 Research Gap In Vehicles Positioning..................................................................................11

2.3 The Global Positioning System............................................................................................12
2.4 Related Studies.....................................................................................................................12
2.5 Self-Driving Locations.........................................................................................................17
CHAPTER THREE........................................................................................................................20
3.0 METHODOLOGY...................................................................................................................20
3.1 Introduction...........................................................................................................................20
3.2 Research Problem and Philosophical Questions...................................................................20
3.3 Selection of Research Methodology.....................................................................................20
3.4 The working of RFID-Based Positioning of Vehicles..........................................................21
3.5 Summary...............................................................................................................................24
CHAPTER FOUR..........................................................................................................................26
4.0 EXPERIMENTS.......................................................................................................................26
4.1 Introduction...........................................................................................................................26
4.2 Experimental Design.............................................................................................................26
4.2.1 Experimental verification Based on the Radar...............................................................27
4.2.2 Experimental verification based on the Photoelectric Switch........................................28
4.2.3 Experimental verification using the ns-2 simulation software.......................................29
4.3 Data Collection.....................................................................................................................29
4.3.1 Location Identification based on Dynamic Active RFID calibration............................30
4.3.2 LANDMARC algorithm................................................................................................30

4.3.3 Vehicle location system.................................................................................................31
4.3.4 RFID tags.......................................................................................................................31
4.3.5 Active tag.......................................................................................................................31
4.3.6 Controller components...................................................................................................33
4.3.7 Workflow of vehicle controller......................................................................................34
4.3.8 RFID in transportation...................................................................................................36
4.4 Data Analysis........................................................................................................................37
4.5 Summary...............................................................................................................................38
CHAPTER FIVE............................................................................................................................39
5.0 RESULTS.................................................................................................................................39
5.1 Introduction...........................................................................................................................39
5.2 Verification based on Radar.................................................................................................39
5.3 Using Photo Electric Switch.................................................................................................40
5.4 Discussion.............................................................................................................................41
CHAPTER SIX..............................................................................................................................43
6.0 CONCLUSION........................................................................................................................43
6.1 Conclusion............................................................................................................................43
6.2 Recommendation..................................................................................................................44
6.3 Future Work..........................................................................................................................44

Table of Figures
Figure 1: Layout of RFID tags and reader......................................................................................21
Figure 2: RFID tag, antenna, reader, and controller unit................................................................22
Figure 3: The connection of RFID reader and ECU.......................................................................23
Figure 4: Kinematic integration algorithm.....................................................................................23
Figure 5: Setup of experimental verification based on radar.........................................................27
Figure 6: Setup of experimental verification based on photoelectric switch.................................28
Figure 7: LANDMARC algorithm.................................................................................................30
Figure 8: Local information of a vehicle........................................................................................31
Figure 9: RFID tags........................................................................................................................32
Figure 10: RFID tags for positioning.............................................................................................32
Figure 11: How RFID works..........................................................................................................33
Figure 12: Vehicle controller architecture......................................................................................33
Figure 13: Workflow chart of vehicle controller............................................................................34
Figure 14: Tracking vehicle with RFID tags..................................................................................35
Figure 15: Results of experimental verification based on radar.....................................................38
Figure 16: Results of experimental verification based on photoelectric switch.............................39
List Of Tables
Table 1: Definition of the data stored in RFID tags.......................................................................22

CHAPTER ONE
1.0 INTRODUCTION
This is the first section of the report where the topic is introduced, a brief background information
related to the topic is introduced for the purpose of creating foundation of understanding the topic
under research. The main aim of carrying out research is also discussed, a brief information of the
work which is expected to be done in the report including the motivations, priorities, ideas as
well as the expected results are discussed. And finally the section ends with the contribution
which defined what the report will be adding to the existing manuscript.
1.1 Background Information
In early 2014, the depart of transportation in the united states announced officially of the decision
to adapt the vehicle to vehicle technology of communication for the vehicles which are light. This
came into its final implementation in around 10 years after where the department gave the
communication technology a clean bill of right for the cars to be able to communicate with each
other (Prinsloo & Malekian, 2016). At this time when the vehicle to vehicle communication
technology was getting track, the Global Positing system had also been in use for a couple of
years. So many applications of innovations had already been tested in this particular case,
including the cooperative collision warning, intersection movement assistance, intersection safety
support among other innovative applications which were present at that time (Xu, 2018). In this
particular applications, the real time positioning of vehicle was assumed in terms of the
algorithms as well as the protocol for the movement guidance, interaction with many other
vehicles as well as operational controls. This assumption is reasonable enough because the global
positioning system GPS technology is available widely and also affordable. Currently, so many

vehicles have already been equipped with GPS for the purpose of tracking as well as navigation.
However, the Global Positioning system based for the vehicles showed some limitations as the
connected vehicles were been advanced to the implementation of the real world as well as the
fact that the success of the other applications was depending heavily on the accuracy of the
position of the vehicles (Wiseman, 2018).
These limitations which was being faced by the GPS application includes poor or rather lack of
signals in some of the areas including the urban canyon and the accuracy of positioning in the
dynamic environment. In order to address the above named and mentioned limitation for the
GPS, the paper proposes an alternative supplementary but also independent approach which is the
Radio Frequency Identification RFID based vehicle positioning in order to facilitate the
connection of vehicles applications at those areas where the GPS services is either not reliable or
not available (Ali & Hussein, 2017). In this advanced moving world, every day is an innovation
day with new technologies. One of the latest technologies, is an autonomous vehicle or Self-
driving vehicle. World can get many benefits from self-driving vehicle as there will be no driver
one can travel and plan long journeys without any causalities and rest. As an autonomous vehicle
does not need any rest for travelling a long journey, other advantage with an autonomous vehicle
is children under eighteen years of age can travel and own their own car because they no need of
driving the car and they can travel where ever they want without any driver (Arthikeyani et al.
2018).
Self-driving vehicles rely on GPS tracking system for mapping the routes, which navigates the
vehicle to move from one location to the desired location. As nowadays, most of the vehicles and
drivers are using GPS tracking system to travel from one place to other by applications like i.e.;
google maps, Waze-route, Maps and many other applications are helping to travel with easy

