Alexandria University

Faculty of Engineering

Communication and Electronics Department

B. Eng. Final Year Project

Sensor based Autonomous car By:

Mennatallah Hany Hosny

Nancy Mohamed Abdou

Nagwan fawzyHassan

Nada Ashraf Megahed

Nermeen Mohamed Rmdan

Nehal Salah El-kony

Nourhan Abdelnaser Farrag

Supervised by:

Dr. Mohammed Morsy Farag

I

ACKNOWLEDGMENT

First and foremost, we thank Allah Almighty who paved path for us, in

achieving the desired goal.

We would like to express our sincere gratitude to our mentor

Dr. Mohammed Morsy Farag for the continuous support of our studies

and research, for his patience, motivation, enthusiasm, and immense

knowledge.

His guidance helped us in all the time for achieving the goals of our

graduation project.

We could not have imagined having a better advisor and mentor for our

graduation project.

Our thanks and appreciations also go to our colleagues in developing the

project and people who have willingly helped us out with their abilities.

Finally, an honorable mention goes to our parents, brothers, sisters and

families. Words cannot express how grateful we are. Your prayer for us

was what sustained us thus far. Without helps of the particular that

mentioned above, we would face many difficulties while doing this.

II

ABSTRACT

Whether you call them self-driving, driverless, automated, or autonomous, these

vehicles are on the move. Recent announcements by Google (which drove over

500,000 miles on its original prototype vehicles) and other major automakers indicate

the potential for development in this area. Driverless cars are often discussed as

“disruptive technology” with the ability to transform transportation infrastructure,

expand access, and deliver benefits to a variety of users. Some observers estimate

limited availability of driverless cars by 2020 with wide availability to the public by

2040.

The following sections describe the development and implementation of an

autonomous car model with some features. It provides a history of autonomous cars

and describes the entire development process of such cars.

The development of our prototype was completed through the use of two controllers;

Raspberry pi and Arduino.

The main parts of our model include the Raspberry pi, Arduino controller board,

motors, Ultrasonic sensors, Infrared sensors, optical encoder, X-bee module, and

lithium-ion batteries.

It also describes speed control of the car motion by the means of a process known as

PID tuning to make correct adjustments to the behavior of the vehicle.