routes. On the other hand, in some places like urban areas and in tunnels driver gets confuse
when an application stops working because of a lost signal from satellite, but for a driver he
drives his car on the same route till he gets back the signal and then he can continue his journey
by following again the application (Lu et al. 2018). If the same thing happen with the self-driving
car then can self-driving car travel or get stooped at same location as it lost its route. This paper
will shed a light on various aspects of this issue.
1.2 Aim and Objectives
The main aim of the study is to provide an alternative application and technology to GPS when
the signal is lost, and vehicle is stuck in between for self-driving cars, with the help of RFID tags.
To design a system when there is no network coverage for GPS system then vehicle starts
connecting to the active RFID tags to reach the desired destination.
1.3 Research Brief
1.3.1 Project Overview
The research is on the independent approach which is the Radio Frequency Identification RFID
based vehicle positioning in order to facilitate the connection of vehicles applications at those
areas where the GPS services is either not reliable or not available.
1.3.2 Motivations
As there are everyday new innovations in the current world. The most awaited innovation is
future self-driving vehicle which helps people who could not drive the vehicle but has desire to
travel alone in their vehicle. With the future innovation of an autonomous vehicle an elderly
person, disable person everyone can travel and plan their journeys. To provide this innovation
with an effective aspect as when self-driving vehicle travel on GPS system if in any place where
there is an outage of GPS. Then to provide high accuracy to vehicles on roads in places where

network coverage fails to navigate the system to reach the desired location. The main purpose is
to design a system which navigates in the absence of GPS tracking system.
 To provide an alternative to GPS tracking system
 To provide an accurate application
 To provide a simple route with the help of RFID
1.3.3 Ideas and Research Priority
The idea behind this research is to device a way under which the vehicles can be able to be
located in areas where the GPS signals and services are not available. The priority of the research
is safety of the people, travelling in a self-driving vehicle. When there is lost of GPS network in a
vehicle then it should not stop in middle of motorway or road where there is a high chance of
causality. Therefore, there is need of a system which starts in a fraction of seconds of a lost GPS
tracking system without any effect on self-driving vehicle. Which must connect with an active
RFID tags because, Active RFID tags has long range compare to passive tags.
1.4 Project scope
Scope of the research is to provide an alternative system or backup in the absence of global
positioning system for a self-driving vehicle to travel from vehicle current position to destination.
Which manages the owner of a self-driving vehicle to reach the destination without driving when
the GPS system is down. Because the Self-driving vehicles might be mostly used by a disable
person or any elderly person or under age as they cannot drive the vehicle due to some
conditions. This research will help them to be stress-free even I lost of GPS (global positioning
system) as they can reach there destination by connecting to nearest RFID tags.

1.5 Contribution
Autonomous vehicles rely on GPS tracking system to travel from one place to other. GPS signal
covers when it is connected to four satellite signal at a time when there is no network coverage
GPS signal doesn’t react to system. In this case, the proposed system is by implementing a
vehicle controller in self-driving car and with the help of RFID tags, vehicle can connect with
near by active tag and follow the route by connecting continuously routes RFID tags till it is
connected to GPS tracking system again and reach to desired location.
1.6 Report Structure
It is very much important to understand that the report will be focusing on the aspects that’s
are centered to the use of RFID as an alternative for GPS. This report therefore is divided into six
major sections as each section will be focusing on different report aspects. This sections includes;
Chapter One: Introduction
This is the first section of the report where the topic is introduced, a brief background
information related to the topic is introduced for the purpose of creating foundation of
understanding the topic under research. The main aim of carrying out research is also discussed, a
brief information of the work which is expected to be done in the report including the
motivations, priorities, ideas as well as the expected results are discussed. And finally the section
ends with the contribution which defined what the report will be adding to the existing
manuscript.
Chapter two: Literature Review
This is the second section of the report which gives the related work which was being done by
other researchers related to the current topic under investigation. All the related work and
researchers are discussed in details here.

Chapter Three: Research Methodology
This is the third section of the report. In this section, data is collected regarding the topic
foundation, this will help in development of the system which will help in achieving the set
objectives.
Chapter Four: Experiments
This is the fourth section of the report. Experiments are carried out here using the data
collected at the methodology stage in order to achieve the set objectives.
Chapter Five: Results
This is the fifth section of the report where the outcome of the experiments done are presented
in an analysis form so that the reader can be able to create conclusion.
Chapter Six: Conclusion
This is the last section of the report. In this section the report is concluded by summarizing the
main points which were done and taken note of in the report for drawing recommendations. This
section relies on the results which were obtains in the experimentation section.