III

Table of content 1. Introduction………………………………………………........ 1

1.1 History…………………………………………………….... 2

1.2 Why autonomous car is important…………………………. 4

1.2.1 Benefits of self-driving cars………………………….. 4

1.3 What are autonomous and automated vehicles…………….. 5

1.4 Advanced driver assistance system………………………… 7

1.5 Project description …………………………………………. 7

1.5.1 Auto parking………………………………………..… 7

1.5.2 Adaptive cruise control……………………………….. 7

1.5.3 Lane keeping………………………………………….. 8

1.5.4 Lane departure……………………………………….... 8

1.5.5 Indoor positioning system…………………………….. 8

1.5.6 Connected car…………………………………………… 8

1.6 Related work…………………………………………………. 9

2.Car Design……………………………………………… 12

2.1 First car design……………………………………………….. 13

2.1.1 Four WD robot ………………………………………… 13

2.1.1.1 specifications ………………………………….. 13

2.1.1.2 Real shots ……………………………………… 14

2.1.1.3 Drawbacks ……………………………………... 14

2.1.1.4 Overcoming Drawbacks ………………………. 14

2.1.2 Second trial …………………………………………….. 14

2.1.2.1 specifications ………………………………….... 15

2.1.2.2 Real shots ……………………………………….. 16

2.1.2.3 Drawbacks ……………………………………… 17

2.1.2.4 Overcoming Drawbacks …………………………17

2.1.3 Final design ……………………………………………...17

2.1.3.1 Body ……………………………………………...17

2.1.3.2 Side covers ……………………………………….18

2.1.3.3 Front and back covers …………………………….19

2.1.3.4 Cover ………………………………………………20

2.1.3.5 Real shots ………………………………………….20

2.1.3.6 Drawbacks ……………………………………… 20

2.2 Car wheel drive ………………………………………………………21

2.2.1 Types of wheel drive …………………………………………...21

2.2.2 Advantages of this configurations ……………………………...21

IV

2.3 Car steering …………………………………………………………….22

2.3.1 Basic geometry …………………………………………………...22

2.3.2 Our mechanism …………………………………………………...22

2.3.3 Servo motor modifications ………………………………………...23

2.4 Second car design ……………………………………………………….24

2.4.1 Final design ……………………………………………………….24

2.4.2 Real shots ………………………………………………………….24

2.5 Motors ……………………………………………………………………24

2.5.1 DC motor ………………………………………………………….25

2.5.1.1 DC motor fundamentals …………………………………..25

2.5.1.2 DC motor principle ……………………………………….25

2.5.1.3 Controlling DC motor …………………………………….26

2.5.2 Servo motor……………………………………………………….. 28

2.5.2.1 Servo motor features ……………………………………….28

2.5.2.2 Servo motor mechanism ……………………………………29

2.5.2.3 Inside Servo motor ………………………………………….30

2.5.2.4 Servo motor with Arduino …………………………………..31

3 Microcontrollers and Hardware ………………………………... 33

3.1 Microcontrollers ………………………………………………………… 34

3.1.1 Arduino ……………………………………………………………. 34

3.1.1.1 Introduction ………………………………………………... 34

3.1.1.2 Arduino Mega ……………………………………………... 36

3.1.2 Raspberry pi ……………………………………………………….. 39

3.1.2.1 Design ……………………………………………………… 39

3.1.2.2 ARM 1176 processor ……………………………………… 39

3.1.2.3 Features …………………………………………………….. 40

3.1.2.4 Performance ………………………………………………… 40

3.1.2.5 Function supported in Hardware ……………………………. 40

3.1.2.6 Comparison ………………………………………………….. 41

3.2 Hardware …………………………………………………………………… 42

3.2.1 Ultrasonic sensor ……………………………………………………. 42

3.2.1.1 Introduction ……………………………………………………42

3.2.1.2 Features ………………………………………………………...42

3.2.1.3 Connection with Arduino ………………………………………43

3.2.1.4 Connection with raspberry pi …………………………………43

3.2.2 Infrared tracking sensor ………………………………………………..44

3.2.2.1 Introduction …………………………………………………….44

3.2.2.2 Theory of operation ……………………………………………44

3.2.2.3 Features and pins ……………………………………………….45

V

3.2.2.4 Arduino connection …………………………………………..46

3.2.3 Optical encoder ……………………………………………………….46

3.2.3.1 Introduction …………………………………………………..46

3.2.3.2 Specifications ………………………………………………….47

4 Adaptive cruise control 48

4.1 Definitions………………………………………………………………….. 49

4.2 Introduction ………………………………………………………………….49

4.3 Applications ………………………………………………………………….50

4.3.1 Theory of operations ……………………………………………………50

4.3.2 Sensor options …………………………………………………………..50

4.3.2.1 LIDAR …………………………………………………………..51

4.3.2.2 RADAR ………………………………………………………….51

4.3.2.3 Fusion sensor …………………………………………………….51

4.3.2.4 Ultrasonic sensor ………………………………………………..52

4.4 Algorithm ……………………………………………………………………..53

4.5 Implementation and testing …………………………………………………..54

4.5.1 Steps ……………………………………………………………………..54

4.6 Designing a control system …………………………………………………...55

4.6.1 Design in real systems …………………………………………………..55

4.6.2 Design in our system …………………………………………………….55

4.6.3 Controllers ……………………………………………………………….55

4.6.3.1 P controller ……………………………………………………...55

4.6.3.2 PI Controller ……………………………………………………..56

4.6.3.3 PD Controller …………………………………………………....57

4.6.3.4 PID controller ……………………………………………………57

4.6.4 Definitions of terminologies …………………………………………….60

4.6.5 Distance-speed PID ……………………………………………….61

4.6.6 Speed PID ………………………………………………………….62

4.7 Application in real car …………………………………………………………67

5 Lane keeping …………………………………………………………..68

5.1 Introduction ……………………………………………………………………69

5.2 History …………………………………………………………………………69

5.3 Timeline of available systems …………………………………………………69

5.4 Application in real Car …………………………………………………….......71

5.5 Limitations of Lane keeping system …………………………………………..72

5.6 Prototyping of lane keeping …………………………………………………..72

5.6.1 Basic components ………………………………………………………..72

VI

5.6.2 Algorithm ……………………………………………………………….72

5.6.3 Implementation and testing ……………………………………………..73

5.6.4 Limitations of implemented model …………………………………….77

5.7 Conclusion …………………………………………………………………….77

6 Lane departure ………………………………………………………..78 6.1 Definition ………………………………………………………………….......79

6.2 Introduction …………………………………………………………………...80

6.3 Basic components ……………………………………………………………...80

6.4 Implementation ………………………………………………………………..80

6.5 Algorithm ……………………………………………………………………...81

6.5.1 Concept ………………………………………………………………….81

6.5.2 Algorithm ……………………………………………………………….82

6.6 Application in real car …………………………………………………………82

7 Integrating function …………………………………………………...83

7.1 Algorithm ……………………………………………………………………...84

7.2 Implementation ………………………………………………………………..86

8 Auto parking …………………………………………………………87

8.1 Definitions …………………………………………………………………..88

8.2 Introduction ………………………………………………………………….88

8.3 Application …………………………………………………………………..88

8.4 Hardware …………………………………………………………………….89

8.5 Parking types ………………………………………………………………..103

8.6 Parallel parking ………………………………………………………………104

8.6.1 Parallel parking steps …………………………………………………..104

8.6.2 Algorithm ………………………………………………………………107

8.7 Perpendicular parking ………………………………………………………..108

8.7.1 Steps ……………………………………………………………………108

8.7.2 Perpendicular parking Algorithm ………………………………………108

8.8 Tuning methods ………………………………………………………………109

9 Indoor positioning system ………………………………………....110

9.1 Introduction ………………………………………………………………..111

9.2 Indoor positioning Algorithm ……………………………………………..113

9.2.1 Angle of arrival ………………………………………………………113

9.2.2 Time of arrival ……………………………………………………….114

9.2.3 Time difference of arrival ……………………………………………114

VII

9.2.4 Received signal strength …………………………………………….115

9.2.4.1 Log distance path loss model ………………………………..116

9.3 Available positioning systems …………………………………………….116

9.3.1 Infrared base system …………………………………………………116

9.3.2 Ultrasound base system ……………………………………………...118

9.3.2.1 Ultrasound system application ………………………………118

9.3.3 Ultra-wide band ……………………………………………………...119

9.3.4 Appling radio frequency based system ………………………………119

9.3.4.1 Indoor positioning system using RFID ……………………… 120

9.3.4.2 Indoor positioning system using ZigBee ……………………. 122

10 Connected Car ……………………………………………………. 124

10.1 Definition ……………………………………………………………….. 125

10.2 Function Description ……………………………………………………. 125

10.3 Implementation tools ……………………………………………………. 126

10.3.1 Arduino Yun ……………………………………………………... 126

10.3.2 Web-cam …………………………………………………………. 126

10.4 Steps of implementation………………………………………………….. 127

10.5 Results ……………………………………………………………………. 128

11 Conclusion …………………………………………………………. 129

12 References ………………………………………………………….. 131

VIII

List of Figures Figure 1-1 Google car (Page 9)

Figure 1-2 Progression of automated vehicle technologies (Page 10)

Figure 2-1 Four WD robot (Page 14)

Figure 2-2 Four WD real shot (Page 14)

Figure 2-3 Second design trial AutoCad design. (Page 15)

Figure 2-4 Second car trial real shots. (Page 16)

Figure 2-5 Second car trial connection. (Page 17)

Figure 2-6 Final car design. (Page 17)

Figure 2-7 Final car side covers. (Page 18)

Figure 2-8 Final car front and back covers. (Page 19)

Figure 2-9 Final car cover. (Page 20)

Figure 2-10 Final car real shots. (Page 20)

Figure 2-11 Types of wheel drive. (Page 21)

Figure 2-12 Final car inside view. (Page 21)

Figure 2-13 Final car center of turning circle. (Page 22)

Figure 2-14 Old car design servomotor mechanism. (Page 23)

Figure 2-15 Last car design servomotor. (Page 23)

Figure 2-16 Second car real shots. (Page 24)

Figure 2-17 DC motor theory. (Page 25)

Figure 2-18 Dual H-bridge. (Page 26)

Figure 2-19 Second car motors connection. (Page 27)

Figure 2-20 First car motor connection. (Page 28)

Figure 2-21 Servo motor mechanism. (Page 29)

Figure 2-22 Inside servo motor diagram. (Page 30)

Figure 2-23 Inside servo motor real shot. (Page 31)

Figure 2-24 Connection of servo motor with Arduino. (Page 32)

Figure 3-1 Arduino types. (Page 36)

Figure 3-2 Arduino mega. (Page 36)

IX

Figure 3-3 Rpi memory management. (Page 39)

Figure 3-4 Ultrasonic sensor. (Page 42)

Figure 3-5 Ultrasonic sensor connection with Arduino. (Page 43)

Figure 3-6 Ultrasonic sensor connection with Rpi. (Page 43)

Figure 3-7 Infrared sensor. (Page 44)

Figure 3-8 Infrared sensor pins. (Page 45)

Figure 3-9 IR sensor connection with Arduino. (Page 46)

Figure 3-10 Optical Encoder. (Page 46)

Figure 4-1 Adaptive cruise control in real cars. (Page 50)

Figure 4-2 Operation of LIDAR. (Page 51)

Figure 4-3 Operation of fusion sensor. (Page 52)

Figure 4-4 Operation of ultrasonic sensor. (Page 52)

Figure 4-5 Feedback control system. (Page 53)

Figure 4-6 PID theory. (Page 59)

Figure 4-7 Graph showing PID definition of terminologies. (Page 60)

Figure 4-8 Graph showing dead time. (Page 61)

Figure 4-9 On and off switching method of motor. (Page 63)

Figure 4-10 Unit-step response of a plant. (Page 63)

Figure 4-11 S-shaped response curve. (Page 64)

Figure 4-12 Closed-loop system with a proportional controller. (Page 65)

Figure 4-13 Sustained oscillation with period Pcr. (Page 65)

Figure 4-14 step response of motor. (Page 66)

Figure 4-15 step response with tangent. (Page 66)

Figure 4-16 system performance. (Page 67)

Figure 5-1 Lane-keeping system structure. (Page 71)

Figure 5-2 Lane-keeping flowchart. (Page 73)

Figure 5-3 Showing first car design length. (Page 74)

Figure 5-4 First track material. (Page 74)

Figure 5-5 Final track path and material. (Page 75)

X

Figure 5-6 Final car design length. (Page 76)

Figure 5-7 New connection of Dual H-bridge. (Page 76)