CHAPTER TWO
2.0 LITERATURE REVIEW
2.1 Introduction
This is the second section of the report which gives the related work which was being done by
other researchers related to the current topic under investigation. All the related work and
researchers are discussed in details here.
2.2 Research Gap In Vehicles Positioning
Because of its wide inclusion and accessibility, GPS is by all accounts perfect for associated
vehicle applications. Independent GPS has the capacity of accomplishing exactness of around
20m to 30m, that might be limited from 8m to 12m after evacuation has been done on the specific
accessibility (Chen et al., 2013). Differential Positioning System can upgrade precision further to
around 1m. Notwithstanding, it depends on reference stations based on the ground that just
covers constrained regions and, along these lines, essentially drives the expense. In associated
vehicles, prerequisite on the vehicle positioning changes with the idea of utilization. When all is
said in done, those including an enormous spatial and transient degree, for example, roadway rate
help and dynamic steering, don't require exact situating refreshed at a high recurrence (Deacu,
2014). Interestingly, applications in little spatial and fleeting zones, for example, movement
control particularly crash evasion, require precise positioning continuously.
Joined with unfavorable areas, for example, urban gully, these circumstances represent an
incredible test to GPS-based situating. All things considered, further improvements of GPS as
well as the elective vehicle positioning advances are called for. Subsequently, various
methodologies were proposed with inclusion of the inertial frameworks, dead retribution, data
combination, and also the guide coordinating. In order to improve the positioning systems using

the GPS, a typical decision is to coordinate it the with inertial frameworks. For instance,
according to (Dominikus and Kraxberger, 2011), executed a continuous bearer stage DGPS
supported inertial route framework which can accomplish a precision almost 1cm level. (Kumar
et al., 2014), utilized a Kalman channel in order to consolidate in-vehicle movement sensors in
the improvement of the vehicle position.
2.3 The Global Positioning System
GPS (Global Positioning System), is a satellite system in orbit which sends continue signals to
ground which monitors and controls the operations of GPS. The signals are detected by GPS
receiver and then it calculates the precise location of the receiver around 10 meters.
The GPS was first intended and introduced and used by military force of United States to detect
the submarines or any other ships in 1960 (Mamei and Zambonelli, 2016).
GPS satellites are circle earth in three planes, and a group over equator. GPS takes signals from
satellite in orbit around earth. This radio signals contains the information about position and time
of satellite, GPS will subtract current time from the time radio signal sent. Then difference
between time is multiplied by speed of light. It also uses trigonometry for calculating the position
of satellite and distance where the signal is coming from, GPS only works if it relates to three or
four satellites for solving geometric equation. GPS can calculate its position multiple times in one
second (Maruthaveni, 2017).
2.4 Related Studies
Based on embedded Linux system Maruthaveni, (2017) proposed a GPS and CDMA application.
Intelligent management system was proposed by Mamei and Zambonelli, (2016) was a rapid
growth in industry, which helps an institution in managing their vehicle fleet effectively with the

help of smart resources allocation. The proposed system incorporates power of Global system
and mobile communications and Global Positioning system with front end and management
software. Which tracks with high accuracy where GPS system is unavailable. The studies found
GPRS using IP for monitoring vehicle with other systems like GPS and RFID, Bluetooth also
utilize for receiving and transmitting location (Park and Lee, 2013).
Park and Hashimoto, (2010). Introduces a new vehicle monitoring system with the help of
combining both GPRS and GPS navigation system. GPRS (General packet radio service) and
GPS (Global Positioning System). By combining GIS and Internet technology Park and Lee,
(2013) achieved vehicle tracking monitoring system. (Qian, Guo and Tian, (2013) Proposed and
later implemented an application for integration and enhancing public transport for Punjab in
Pakistan, based on GPS and GSM. The system consists of four modules In-module, BUS stop
module, BUS station, Base station module which is equipped on GSM and PC. P is designed in a
way to keep data of every bus. The information regarding license number of bus and plate
number is sent from BUS station module to IN BUS using SMS. The GPS receiver in IN-BUS
module starts transmitting location to base station which is interfaced to PC which save data and
update the location of bus.
Sisa, (2017) proposed a system called GPS-Tracking which is composed of both hardware and
software system which enables with google maps by using GPS tracking system to track a
vehicle. By using GPRS and GPS for message transmission with an 8bit AVR microcontroller.
Sung et al., (2012) have also developed a real-time vehicle tracking system by installing In-
vehicle unit and a remote server which tracks a vehicle. The information in this system is
transferred in form of SMS or TCP/IP connection. The server then stores the information in

database which is available to an authorized user via internet. With the help of sensors like
proximity, Ultrasonic, vibration sensor Tsumura, (2016) Designed an In-vehicle monitoring
system for measuring a distance. GSM modem helps the system to send and receive the data and
sending SMS to GPRS data on GSM network.
Xiao, Zhang and Liang, (2011) designed a tracking system with embedded sensor and modem of
GPS and GSM for sending position of vehicle from remote area to mobile on another end from
the place where vehicle is demanded. Where an EEPROM follows the path, which ensure the
four satellites are visible all the time from any point on earth within any given time. GPS signals
which received the signals are decoded by GPS receiver, to use this on smart phones GPS must
install GPS -receiver, but without the rest of system this receiver is useless.
GPS consist of three segments, space segment, control and user segment. At the rate of 50 bits p/s
space segment (satellite) sends navigation continuously. When the time message is sent with
exact orbital information and the general status and rough orbits of all the satellites is the main
information of space segment.
To use and monitor the health of all the satellites, the control segment is composed of many
numbers of stations also do the corrections in orbit data when needed and adjust the clock of all
satellite.
The user is composed of stable clock and software which calculates the location and speed of a
user. To calculate location of user, distance must be defined by GPS receiver using multiple
satellites. It is done by using ephemeris data which includes the data transmitted by receiver from
satellite.
Zhang, Chen and Hu, (2014) proposed a system of tracking vehicle by using GPRS, GPS and
data coding. Which uses web-interface to check the location and number of vehicles by installing

embedded receivers inside the vehicle. IN-vehicle unit capture the location and stores the
information in database. These data sent to track server which depends on operation cost and
reliability of in-vehicle unit.
RFID (Radio Frequency Identification) an automatic system for identifying people and other
products. By using radio frequency for identifying the tagged items, after which data collects the
transmitted system using RF reader. The transmitted data provides the information presents n the
tag by specifying type, color, price or any other available data and date of purchase. On October
20, 1949, Woodland and Silver patent application for “article classification through identifying
patterns”.
In World War-II, Germans, Americans, and British were using radar warn of approaching fighter
planes from miles away. The problem was to differentiate between own plane and enemy plane.
Germans discovered that pilots must rolled their planes when returning which alerts the radar
crew this was the first passive RFID system by Germans.
Aadil, (2012), proposed a design for GPS in autonomous vehicle by memorizing technique for an
autonomous vehicle by using pre-saved maps which also detect bumps and avoid obstacles.
The system presents the designed mechatronic system in a vehicle which consists of wire controls
and all sensors and hardware. By connecting different sensors and all actuators to the position. It
navigates the vehicle for that driver must drive the vehicle on the route for one time, which will
be saved in data for future use for the vehicle in the absence of GPS.
Based on(SINS) Inertial Navigation system and visual sensors Ho, Abdullah and Mokhtar, (2014)
proposed a system which enables an autonomous vehicle by inertial-visual fusion method. It is
first time I autonomous vehicle system which combines line feature with image in SINS.

Navigation and mapping in autonomous vehicle, to navigate the vehicle by using communication
by internet and modems Ho, Abdullah and Mokhtar, (2014), proposed a design using GPRS,
GPS, navigation compass, inertial and laser sensors. The system is designed in a way which can
be monitored and controlled all over the world through internet. By using the appliances of
geodata from OSM (Open Street Map) for autonomous navigation. In this system an autonomous
vehicle senses its surroundings by radar, laser and computer vision. The navigation path is
defined in three competences, Localization which denotes robot’s ability for defining its own
position within a frame. Path defines computation to reach destination from robot position. The
controller reacts on avoiding obstacles o global path to reach destination.
In 2012, 5th Mechatronics and Robotics conference in South Africa, (Li, Fang and Li, (2013),
proposed a localization and mapping when no GPS coverage by RFID. This research illustrates
the SLAM (Simultaneous Localization and Mapping) in the urban area or any underground
mining area. This research indicates that Tracking fast lane for an autonomous vehicle in urban
area by using Markova models and Hough transform. Kaplan proposed a design of fast lane for
tracking an autonomous vehicle, tracking lane is an important in future for autonomous vehicle as
navigation stand between specially in urban areas. The methodology was to design a lane
tracking by using partitioning technique to obtain multi-resolution Hough of acquire data.
Maruthaveni, (2017) proposed a system by explaining that a Car - navigation system has two
issues, currently an autonomous vehicle is facing issues how to start from current location to
destination, and current location of the self – driving vehicle.

2.5 Self-Driving Locations
The basic autonomous car navigation system works in six categories. Location system, map-
matching, digital map database, vehicle location, path – planning, and driving route.
Location system is further divided into three categories, relative location, hybrid location,
absolute location. In a relative location, current position of a vehicle is acquired by adding total
moving distance and the direction to prior position. By combining with the prior location of a
vehicle, current location of a vehicle is calculated. The absolute location of a vehicle method is to
locate the vehicle as per the information obtained from positioning system through satellites like
global positioning system, GLONASS. In urban environment such as tunnels, buildings and
severe weather conditions the signal from satellite causes error, noise in he location signal. Thus,
this type of absolute location is currently not accurate for self-driving vehicles. Coming to hybrid
location of a vehicle location system, it combines the two relative location and absolute location
methods. This method is commonly used in self-driving vehicles for tracking their current
positioning system. Najera, Roman and Lopez, (2013) based on GPS global positioning system
and INS inertial navigation system GPS/INS proposed a new cross road turning method system
for a vehicle. According to these methods of Zhu et al, by pre-defining map vehicle turning can
be achieved.
Another proposal named Boss by Carnegie Mellon University Najera, Roman and Lopez, (2013),
by use of GPS sparse data combined with imagery for locating self-driving car on roads.
Electronic map (EM) is used for storing information of maps, which includes all the information
of geographic, building information, traffic information, and all road facilities. Currently an
Electronic map for self-driving car is already present named HD. HD map is also divided further
in three layers 1. Active layer 2. Dynamic layer. analytical layer. Active layer consists of shape of

the road, direction on the road, curvature, laying all other objects like trees, road edges,
landmarks on roads and other target areas are also elevated in active layer of HD map. Dynamic
layer updates the data like traffic flow and real-time locations with the help of other vehicle
sensors and other road sensors (Najera, Roman and Lopez, 2013). There is an update on all
surroundings of HD map named integrated collaboration. Analysis type of layer helps in self-
driving car for analyzing real-time available big data of all human and other records. At present,
there is an advance driver assistance system (ADAS) map has the information and accuracy of 1-
5m.
Map matching is also the foundation of path planning which calculates the location of the car by
using the geographical information available from the global positioning system GPS and integral
navigation system INS and also the available information from electronic map. During this
calculation an advanced fusing method is employed which fuse the latitude and longitude and
then coordinates the information on electronic map. From this point of view by practically the
location of a vehicle should be accurate location and time efficient (Santerre and Geiger, 2018). It
is an important factor of finding a better method to fuse information from integral navigation
system INS and global positioning system GPS. When the signal from GPS global positioning
system and INS integral navigation system, therefore a good fusion of data method which can
integrate the available information from an existing location and other route will enhance the
reliability, robustness and accuracy of a vehicle (Zheng, Feng and Bai, 2013).
A research hotspot which make the use of vehicle running in map matching by narrative method
for solving map matching proposed by (Santerre and Geiger, 2018).
(HMM) algorithm Hidden Markov Model, heuristic algorithms proposed by (Thompson, 2014)
are some of the competitive algorithms in the methods of map- matching.