Figure 6-1 Lane departure flow chart. (Page 82)

Figure 7-1 Lane keeping and Lane departure flag. (Page 84)

Figure 7-2 Merging flow chart. (Page 85)

Figure 8-1 H-bridge used with auto parking. (Page 89)

Figure 8-2 Male to male jumpers. (Page 89)

Figure 8-3 Raspberry pi. (Page 89)

Figure 8-4 Second car DC motor. (Page 89)

Figure 8-5 Ultrasonic sensor of second car. (Page 90)

Figure 8-6 Battery holder and batteries. (Page 90)

Figure 8-7 Resistance in second car connection (Page 90)

Figure 8-8 Mini breadboard. (Page 90)

Figure 8-9 Second car DC motor connection. (Page 91)

Figure 8-10 Raspberry pi GPIO. (Page 93 )

Figure 8-11 PWM. (Page 94)

Figure 8-12 Voltage divider circuit. (Page 96)

Figure 8-13 Ultrasonic sensor connection with Rpi. (Page 97)

Figure 8-14 Ultrasonic sensor pins. (Page 98)

Figure 8-15 Ultrasonic real shot connection with Rpi. (Page 98)

Figure 8-16 Second car final shot. (Page 101)

Figure 8-17 Testing ultrasonic code. (Page 102)

Figure 8-18 Parallel parking. (Page 103)

Figure 8-19 Perpendicular parking. (Page 103)

Figure 8-20 Angle parking. (Page 104)

Figure 8-21 Finding parking area. (Page 104)

Figure 8-22 Step 1 for parallel parking. (Page 105)

Figure 8-23 Step 2 for parallel parking. (Page 105)

Figure 8-24 Rest of step 2 for parallel parking. (Page 105)

XI

Figure 8-25 Step 3 for parallel parking. (Page 106)

Figure 8-26 Step 4 for parallel parking. (Page 106)

Figure 8-27 Parallel parking detect space state diagram. (Page 107)

Figure 8-28 Parallel parking state diagram. (Page 107)

Figure 8-29 Perpendicular parking state diagram. (Page 108)

Figure 8-30 Car moving forward for enough space. (Page 109)

Figure 8-31 Sensing perpendicular parking area dimensions. (Page 109)

Figure 9-1 Positioning Satellite in the orbit. (Page 111)

Figure 9-2 Localization systems. (Page 112)

Figure 9-3 Triangulation. (Page 113)

Figure 9-4 An antenna array. (Page 113)

Figure 9-5 The length of the arrows corresponds to the arrival time at receiver P. (Page 114)

Figure 9-6 Positioning based on TDOA measurements. (Page 114)

Figure 9-7 Position measuring. (Page 116)

Figure 9-8 Positioning systems. (Page 116)

Figure 9-9 Infrared base system positioning (Page 117)

Figure 9-10 Ultrasound system application. (Page 118)

Figure 9-11 RFID. (Page 120)

Figure 9-12 RFID reader. (Page 121 )

Figure 9-13 Arduino yun. (Page 121)

Figure 9-14 Layout of the 3 by 3 Grid RFID Positioning System. (Page 122)

Figure 9-15 Zigbee modules. (Page 123)

Figure 10-1 Arduino Yun WiFi connection. (Page 126)

Figure 10-2 uploaded image on drop+box. (Page 128)

1

Introduction

Chapter 1

2

1.1 History

1930s

An early representation of the autonomous car was Norman Bell Geddes's Futurama exhibit

sponsored by General Motors at the 1939 World's Fair, which depicted electric cars powered

by circuits embedded in the roadway and

controlled by radio.

1950s

In 1953, RCA Labs successfully built a miniature

car that was guided and controlled by wires that

were laid in a pattern on a laboratory floor. The

system sparked the imagination of Leland M.

Hancock, traffic engineer in the Nebraska

Department of Roads, and of his director, L. N.

Ress, state engineer. The decision was made to

experiment with the system in actual highway

installations. In 1958, a full size system was successfully demonstrated by RCA Labs and the

State of Nebraska on a 400-foot strip of public highway just outside Lincoln, Neb.

1980s

In the 1980s, a vision-guided Mercedes-Benz robotic van, designed by Ernst Dickmanns and

his team at the Bundeswehr University Munich in Munich, Germany, achieved a speed of 39

miles per hour (63 km/h) on streets without traffic. Subsequently, EUREKA conducted the

€749 million Prometheus Project on autonomous vehicles from 1987 to 1995.

1990s

In 1991, the United States Congress passed the ISTEA Transportation Authorization bill,

which instructed USDOT to "demonstrate an automated vehicle and highway system by

1997." The Federal Highway Administration took on this task, first with a series of Precursor

Systems Analyses and then by establishing the National Automated Highway System

Consortium (NAHSC). This cost-shared project was led by FHWA and General Motors, with

Caltrans, Delco, Parsons Brinkerhoff, Bechtel, UC-Berkeley, Carnegie Mellon University,

and Lockheed Martin as additional partners. Extensive systems engineering work and

research culminated in Demo '97 on I-15 in San Diego, California, in which about 20

automated vehicles, including cars, buses, and trucks, were demonstrated to thousands of

onlookers, attracting extensive media coverage. The demonstrations involved close-headway

platooning intended to operate in segregated traffic, as well as "free agent" vehicles intended

to operate in mixed traffic.

3

2000s

The US Government funded three military efforts known as Demo I (US Army), Demo II

(DARPA), and Demo III (US Army). Demo III (2001) demonstrated the ability of unmanned

ground vehicles to navigate miles of difficult off-road terrain, avoiding obstacles such as

rocks and trees. James Albus at the National Institute for Standards and Technology provided

the Real-Time Control System which is a hierarchical control system. Not only were

individual vehicles controlled (e.g. Throttle, steering, and brake), but groups of vehicles

had their movements automatically coordinated in response to high level goals. The Park

Shuttle, a driverless public road transport system, became operational in the Netherlands in

the early 2000s.In January 2006, the United Kingdom's 'Foresight' think-tank revealed a

report which predicts RFID-tagged driverless cars on UK's roads by 2056 and the Royal

Academy of Engineering claimed that driverless trucks could be on Britain's motorways by

2019.

Autonomous vehicles have also been used in mining. Since December 2008, Rio Tinto Alcan

has been testing the Komatsu Autonomous Haulage System – the world's first commercial

autonomous mining haulage system – in the Pilbara iron ore mine in Western Australia. Rio

Tinto has reported benefits in health, safety, and productivity. In November 2011, Rio Tinto

signed a deal to greatly expand its fleet of driverless trucks. Other autonomous mining

systems include Sandvik Automine’s underground loaders and Caterpillar Inc.'s autonomous

hauling.

In 2013, on July 12, VisLab conducted another pioneering test of autonomous vehicles,

during which a robotic vehicle drove in downtown Parma with no human control,

successfully navigating roundabouts, traffic lights, pedestrian crossings and other common

hazards.

In 2011, the Freie Universität Berlin developed two autonomous cars to drive in the inner

city traffic of Berlin in Germany. Led by the AUTONOMOS group, the two vehicles Spirit

of Berlin and made in Germany handled intercity traffic, traffic lights and roundabouts

between International Congress Centrum and Brandenburg Gate. It was the first car licensed

for autonomous driving on the streets and highways in Germany and financed by the German

Federal Ministry of Education and Research.

The 2014 Mercedes S-Class has options for autonomous steering, lane keeping,

acceleration/braking, parking, accident avoidance, and driver fatigue detection, in both city

traffic and highway speeds of up to 124 miles (200 km) per hour.