Global path planning of a self-driving car which determines the optimal path of driving between
the starting point of a vehicle to ending point of a vehicle. Some of the algorithms used for global
path planning are such as Dijkstra algorithm and ford algorithm, Floyd algorithm, Bellman-
algorithm and heuristic algorithm proposed by (Thompson, 2014) are some of the algorithms
which fuse the electronic map information for calculating optimal path of a vehicle.
Control of a self-driving vehicle includes the speed of a vehicle and direction of a vehicle. For
achieving speed of a vehicle and direction of a vehicle the information of an electronic map
including traffic -regulations, weather perception, status of a vehicle, driving target and
knowledge of driving and other traffic regulations must be fed into perception module
(Thompson, 2014). Then algorithm of a self-driving vehicle performs calculation of target. Later
which is passed to vehicle control system of a vehicle. Those instructions are followed by or
executes to control the direction of a vehicle and speed of a vehicle with all other ignition lights,
headlights , horns are instructed by vehicle control system (Wang and Gao, 2014).
In self-driving vehicle the core component is control platform which controls the various systems
in a vehicle, such as braking antilock system, the auto sensing brake control, electronic break,
ABS (auxiliary braking system), auto power steering system, variable transmission, cruise control
of a car system, electronic suspension control and many other related controlling systems are
controlled by vehicle control platform (Wang, Qin and Jin, 2019). Control platform consists of
further two type of parts, communication bus and (ECU) electronic control unit. Control unit
(ECU) implemented by control algorithm and communication bus communicates between
mechanical and ECU parts.

CHAPTER THREE
3.0 METHODOLOGY
3.1 Introduction
The inspiration driving the Research technique is to grasp the degree, strategy for thinking and
the fundamental research way to deal with furthermore giving focal point of the especially
investigated and practiced procedures for driving this particular research. This part will include
most of the above point by point discussed features in logically natty coarse structure. There are
number of subsections used for outlining this segment in this investigation report. The principal
part of research report is the detail delineation of the detail how the investigation is coordinated.
3.2 Research Problem and Philosophical Questions
The major issue was the design of an autonomous vehicle. As vehicle completely rely on GPS
tracking system. The question arises if There is no GPS network coverage in areas whether the
car stops on the same location or move to some other route. Another hurdle is if a system is
following the GPS and on its way to destination there is an ongoing construction, then how will
the system reacts do it take any detour without notification. Mapping all the instructions and
signs on the route how self-driving vehicle will followed sign boards as this all have to mention
on the system. For instance; Australia is around 5 miles including all the gateways, round-about,
drive-ways and each street along with the sign boards of speed, school-zone, pedestrian crossing
must be mapped on the system which is daunting task.
3.3 Selection of Research Methodology
In order for the methodology to be instinctive and progressively exact, there are some factor
which should be examined, from these components, the procedure will be plainly observed
(Vaščák & Hvizdoš, 2016). The purpose behind research system is to perform real assessment

and evaluate the investigation issues. By the day's end, the explanation behind the fragment is to
recognize the crucial strategies and strategy by which the assessment can perform fitting desire
and depict the technique of the investigation work. For the proposed research work, Quantitative
strategy for this situation is significant. This kind of philosophy is helpful for itemizing and
directing filled research strategies and needs.
3.4 The working of RFID-Based Positioning of Vehicles
The RFID tags are considered to be a series of RFID which are fastened on the surfaces of the
road. The tags contain the position data which includes, the lane number, the distance to the point
of reference as well as the direction of travelling (Hwang, 2017). When the vehicle passes by the
tag, the RFID reader which is in the vehicle will activate the tag and then it will read the position
data in the tag. This is shown in the below figure.
Figure 1: Layout of RFID tags and reader.

Figure 2: RFID tag, antenna, reader, and controller unit.
The table below shows the format under which the infromation in the tag is represented.
Table 1: Definition of the data stored in RFID tags
In the above setup, the panel antenna version XCAF-12L from China is used. The antenna has a
rugged directional UHF having a central frequency of about 915 MHz as well as the polarization
which is circular. The RFID reader which is used is the Invengo XCRF-502E whose frequency of
working is around 902MHz with a range of working of only 10m (Ferguson, 2017). The RFID
tags which is used is ZT-T80s having an effective range of around 100m and the speed of its
identification was 200km/h. In order to facilitate the communication that should exist between
the tags and the readers of the RFID, the electronic circuit controller Unit is developed. As shown
in the figure above, the electronic circuit controller unit is utilized in controlling the reader using

the RS232 as well as transferring data to the other modules using the CAN bus (Nanda et al,
2019). The connection of the Electronic Circuit Controller unit, the RFID reader and the CAN
bus is shown in the figure below. The electronic circuit Controller unit is made up of the power
module, as well as the Central processing unit on the Chip of Motorola device.
Figure 3: The connection of RFID reader and ECU.
Due to the fact that the RFID tag is made up of the static position data which is fixed at a static
location, there is a need for the vehicles which are moving to gain an accurate position in a
fashion which is continuous for the purpose of supporting the vehicle connection applications.
For this reason, an algorithm based on the kinematic integration is devised and is added to the
RFID system of position as shown in the below figure.
Figure 4: Kinematic integration algorithm.

The algorithm which is utilized in the calculation of the vehicle position is given below;
In this case;
d2 represents the current position
dtag is the position which is stored and obtained from the RFID tag the very last time of
detection.
d1 is the distance which is estimated for driving in accordance with the integral speed. Ftag is a
flag that has a value 1 or 0 so that the system is in a position to read the information which is in
the tag. k is a data sequence number which starts counting when the framework is not in a
position to read the tag and then it is reset to 0 when the reading begins again (Nanda et al, 2019).
v and a are the speed of the vehicle as well as the acceleration respectively, t is representative of
the time which has elapsed since the successful tag reading from the previous tag.
3.5 Summary
GPS works only if it relates to four satellites at a time. Around the world there are many places
where GPS system does not work like Urban areas, tunnels, roads with poor signal coverage. To
use an alternative for GPS in autonomous vehicles we try to build an application using RFID

(Radio frequency Identification). Radio-frequency identification which uses magnetic fields for
automatically identifying and tracking an object with the help of tags which contain electronic
stored information. There are two tags namely, Passive tag and Active tag. Passive tags usually
collect energy from nearby radio waves and Active tags are which uses batteries and covers
hundred meters from nearby RFID reader. RFID tags are used in many industries for tracking
their products and by implanting small chips to an animal to track its position from the monitor.
Even it is used in many shopping malls for clothing and any other valuable item for tracking the
position. For instance; Shopping malls where they use this RFID tags to prevent from theft, so
that no customer can check out without paying or else it will buzz a sound when you try to move
out from the mall.