Released in 2013, the 2014 Infiniti Q50 uses cameras, radar and other technology to deliver

various lane-keeping, collision avoidance and cruise control features. One reviewer

remarked, "With the Q50 managing its own speed and adjusting course, I could sit back and

simply watch, even on mildly curving highways, for three or more miles at a stretch adding

that he wasn't touching the steering wheel or pedals.

4

Although as of 2013, fully autonomous vehicles are not yet available to the public, many

contemporary car models have features offering limited autonomous functionality. These

include adaptive cruise control, a system that monitors distances to adjacent vehicles in the

same lane, adjusting the speed with the flow of traffic lane which monitors the vehicle's

position in the lane, and either warns the driver when the vehicle is leaving its lane, or, less

commonly, takes corrective actions, and parking assist, which assists the driver in the task of

parallel parking

1.2 Why autonomous car is important

1.2.1 Benefits of Self-Driving Cars

1. Fewer accidents

The leading cause of most automobile accidents today is driver error. Alcohol, drugs,

speeding, aggressive driving, over-compensation, inexperience, slow reaction time,

inattentiveness, and ignoring road conditions are all contributing factors. Given some

40 percent of accidents can be traced to the abuse of drugs and or alcohol, self-driving

cars would practically eliminate those accidents altogether.

2. Decreased (or Eliminated) Traffic Congestion

One of the leading causes of traffic jams is selfish behavior among drivers. It has been

shown when drivers space out and allow each other to move freely between lanes on

the highway, traffic continues to flow smoothly, regardless of the number of cars on

the road.

3. Increased Highway Capacity

There is another benefit to cars traveling down the highway and communicating with

one another at regularly spaced intervals. More cars could be on the highway

simultaneously because they would need to occupy less space on the highway

4. Enhanced Human Productivity

Currently, the time spent in our cars is largely given over to simply getting the car and

us from place to place. Interestingly though, even doing nothing at all would serve to

increase human productivity. Studies have shown taking short breaks increase overall

productivity.

You can also finish up a project, type a letter, monitor the progress of your kid’s

schoolwork, return phone calls, take phone calls safely, text until your heart’s content,

read a book, or simply relax and enjoy the ride .

5. Hunting For Parking Eliminated

Self-driving cars can be programmed to let you off at the front door of your

destination, park themselves, and come back to pick you up when you summon them.

You’re freed from the task of looking for a parking space, because the car can do it all

5

6. Improved Mobility For Children, The Elderly, And The Disabled

Programming the car to pick up people, drive them to their destination and Then Park

by themselves, will change the lives of the elderly and disabled by providing them

with critical mobility.

7. Elimination of Traffic Enforcement Personnel

If every car is “plugged” into the grid and driving itself, then speeding,—along with

stop sign and red light running will be eliminated. The cop on the side of the road

measuring the speed of traffic for enforcement purposes? Yeah, they’re gone. Cars

won’t speed anymore. So no need to Traffic Enforcement Personnel.

8. Higher Speed Limits

Since all cars are in communication with one another, and they’re all programmed to

maintain a specific interval between one another, and they all know when to expect

each other to stop and start, the need to accommodate human reflexes on the highway

will be eliminated. Thus, cars can maintain higher average speeds.

9. Lighter, More Versatile Cars

The vast majority of the weight in today’s cars is there because of the need to

incorporate safety equipment. Steel door beams, crumple zones and the need to build

cars from steel in general relate to preparedness for accidents. Self-driving cars will

crash less often, accidents will be all but eliminated, and so the need to build cars to

withstand horrific crashes will be reduced. This means cars can be lighter, which will

make them more fuel-efficient

1.3 What Are Autonomous and Automated Vehicles Technological advancements are creating a continuum between conventional, fully human-

driven vehicles and automated vehicles, which partially or fully drive themselves and which

may ultimately require no driver at all. Within this continuum are technologies that enable a

vehicle to assist and make decisions for a human driver. Such technologies include crash

warning systems, adaptive cruise control (ACC), lane keeping systems, and self-parking

technology.

• Level 0 (no automation):

The driver is in complete and sole control of the primary vehicle functions (brake, steering,

throttle, and motive power) at all times, and is solely responsible for monitoring the roadway

and for safe vehicle operation.

• Level 1 (function-specific automation):

Automation at this level involves one or more specific control functions; if multiple functions

are automated, they operate independently of each other. The driver has overall control, and

is solely responsible for safe operation, but can choose to cede limited authority over a

6

primary control (as in ACC); the vehicle can automatically assume limited authority over a

primary control (as in electronic stability control); or the automated system can provide

added control to aid the driver in certain normal driving or crash-imminent situations (e.g.,

dynamic brake support in emergencies).

• Level 2 (combined-function automation):

This level involves automation of at least two primary control functions designed to work in

unison to relieve the driver of controlling those functions. Vehicles at this level of

automation can utilize shared authority when the driver cedes active primary control in

certain limited driving situations. The driver is still responsible for monitoring the roadway

and safe operation, and is expected to be available for control at all times and on short notice.

The system can relinquish control with no advance warning and the driver must be ready to

control the vehicle safely.

• Level 3 (limited self-driving automation):

Vehicles at this level of automation enable the driver to cede full control of all safety-critical

functions under certain traffic or environmental conditions, and in those conditions to rely

heavily on the vehicle to monitor for changes in those conditions requiring transition back to

driver control. The driver is expected to be available for occasional control, but with

sufficiently comfortable transition time

• Level 4 (full self-driving automation):

The vehicle is designed to perform all safety-critical driving functions and monitor roadway

conditions for an entire trip. Such a design anticipates that the driver will provide destination

or navigation input, but is not expected to be available for control at any time during the trip.

This includes both occupied and unoccupied vehicles.

Our project can be considered as level 1 or level 2 type.

7

1.4 Advanced Driver Assistance System (ADAS)

A rapid growth has been seen worldwide in the development of Advanced Driver Assistance

Systems (ADAS) because of improvements in sensing, communicating and computing

technologies.

ADAS aim to support drivers by either providing warning to reduce risk exposure, or

automating some of the control tasks to relieve a driver from manual control of a vehicle.

From an operational point of view, such systems are a clear departure from a century of

automobile development where drivers have had control of all driving tasks at all times.

ADAS could replace some of the human driver decisions and actions with precise machine

tasks, making it possible to eliminate many of the driver errors which could lead to accidents,

and achieve more regulated and smooth vehicle control with increased capacity and

associated energy and environmental benefits.

Autonomous ADAS systems use on-board equipment, such as ranging sensors and

machine/computer vision, to detect surrounding environment.

The main advantages of such an approach are that the system operation does not rely on

other parties and that the system can be implemented on the current road infrastructure.

Now many systems have become available on the market including Adaptive Cruise Control

(ACC), Forward Collision Warning (FCW) and Lane Departure Warning systems, and many

more are under development. Currently, radar sensors are widely used in the ADAS

applications for obstacle detection. Compared with optical or infrared sensors, the main

advantage of radar sensors is that they perform equally well during day time and night time,

and in most weather conditions. Radar can be used for target identification by making use of

scattering signature information.

It is widely used in ADAS for supporting lateral control such as lane departure warning

systems and lane keeping systems.

Currently computer vision has not yet gained a large enough acceptance in automotive

applications. Applications of computer vision depend much on the capability of image

process and pattern recognition (e.g. artificial intelligence). The fact that computer vision is

based on a passive sensory principle creates detection difficulties in conditions with adverse

lighting or in bad weather situations.

1.5 Project description

1.5.1 Auto-parking

The aim of this function is to design and implement self-parking car system that moves a

car from a traffic lane into a parking spot through accurate and realistic steps which can be

applied on a real car.

1.5.2 Adaptive cruise control (ACC)

Also radar cruise control, or traffic-aware cruise control is an optional cruise control system

for road vehicles that automatically adjusts the vehicle speed to maintain a safe distance from

vehicles ahead. It makes no use of satellite or roadside infrastructures nor of any cooperative

8

support from other vehicles. Hence control is imposed based on sensor information from on-

board sensors only.

1.5.3 Lane Keeping Assist

It is a feature that in addition to Lane Departure Warning System automatically takes steps

to ensure the vehicle stays in its lane. Some vehicles combine adaptive cruise control with

lane keeping systems to provide additional safety. A lane keeping assist mechanism can

either reactively turn a vehicle back into the lane if it starts to leave or proactively keep the

vehicle in the center of the lane. Vehicle companies often use the term "Lane Keep(ing)

Assist" to refer to both reactive Lane Keep Assist (LKA) and proactive Lane Centering

Assist (LCA) but the terms are beginning to be differentiated

1.5.4 Lane departure

Our car moves using adaptive cruise control according to distance of front vehicle .If front

vehicle is very slow and will cause our car to slow down the car will start to check the lane

next to it and then depart to the next lane in order to speed up again.

1.5.5 Indoor Positioning system

An indoor positioning system (IPS) is a system to locate objects or people inside a building

using radio waves, magnetic fields, acoustic signals, or other sensory information collected

by mobile devices. There are several commercial systems on the market, but there is no

standard for an IPS system.

IPS systems use different technologies, including distance measurement to nearby anchor

nodes (nodes with known positions, e.g., Wi-Fi access points), magnetic positioning, dead

reckoning. They either actively locate mobile devices and tags or provide ambient location or

environmental context for devices to get sensed. The localized nature of an IPS has resulted

in design fragmentation, with systems making use of various optical, radio, or even acoustic

technologies.

1.5.6 Connected car

The aim of this function is:

Take a picture with a webcam plugged into the Arduino Yun

Upload the image to drop box using Python

9

1.6 Related work

The appearance of driverless and automated vehicle technologies offers enormous

opportunities to remove human error from driving. It will make driving easier, improve road

safety, and ease congestion. It will also enable drivers to choose to do other things than

driving during the journey.

It is the first driverless electric car prototype built by Google to test self-driving car project. It

looks like a Smart car, with two seats and room enough for a small amount of luggage

Figure 1-1 Google car

It operates in and around California, primarily around the Mountain View area where Google

has its headquarters.

It move two people from one place to another without any user interaction. The car is called

by a smartphone for pick up at the users location with the destination set. There is no steering

wheel or manual control, simply a start button and a big red emergency stop button. In front

of the passengers there is a small screen showing the weather and the current speed. Once

the journey is done, the small screen displays a message to remind you to take your personal

belongings. Seat belts are also provided in car to protect the passengers from the primary

systems fails; plus that emergency stop button that passengers can hit at any time.

Powered by an electric motor with around a 100 mile range, the car uses a combination of

sensors and software to locate itself in the real world combined with highly accurate digital

maps. A GPS is used, just like the satellite navigation systems in most cars, lasers and

cameras take over to monitor the world around the car, 360-degrees.

The software can recognize objects, people, cars, road marking, signs and traffic lights,

obeying the rules of the road. It can even detect road works and safely navigate around them

10

The new prototype has more sensors fitted to it that can see further (up to 600 feet in all

directions)

The simultaneous development of a combination of technologies has brought about this

opportunity. For example, some current production vehicles now feature adaptive cruise

control and lane keeping technologies which allow the automated control of acceleration,

braking and steering for periods of time on motorways, major A-roads and in congested

traffic. Advanced emergency braking

Systems automatically apply the brakes to help drivers avoid a collision. Self-parking

systems allow a vehicle to parallel or Reverse Park completely hands free. Developments in

vehicle automation technology in the short and medium term will move us closer to the

ultimate scenario of a vehicle which is completely “driverless”.

Figure 1-2 progression of automated vehicle technologies

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VOLVO autonomous CAR

semi-autonomous driving features:

sensors can detect lanes and a car in front of it.

Button in the steering wheel to let the system know I want it to use

Adaptive Cruise Control with Pilot Assist.

If the XC90 lost track of the lanes, it would ask the driver to handle steering duties with a

ping and a message in the dashboard. This is called the Human-machine interface

BMW autonomous CAR

A new i-Series car will include forms of automated driving and

digital connectivity most likely Wi-Fi, high-definition digital maps,

sensor technology, cloud technology and artificial intelligence.

Nissan autonomous CAR

Nissan vehicles in the form of Nissan’s Safety Shield-inspired

technologies. These technologies can monitor a nearly 360-degree view

around a vehicle for risks, offering warnings to the driver and taking

action to help avoid crashes if necessary.

12

Car Design

Chapter 2

13

2.1 First car design

In this section, we will talk about the trials that we passed by till reaching our last prototype

concerning the first car in our project. This car performs some functions of our project such

as the adaptive cruise control (ACC), lane keeping and lane departure.

2.1.1 Four WD Robot (Acrylic with 4 Motors and 4 Wheels)

The 4WD robot consists of four gear-motors with 65mm diameter wheels. The chassis plates

contain numerous cuts and holes for mounting sensors, microcontrollers and other hardware.

The space between the plates is ideal for batteries or more components.

2.1.1.1 Specifications

1) Motors Suggested Voltage:4.5V DC (work

well from 3-6V)

No load Speed:90±10rpm

No Load

Current:190mA(max.250mA)

Torque:800gf.cm (Minimum)

Stall current approximately 1A

2) Wheels 65mm diameter, 30mm width

Plastic rims with solid rubber tires

3) Chassis Laser cut acrylic

Metal standoffs

Top and bottom plates are both

110mm long x 174mm wide Figure 2-1 four WD robot

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2.1.1.2 Real Shots

Figure 2-2 four WD real shot

2.1.1.3 Drawbacks

1) The 4WD robot uses four wheels with four Dc motors which is not similar to a real car.

2) The steering mechanism is not accurate, which causes many problems while working.

3) The robot size is very small which is not stable and can be easily broken.

2.1.1.4 Overcoming the drawbacks

1) Implementing a mechanical design to serve our needs

2) Using servo motor with steering mechanism to give stable steering while turning left or

right.

2.1.2 Second Trial

The mechanical design is considered as one of the most important parts in our project

because:

1- Without a good design the car may fail in accomplishing its tasks.

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2- The body carries all the electrical components, if the body might fail to withstand the

stresses.

2.1.2.1 Specifications

1) Main body: length= 360 mm, width=140 mm, aspect ratio between length and width is

similar to Honda Jazz – 2011.

Figure 2-3 second design trial AutoCad design

2) Motors

*Servo motor.

*DC motor with dual H-bridge.

3) Wheels

* 4 rubber wheels.

* Plastic rims with solid rubber tires.

Front wheel place

Front fixing slots

Servo position

Back fixing slots

Driving motor

position

Side fixing slots

16

4) Chassis * Laser cut acrylic.

* 3D holders for the IR sensor.

2.1.2.2 Real Shots

First shot shows the servomotor with the front wheel, Arduino card, and back wheel with the

driving DC motor.