CHAPTER FOUR
4.0 EXPERIMENTS
4.1 Introduction
This is the fourth section of the report. Experiments are carried out here using the quantitative
data collected at the methodology stage in order to achieve the set objectives. In this particular
section the main aim is to collect the information that is relevant to the topic under investigation
by using related researches as well as self-investigation. From the collected data, experiments are
carried out in order to determine the suitability of the data collected in relation to achieving the
set objectives.
4.2 Experimental Design
The accuracy that the RFID vehicle positioning system can only be affected by the range of
communication as well as the distance which exists between the tags. The reading that are
recorded from the tags only takes place when the vehicle is moving over the RFID tad because
the RFID communicates only within a very short distance, just few meters. Hence in order to
ensure accuracy then the vehicle has to be moving over the tag. in any case the vehicle is not in a
position to get the position updates from the tags as it passes over the tag, then it means that the
position has already been estimated (Nanda et al, 2019). The longer kinematics integration
algorithm runs, the error becomes larger. It is therefore recommended to avoid longer gaps in
between the tags in order to guarantee accuracy. For the purpose of verifying the accuracy and
the feasibility of the positioning approach, then in this experiment, three experimental test have
been proposed in this report one of them based on the radar , the other experimental test based on
the photoelectric switch and the last experimental design is based on the virtual simulation of the
RFID communication using the ns-2 simulation software (Nanda et al, 2019).

4.2.1 Experimental verification Based on the Radar
The core objective of this experiment is to verify on whether the dynamic position which is
obtained from the RFID tags matches the real and correct position of the vehicle as measured by
the radar satellite. As indicated in the figure below, the setup of the experiment is shown whereby
the vehicle is equipped with the radar, antenna and the RFID reader as well. The radar is attached
at the front guard of the vehicle. The radar beam of waves is then oriented forward in the same
direction of the travelling (Ferguson et al. 2019). The antenna in this experiment is installed
below the vehicle guard hence the antenna surface is able to sense the ground. The test road has
the guards installed. The radar has the millimeter wave which has a frequency of 76 MHz that
ranges up to 180m with a resolution of about 0.7m
Figure 5: Setup of experimental verification based on radar.
In this particular experiment, the vehicle is left to pass over the RFID tags several time while the
vehicle is accelerating and decelerating several number of times. The computer on the vehicle
then calculates the distance which is between the last tag and the vehicle utilizing the approach
which is proposed. The radar in the vehicle measures the distance that is in between the fixed
target and the vehicle independently. The results which are found from the two experimental

methods are all transferred to the CAN bus that is logged to the computer in the vehicle
(Weissman, 2019).
4.2.2 Experimental verification based on the Photoelectric Switch
The main objective of this experiment is for verification on whether the position which is
estimated using the kinematic integration algorithm is the same with the position which is
obtained using the RFID tags. The figure below shows the setup of the experiment.
Figure 6: Setup of experimental verification based on photoelectric switch.
The photoelectric switch in the figure above is made up of the transmitter that is fixed at the side
of the road and the receiver that is fixed at the outer part of the vehicle. In order to obtain the
results, then the transmitter has to be in the same cross sectional area as the RFID tag, and the
receiver has to be at the same cross sectional areas as the antenna of the RFID. In this case, when
the receiver moves with the vehicle and it is aligned same with the transmitter, then the RFID and

the switch will be triggered at the same time (Weissman, 2019). Beginning from this very state,
the computer which is on-board starts to estimate the position of the vehicle by using the
kinematics integration algorithm. At the same time, a separate information of the position is
being given out using the RFID tags.
4.2.3 Experimental verification using the ns-2 simulation software
In this experiment, the ns-2 simulation environment is used for simulation of the RFID in order to
determine if it can be used as an alternative for GPS. The simulation of RFID using this
simulation software implements the use of tree walking simulation protocol. Software used for
this research is ns2 software. The Network Simulator software is an open source which runs on
Linux, ns 2 is a discrete which provides support in simulation of TCP (Transmission control
protocol), protocols, wired and wireless networks, routing (Weissman, 2019).
An ns-2 module for RFID attached is unzipped, inside the source code, there is a folder named
ns-allinone-2.35 folder. The ns-2.35 file inside is executed. The position and the version of the tcl
and the tk is indicated. The program is executed and results recorded.
4.3 Data Collection
Since the research is quantitative in nature, then quantitative methods of collecting data was used
in this case. Experimentation method of data collection was employed for this matter. During the
experimental verification based on the radar, the experiment was done where the vehicle is left to
pass over the RFID tags several time while the vehicle is accelerating and decelerating several
number of times. The computer on the vehicle then calculates the distance which is between the
last tag and the vehicle utilizing the approach which is proposed. The radar in the vehicle
measures the distance that is in between the fixed target and the vehicle independently. Another