Figure 2-4 second car trial real shots

It’s obvious that the length to width ratio could cause problem during the turns.

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The car with all components on and connected.

Figure 2-5 second car trial connection

2.1.2.3 Drawbacks

1- The car bulk loaded with all other parts makes it hard to take turn.

2- The sharp edge in the front wheel position, causes friction with the wheels and, makes

it hard to take turns.

3- The position of servomotor and the main front wheel link connection was weak.

2.1.2.4 Overcoming these problems

Modifying the original design by changing the length to 260 mm and keep the width 140

mm, that led to better performance in taking turns

2.1.3 Final Design

2.1.3.1 Body

Figure 2-6 final car design

Fillet to overcome

wheel friction with the

body

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2.1.3.2 Side Covers

The height of the side cover is 114 mm and length was 360 mm then reduced to 260 mm to

fit the modified, as following there are two figures, on the right hand side is the right cover,

on the left hand side is the left cover.

Figure 2-7 final car side covers

The side covers are used to overcome the modification draw back, the

sides provide the area required to fix the rest of components without any problems.

Ultrasound

sensors slots

The cover

fixing slots

Front wheels

slots

Arduino cable slot

The Base

fixing slots

Back wheels

slots

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2.1.3.3 The Front and Back Covers

The dimensions are 140 mm width and 114 mm height, provided with slots to fix the

ultrasound sensors.

Figure 2-8 final car front and back covers

The ultrasound sensor

slots

Side slots

Cover slots

Base slots

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2.1.3.4 Cover

The cover

dimensions

are 260 mm

length and

140 mm

width. The

cover

function is to

keep all the

small and

other

components

inside.

We notice that the design is not aerodynamically considered, that’s because the project target

is the control and apply the function, not to study the aerodynamic effects of cars.

2.1.3.5 Real shots

Figure 2-10 final car real shots

2.1.3.6 Drawback

The compact size causes another expected problem, there are many components require more

space, and the total area is reduced.

Figure 2-9 final car cover

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2.2 Car Wheel Drive

2.2.1 Types of wheel drive

Figure 2-11 types of wheel drive

Our car is using rear-wheel-drive.

2.2.2 Advantages of this configuration:

1- Even weight distribution.

2- Better control at the turns.

3- Better steering radius.

Wheel drive

two-wheel drive

front drive

rear drivefour-wheel

drive

Figure 2-12 final car inside view

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2.3 Car Steering

2.3.1 Basic geometry

The basic aim of steering is to ensure that the wheels are pointing in the desired directions.

This is typically achieved by a series of linkages, rods, pivots and gears. One of the

fundamental concepts is that of caster angle – each wheel is steered with a pivot point ahead

of the wheel; this makes the steering tend to be self-centering towards the direction of travel.

Figure 2-13 final car center of turning circle

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2.3.2 Our mechanism

It is controlled automatically by a servomotor

connected directly to the main link.

Shown in the picture the old mechanism, it’s

obvious that the orientation of the

servomotor is not correct.

The servomotor at this position doesn’t give

all the desired angles.

So modifications are made.

2.3.3 Servomotor

modifications

Modifications are made to ensure

better performance in turns, the new

mechanism is less heavy, more

flexible with turns. As shown in

following image.

The servomotor in the new position

gives all the angles and transfer more

torque.

Servomotor in the

new position

Servomotor in the

old position Figure 2-14 old car design servomotor mechanism

Figure 2-15 last car design servomotor

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2.4 Second car design

We are going to talk about the design of the second car used in our project and the trials till

reaching the last prototype. The second car performs the auto-parking function in our project.

It passed by nearly the same trials as the first car but concerning the final design, it was not

the same. So we will discuss the final design in the following lines.

2.4.1 Final design

The final design of the second car is an RC car. The RC car is the best choice for

implementing the auto-parking function because all the above designs did not help in

fulfilling the function correctly.

2.4.2 Real shots

Figure 2-16 second car real shots

2.5 Motors

The motor is the primary tool for creating motion. At its simplest use, you can use it to make

something spin. With a little more mechanical work, using gears and other mechanical

devices, you can use a motor to make something move, vibrate, rise, fall, roll, creep, or

perform almost any other type of motion that does not require precise positioning. There are

several different kinds of motors: servos, stepper motors, or unidirectional DC motors. In this

section, we will talk about DC and servo motors and how they can be. Simple motors are

good for designs that need motion forward or backward, like a remote control car or a fan,

but not for things that need to move to a precise position, like a robotic arm or anything that

points or moves something to a controlled position.

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2.5.1 DC Motor

D. C motors are seldom used in ordinary applications because all electric supply companies

furnish alternating current. However, for special applications such as in steel mills, mines

and electric trains, it is advantageous to convert alternating current into direct current in order

to use DC motors. The reason is that speed/torque characteristics of DC motors are much

more superior to that of AC motors. Therefore, it is not surprising to note that for industrial

drives, DC motors are as popular as 3-phase induction motors. Like DC generators, dc

motors are also of three types. (series-wound , shunt-wound and compound wound). The use

of a particular motor depends upon the mechanical load it has to drive. Motors are all around

us; just look inside moving toys, and you’ll find a number of excellent motors and gears. Any

electronics supplier will have a wide range of motors that will suit many purposes from

spinning small objects to driving large loads

2.5.1.1 D.C Motor Fundamentals

DC motors consist of rotor (or armature), commentator, brushes, rotating shaft and bearings,

stator with permanent magnet. The principle of operation with a simple two-pole dc motor:

The torque is produced by the fact that like field poles attracts and unlike poles repel.

2.5.1.2 DC Motor Principle

It is a machine that converts DC power into mechanical power. Its operation is based on the

principle that when a current carrying conductor is placed in a magnetic field, the conductor

experiences a mechanical force. Basically, there is no constructional difference between a

DC motor and a DC generator. The same DC machine can be run as generator motor.

Figure 2-17 DC motor theory

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2.5.1.3 Controlling DC Motor

First we have to mention that the Dc motor used in our project was taken from RC toy car

and we repurpose it to match our needs and design for both cars. Here we will talk briefly

about controlling the motor and its connection with the Arduino.

1) H-Bridge

An H bridge is an electronic circuit that enables voltage to be supplied to the DC motor and

control its direction. This circuit is often used in robotics and other applications.

1.1 L298 Dual Motor Driver Module This driver module is based on L298N H-bridge, a

high current, high voltage dual full bridge driver manufactured by ST Company. It

can drive up to 2 DC motors 2A each. The driver can control both motor RPM and

direction of rotation.

The RPM is controlled using PWM input to

ENA or ENB pins, while rotation direction

is controlled by supplying high and low

signal to EN1-EN2 for the first motor or

EN3-EN4 for second motor. This Dual H-

Bridge driver is capable of driving voltages

up to 46V.

1.2 Features

Dual H bridge drive (can drive 2 DC

motors).

Chip L298N.

Logical voltage 5V.

Drive voltage 5V-35V.

Logic current 0mA-36mA.

Drive current 2A (For each DC motor)).

Weight 30gm.

Size: 43*43*27mm.

Figure 2-18 Dual H-bridge

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1.3 Driver connection with Arduino

It has 8 pins:

1- GND.

2- + 5 V (power for driver (not motor)).

3- ENA: Motor enable for Motor A (high/low).

4, 5- IN1, IN2: pins control Motor A direction of rotation (one is high and the other is low).

6-ENB: Motor enable for Motor B (high/low).

7, 8- IN3, IN4: These pins define Motor B direction of rotation (one is high and the other is

low).

The following figures show the exact connection with Arduino.

It is obvious that the first connection is when connecting two motors with the driver and this

happened in the second car (RC car).

Figure 2-19 second car motors connection

While the first car connection is using only one motor and connecting it with the driver as

shown in the following connection.

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Figure 2-20 first car motor connection

2.5.2 Servo motor

Unlike dc motors, with servo motors you can position the motor shaft at a specific position

(angle) using control signal. The motor shaft will hold at this position as long as the control

signal not changed. This is very useful for controlling robot arms, unmanned airplanes

control surface or any object that you want it to move at certain angle and stay at its new

position. Servo motors may be classified according to size or torque that it can withstand into

mini, standard and giant servos. Usually mini and standard size servo motors can be powered

by Arduino directly with no need to external power supply or driver. Usually servo motors

come with arms (metals or plastic) that are connected to the object required to move. In our

project we used a TowerPro MG995 servo motor and in the following lines we will show its

features in brief.

2.5.2.1 Servo motor features

Model: TowerPro MG995 Metal Servo.

Dimensions: :4.07*1.97*4.29cm.

Speed: 0.2sec/60° (4.8V).

Torque: 10kg-cm.

Rated Voltage: 4.8-7.2V.

29

2.5.2.2 Servo Motor mechanism

Servo motor has 3 wires:

Black wire: GND (ground).

RED wire: +5v.

Colored wire: control signal.

The third pin (colored wire) accepts the control signal which is a pulse-width modulation

(PWM) signal which can be easily produced by all micro- controllers and Arduino board. It

accepts the signal from your controller that tells it the turn angle. The control signal is fairly

simple compared to that of a stepper motor. It is just a pulse of varying lengths. The length of

the pulse corresponds to the angle by which the motor turns to.

Figure 2-21 servo motor mechanism

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2.5.2.3 Inside Servo Motor

Did you ever wonder how the servo motors looks from inside? Have a look at figure 2-22

and figure 2-23. A servo motor was taken apart to show the internal parts. You can see a

regular dc motor connected to a gear box and a potentiometer that gives the feedback for

angle position. This is represented by the diagram below.

Figure 2-22 inside servo motor diagram

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Figure 2-23 inside servo motor real shot

2.5.2.4 Servo Motor with Arduino

Standard servo motor control using Arduino is extremely easy. This is because the Arduino

software comes with a sample servo sketch and servo Library that will get you up and

running quickly.

Connect the black wire from the servo to the GND pin on Arduino.

Connect the red wire from servo to the +5V pin on Arduino.

Connect the third wire (usually orange or yellow) from the servo to a digital

pin on Arduino.

32

Figure 2-24 Connection of servo motor with Arduino

33

Microcontroller and Hardware

Chapter 3

34

3.1 Micro Controllers A Microcontroller (sometimes abbreviated µC, or MCU) is a small computer on a single

integrated circuit containing a processor core, memory, and programmable input/output

peripherals. Program memory in the form of NOR flash or OTP ROM is also often included

on chip, as well as a typically small amount of RAM. Microcontrollers are designed for

embedded applications, in contrast to the microprocessors used in personal computers or

other general purpose applications.

Microcontrollers are used in automatically controlled products and devices, such as

automobile engine control systems, implantable medical devices, remote controls, office

machines, appliances, power tools, toys and other embedded systems. By reducing the size

and cost compared to a design that uses a separate microprocessor, memory, and input/output

devices, microcontrollers make it economical to digitally control even more devices and

processes. Mixed signal microcontrollers are common, integrating analog components

needed to control non-digital electronic systems.

Some microcontrollers may use four-bit words and operate at clock rate frequencies as low as

4 kHz, for low power consumption (single-digit mille watts or microwatts). They will

generally have the ability to retain functionality while waiting for an event such as a button

press or other interrupt, power consumption while sleeping (CPU clock and most peripherals

off) may be just Nano watts, making many of them well suited for long lasting battery

applications. Other microcontrollers may serve performance-critical roles, where they may

need to act more like a digital signal processor (DSP), with higher clock speeds and power

consumption.

3.1.1 Arduino 3.1.1.1 Introduction

1. What is Arduino?

Arduino is a tool for making computers that can sense and control more of the

physical world than your desktop computer. It’s an open-source physical computing

platform based on a simple microcontroller board, and a development environment

for writing software for the board. Arduino can be used to develop interactive objects,

taking inputs from a variety of switches or sensors, and controlling a variety of lights,

motors, and other physical outputs. Arduino projects can be stand-alone, or they can

be communicate with software running on your computer (e.g. Flash, Processing,

MaxMSP.) The Arduino programming language is an implementation of Wiring, a

similar physical computing platform, which is based on the Processing multimedia

programming environment.

2. Why Arduino?

There are many other microcontrollers and microcontroller platforms available for

physical computing. Parallax Basic Stamp, Netmedia's BX-24, Phidgets, MIT's

35

Handy board, and many others offer similar functionality. All of these tools take the

messy details of microcontroller programming and +wrap it up in an easy-to-use

package.

Arduino also simplifies the process of working with microcontrollers, but it offers

some advantage for teachers, students, and interested amateurs over other systems:

• Inexpensive

Arduino boards are relatively inexpensive compared to other microcontroller

platforms. The least expensive version of the Arduino module can be

assembled by hand, and even the pre-assembled Arduino modules cost less

than $50

• Cross-platform

The Arduino software runs on Windows, Macintosh OSX, and Linux operating

systems. Most microcontroller systems are limited to Windows.

• Simple, clear programming environment

The Arduino programming environment is easy-to-use for beginners, yet

flexible enough for advanced users to take advantage of as well. For teachers,

it's conveniently based on the Processing programming environment, so

students learning to program in that environment will be familiar with the look

and feel of Arduino

• Open source and extensible software

The Arduino software is published as open source tools, available for

extension by experienced programmers. The language can be expanded

through C++ libraries, and people wanting to understand the technical details

can make the leap from Arduino to the AVR C programming language on

which it's based. Similarly, you can add AVR-C code directly into your

Arduino programs if you want to.

• Open source and extensible hardware

The Arduino is based on Atmel's ATMEGA8 and ATMEGA168

microcontrollers. The plans for the modules are published under a Creative

Commons license, so experienced circuit designers can make their own

version of the module, extending it and improving it. Even relatively

inexperienced users can build the breadboard version of the module in order to

understand how it works and save money.

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3. Types of Arduino

There are different types of Arduino to choose from.

3.1.1.2 Arduino mega

Figure 3-2 Arduino mega

1. Overview

The Arduino Mega 2560 is a microcontroller board based on the ATmega2560 It has

54 digital input/output pins (of which 15 can be used as PWM outputs), 16 analog

inputs, 4 UARTs (hardware serial ports), a 16 MHz crystal oscillator, a USB

connection, a power jack, an ICSP header, and a reset button. It contains everything

needed to support the microcontroller; simply connect it to a computer with a USB

cable or power it with a AC-to-DC adapter or battery to get started. The Mega is

compatible with most shields designed for the Arduino Duemilanove or Diecimila.

2. Power

The Arduino Mega can be powered via the USB connection or with an external power

supply. The power source is selected automatically. External (non-USB) power can

come either from an AC-to-DC adapter (wallwart) or battery. The adapter can be

connected by plugging a 2.1mm centerpositive plug into the board’s power jack.