experiment was done based on the photoelectric switch where the computer which is on-board
estimates the position of the vehicle by using the kinematics integration algorithm. At the same
time, a separate information of the position is being given out using the RFID tags. The results
from the two scenarios are collected and recorded. In the third experiment. The simulation is
done by using the ns-2 RFID simulation module where by the source code for the RFID
simulation is executed in the ns-2.35 software and the tcl and tk data is recorded.
4.3.1 Location Identification based on Dynamic Active RFID calibration
Due to some of the places like tunnels and built, basements, underground tunnel we use
LANDMARC and it must be used into our vehicle positioning. To increase accuracy these
systems provides an idea of having reference tags which helps as (reference points like landmarks
in our daily life). With the help of this propose research there are three advantages. No need of
implementing large and expensive RFID tags. Environment dynamics can be accommodated,
because the reference tags are subjected to same effect in environment as tags to be located.
Finally, it is more reliable and with high accuracy. LANMARC approach can accurate real-time
location by sensing. Reference tags and readers together provides better accuracy of the system.
4.3.2 LANDMARC algorithm
LANDMARC algorithm is a typical method, it is used as a reference point for providing position
of constraints. The major advantage of LANDMARC approach is replacing the large number of
RFID with reference tags. The positioning of a system by LANDMARC algorithm will helps to
locate the vehicle by adding number of reference labels. To determine the position accuracy, both
are important factor reference label structure and nearest labels.

Figure 7: LANDMARC algorithm
4.3.3 Vehicle location system
Physical layer- which consist of RFID tags. The vehicle controller sends magnetic waves to tags
to get the position from tags. With the help of RFID tags installed in lanes by connecting to
nearest tag we can accurate the position of a vehicle in any complex environment.
Figure 8: Local information of a vehicle
4.3.4 RFID tags
RFID system also defines the accurate location. There are passive tags and active tags. Passive
tags does not contain any battery; power to passive tags is supplied by reader when the waves are

encountered by RFID tag, the antenna tag forms magnetic field. Tag sends encoded information
in tag memory.
4.3.5 Active tag
These types of tags have transmitter and own source of power battery. These radio frequency
identification tag enables to provide location and identity. Active radio frequency identification
tags are used for covering long distance for automatically identifying and locating humans, and
animals.
Installing RFID tags in lanes and roads which reads the speed and position of a vehicle with
contact time and cost.
Figure 9: RFID tags

Figure 10: RFID tags for positioning
Figure 11: How RFID works
4.3.6 Controller components
Vehicle location component is composed by Radio frequency Identification (RFID), Light
Emitting Diode (LED) module, clock module, sensing module, core module, global positioning
system (GPS) and global system for mobile(GSM) module.

Functions of vehicle controller
 Clock signal for controlling is provided by clock module.
 Power module for managing power and energy.
 LED displays flicker.
 RFID reader for reading RFID tags.
 Sensing module for sensing the position of a vehicle.
.
Figure 12: Vehicle controller architecture
4.3.7 Workflow of vehicle controller
Vehicle controller works when there is no network coverage of global positioning system. it starts
with start of a vehicle and turn off when the ignition of a vehicle is turn off. When it starts it
connects to network and collect all the sense data from the network and connect to GPS global
positioning system if connection is okay to network then it stores the data Ans save the position
of its vehicle. If it is not connected to network, then it collects the data from RFID radio
frequency identification and saves data till it connects back to network.

Figure 13: Workflow chart of vehicle controller
GPRS module helps the system for connecting to network and sending information. GPS module
helps the system to locate the accurate position of vehicle through global positioning system with
current time and movement of a vehicle speed.
 When the vehicle is in parking lot or urban area, in tunnel and any other place where GPS
network coverage is out of reach then controller in vehicle control mounts the RFID
module for reading the active RFID tags on roads. All the information of RFID tags and
vehicle status gets stored in the memory cell of a controller. Once it is reconnected to
network, the controller continues to upload and transfer the data into cloud and

management terminal. With the help of RFID tags vehicle system will display some
functions are.
 Display the vehicle registration number a name, ID which controller reads on the route.
 RFID tags will correspond to the real-time positioning of the vehicle.
 Location of a vehicle display the current location with RFID tags.
Figure 14: Tracking vehicle with RFID tags
4.3.8 RFID in transportation
Yard and shipping management uses RFID tracking. RFID tags are mounted on motives and
stocks tom identify the owner these all are carried out with the use of database which identify and
save information.
RFID used in transportation systems, in New York city where RFID readers deployed at road
intersections for monitoring the traffic flow.
RFID with an antenna can read and write tag. The reader can communicate with tag through help
of an antenna. After receiving the signal from tag it decodes and pass the information. A reader

will first send a request to tag and after receiving signal from reader, a tag will send its ID to
reader.
RFID utilises Low Frequency at 125-134 kHz and High Frequency at 13.56 MHz and Ultra high
frequency up to 930 MHz, recently more microwave frequency at 2.45 GHz. The range of tags in
RFID can be determined by output power of reader.
4.4 Data Analysis
From the 3 experiments, the data obtained has to be analyzed for presentation purposes. There are
several steps which will be deployed in order to analyze experimental data.
The data obtained from the experimental analysis is validated to ensure that the information
meets the set standards. The first experiment which was on the verification of the RFID using the
radar, data was collected as validated to ensure completeness and correctness of data. This data
will be analysed in graphical form where by the graphs will be drawn in relation to the results
found.
Data obtained from the experimentation of the verification using the photoelectric switch will
also be analyzed using the graphs and tables as illustration of the quantitative methodology.
Data obtained form the simulation using the ns-2 RFID module will be analyzed from the tcl file
output given from the simulation. In this case several means will be used for analysis including
representing data obtained in different charts and graphs.

4.5 Summary
Hence in order to ensure accuracy then the vehicle has to be moving over the tag. in any case the
vehicle is not in a position to get the position updates from the tags as it passes over the tag, then
it means that the position has already been estimated. The longer kinematics integration
algorithm runs, the error becomes larger. It is therefore recommended to avoid longer gaps in
between the tags in order to guarantee accuracy. The accuracy that the RFID vehicle positioning
system can only be affected by the range of communication as well as the distance which exists
between the tags. The reading that are recorded from the tags only takes place when the vehicle is
moving over the RFID tad because the RFID communicates only within a very short distance,
just few meters. Both the radar, photo electric switch and the ns-2 RFID module simulation
experimentation provided the right results which were relevant to the center of investigation.

CHAPTER FIVE
5.0 RESULTS
5.1 Introduction
This is the fifth section of the report where the outcome of the experiments done are presented in
an analysis form so that the reader can be able to create conclusion.
5.2 Verification based on Radar
As shown in the figure below, the comparison of results denote that the distance estimated is zero
at the very beginning because the tag reading is not there at the moment hence there was nothing
for estimation. From the 5th second as shown in the graph below, the reading from the tag
becomes available, also the estimation of the vehicle position begins. The overall results shows
that the radar, estimation match ad the tags are very well.
Figure 15: Results of experimental verification based on radar.

From the beginning of the experiment to the end, it shows the similarity in the results obtained,
the accuracy of the position estimation and detection was similar using all the 3 methods. Though
as seen from the graph, the estimation distance began reading at the 5th second, which the radar as
the RFID tag began reading from the 11th second.
5.3 Using Photo Electric Switch
The results obtained from the experiment were represented in the figure below. From the results,
the error position is almost 5.4&% within the first 30m of vehicle acceleration. This is merely
because the vehicle was in acceleration mode. When the speed of the vehicle stabilizes, the error
drops further from 5.4% to 2.5%. Worth noting, as estimation continues, the error accumulation
increases more and more. Further tests which were carried out using minim speed of about
36km/h shows the reduction of the error above to 3.1% and 1.8% respectively.
Figure 16: Results of experimental verification based on photoelectric switch.

It is also noted that the error that is in the position is majorly derived from the error accumulation
which is caused by the inaccuracy of the velocity. This happens especially when the vehicle
under experimentation is either decelerating or accelerating. Using this data, an algorithm for
calibration is then derived by the use of square method as shown below.
Where delta d is the error of the position, dc is the position calibrated on the basis of the integral,
and a is the acceleration of the vehicle. The estimation of the coefficients are as follows; -1.79
and 0.0613. just after calibration has been done using the algorithm, the error of the very first test
which was 5.4% initially drops to 0.07% and the error of the second experimental test which was
3.1% drops to 0.66%. since there was limitation in terms of time and resources, the research
which was conducted was limited to the above straightforward, transparent, correct and simple
sets of experiments.
5.4 Discussion
However, the results which have been obtained from the above tests indicates that the RFID
which was proposed is very much promising in terms of provision of accurate, trustworthy, fair
and stable vehicle positioning in a very much dynamic process. Before this application has been
done on a large scale or commercial basis, it is also recommended that more experiments to be
done on the photo electric switch as well as the radar tests in order to provide a more realistic and
fair results. This can be done by performing the tests in a more realistic scene, for instance,
involving multiple lanes as well as mixed traffic.

CHAPTER SIX
6.0 CONCLUSION
6.1 Conclusion
This particular paper gives a proposal of an RFID approach which is very much helpful
alternative for the positioning of the connected vehicles in those areas where the Global
Positioning System is either not available or the signals and services are very poor. Using this
approach of positioning for the vehicles, the RFID tags will be installed at the surface of the
roads and the RFID tag readers will be installed at the base of the car. When the vehicle passes
over the tag, the reader which is located at the bottom of the car activates the tag which shared or
rather detects the tag with its details. In the tag, there are some information which can be read by
the reader. The tag information details are the information related to the position of the tag which
further translates to the position of the vehicle. In order to fill the gap which exists between the
tag, estimation technique has to be employed on the basis of the latest position which is updated
in the tag, this estimation can be best served using the kinematic integration algorithm. When the
vehicles decelerate or accelerates, the speed obviously will have to change, this change in speed
due to acceleration and deceleration will affect the accuracy of the method of estimation. This
error which exist can be removed or reduced by the calibration algorithm which is proposed in
this particular paper. The experiments which have been discussed on this paper, duped the road
experiments are carried out in order to validate the proposed approach of the RFID based
positioning system.one of the experiments which involved the radar and the RFID readers and
tags, the radar in that case was utilized in provision of the correct, true and accurate position of
the vehicle, in comparison to the RFID board test which is an estimation test. The aim was to
ascertain that indeed the RFID can be used for positioning alternative. From the results, it showed

good matching between the two sources hence the RFID was given a bill of accuracy. The second
experiment was done with the purpose of verifying if at all the estimation algorithm using the
kinematics integration will match with the position obtained in the RFID tags. In the experiment,
a photoelectric switch was employed in order to trigger the position of the vehicle estimation on
basis of the latest tag which was updated in the RFID tag. from the results, the error which was
noted was about 5.4% which was during the acceleration and deceleration process. At a stable
speed, the error came down to 2.5%. with the assistance of the algorithm of calibration, the error
was dropped to 0.07 from 5.4% and 0.66% from 2.5%. It is also recommended that more tests to
be made in order to ascertain the accuracy and the effectiveness of the RFID.
6.2 Recommendation
From the experiments done and analyzed, it is recommended therefore that RFID can be used as
an alternative of GPS in the areas where the GPS signals and services are either of poor quality of
they are totally not available. From this paper, two major conceptual applications are covered,
one being vehicle control where the RFID gives the most accurate position in order to enable the
prediction of the safety. Also Level IV intersection control has been covered whereby the RFID
position enable traffic conduction more possible and feasible.
6.3 Future Work
With the rapid growth of RFID in the recent times, the future RFID technology will be
implementing the traceable systems as well as the RFID based mobility which will be in
communication with the cloud computing.

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