Leads from a battery can be inserted in the Gnd and Vin pin headers of the POWER

connector. The board can operate on an external supply of 6 to 20 volts. If supplied

Figure 3-1 Arduino types

37

with less than 7V, however, the 5V pin may supply less than five volts and the board

may be unstable. If using more than 12V, the voltage regulator may overheat and

damage the board. The recommended range is 7 to 12 volts. The power pins are as

follows:

1. VIN.

The input voltage to the Arduino board when it’s using an external power source

(as opposed to 5 volts from the USB connection or other regulated power source).

You can supply voltage through this pin, or, if supplying voltage via the power

jack, access it through this pin.

2. 5V.

This pin outputs a regulated 5V from the regulator on the board. The board can

be supplied with power either from the DC power jack (7 - 12V), the USB

connector (5V), or the VIN pin of the board (7-12V). Supplying voltage via the

5V or 3.3V pins bypasses the regulator, and can damage your board. We don’t

advise it.

3. 3V3.

A 3.3 volt supply generated by the on-board regulator. Maximum current draw is

50 mA.

4. GND. Ground pins.

3. Memory

The ATmega2560 has 256 KB of flash memory for storing code (of which 8 KB is

used for the bootloader), 8 KB of SRAM and 4 KB of EEPROM (which can be read

and written with the EEPROM library).

4. Input and Output

Each of the 54 digital pins on the Mega can be used as an input or output, using

pinMode(), digitalWrite(), and digitalRead() functions. They operate at 5 volts. Each

pin can provide or receive a maximum of 40 mA and has an internal pull-up resistor

(disconnected by default) of 20-50 kOhms. In addition, some pins have specialized

functions:

1. Serial: 0 (RX) and 1 (TX); Serial 1: 19 (RX) and 18 (TX); Serial 2: 17 (RX)

and 16 (TX); Serial 3: 15 (RX) and 14 (TX). Used to receive (RX) and transmit (TX)

TTL serial data. Pins 0 and 1 are also connected to the corresponding pins of the

ATmega16U2 USB-to-TTL Serial chip.

2. External Interrupts: 2 (interrupt 0), 3 (interrupt 1), 18 (interrupt 5), 19

(interrupt 4), 20 (interrupt 3), and 21 (interrupt 2). These pins can be configured to

trigger an interrupt on a low value, a rising or falling edge, or a change in value. See

the attachInterrupt() function for details.

38

3. PWM: 2 to 13 and 44 to 46. Provide 8-bit PWM output with the analog-

Write() function.

4. SPI: 50 (MISO), 51 (MOSI), 52 (SCK), 53 (SS). These pins support SPI

communication using the SPI library. The SPI pins are also broken out on the ICSP

header, which is physically compatible with the Uno, Duemilanove and Diecimila.

5. LED: 13. There is a built-in LED connected to digital pin 13. When the pin is

HIGH value, the LED is on, when the pin is LOW, it’s off.

6. TWI: 20 (SDA) and 21 (SCL). Support TWI communication using theWire

library. Note that these pins are not in the same location as the TWI pins on the

Duemilanove or Diecimila. The Mega2560 has 16 analog inputs, each of which

provide 10 bits of resolution (i.e. 1024 different values). By default they measure

from ground to 5 volts, though is it possible to change the upper end of their range

using the AREF pin and analogReference() function. There are a couple of other pins

on the board:

1. AREF. Reference voltage for the analog inputs. Used with analogReference().

2. Reset. Bring this line LOW to reset the microcontroller. Typically used to add

a reset button to shields which block the one on the board.

5. Communication

The Arduino Mega2560 has a number of facilities for communicating with a

computer, another Arduino, or other microcontrollers. The AT mega 2560 provides

four hardware UARTs for TTL (5V) serial communication. An ATmega16U2 (AT

mega 8U2 on the revision 1 and revision 2 boards) on the board channels one of these

over USB and provides a virtual com port to software on the computer (Windows

machines will need a .inf file, but OSX and Linux machines will recognize the board

as a COM port automatically. The Arduino software includes a serial monitor which

allows simple textual data to be sent to and from the board. The RX and TX LEDs on

the board will flash when data is being transmitted via the

ATmega8U2/ATmega16U2 chip and USB connection to the computer (but not for

serial communication on pins 0 and 1). A Software Serial library allows for serial

communication on any of the Mega2560’s digital pins. The ATmega2560 also

supports TWI and SPI communication. The Arduino software includes a Wire library

to simplify use of the TWI bus; see the documentation for details. For SPI

communication, use the SPI library

6. Programming

The Arduino Mega can be programmed with the Arduino software (download). The

ATmega2560 on the Arduino Mega comes preburned with a bootloader that allows

39

you to upload new code to it without the use of an external hardware programmer. It

communicates using the original STK500 protocol (reference, C header files).

7. Automatic (Software) Reset

Rather then requiring a physical press of the reset button before an upload, the

Arduino Mega2560 is designed in a way that allows it to be reset by software running

on a connected computer. One of the hardware flow control lines (DTR) of the

ATmega8U2 is connected to the reset line of the ATmega2560 via a 100 nano-farad

capacitor. When this line is asserted (taken low), the reset line drops long enough to

reset the chip. The Arduino software uses this capability to allow you to upload code

by simply pressing the upload button in the Arduino environment. This means that the

bootloader can have a shorter timeout, as the lowering of DTR can be well-

coordinated with the start of the upload.

3.1.2 Raspberry Pi

3.1.2.1 Design

The Raspberry Pi is a single-board computer developed in the UK by the Raspberry

Pi. The Raspberry Pi is a credit-card sized computer that plugs into your TV and a

keyboard. It’s a capable little PC which can be used for many of the things that your

desktop PC does, like spreadsheets, word-processing and games.

The design is based around a Broadcom BCM2835 SoC, which includes an

ARM1176JZF-S 700 MHz processor and 512 Megabytes of RAM.

The design does not include a built-in hard disk or solid-state drive, instead relying on

an SD card for booting and long-term storage. This board is intended to run Linux

kernel based operating systems.

3.1.2.2 ARM1176 PROSESSOR

The ARM1176™ applications processors deployed broadly in

devices ranging

from smart phones to digital TV's delivering media and

browser performance, a secure computing environment, and

performance up to 1GHz in low cost designs.

The ARM1176 is still actively being licensed for application

processor and baseband processor designs due to its maturity,

low level of implementation risk, and low implementation cost

Figure 3-3 Rpi memory management

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3.1.2.3 Features

• Low risk and fast time to market

• High performance in low-cost designs

• Physically addressed caches for multi-tasking performance

• Broad OS support, multiple Linux distributions, amazing ARM ecosystem

• Full Internet experience

• Low Power Leadership

• 93% of flops are clock gated

3.1.2.4 Performance

The ARM1176 processor performance reaches up to 1GHz and beyond in 40G, and

can reach 1GHz in 65nm with overdrive voltages.

3.1.2.5 Functions supported in hardware

• Multiplication, addition, and multiply-accumulate ( various variants)

• Division and square root operation (multi-cycle, not pipelined)

• Comparisons and format conversions

• Operations can be performed on short vectors (From assembler only)

• Separate pipelines allow load/store and MAC operations to occur

simultaneously with divide/square root unit operation

• Clock gated and/or power completely removed

41

3.1.2.6 Comparison

42

3.2 Hardware

3.2.1 Ultrasonic sensor

3.2.1.1 Introduction Distance measurement sensor is a low cost full functionality solution for distance

measurement applications. The module is based on the measurement of time flight of

ultrasonic pulse, which is reflected by an object. The distance to be measured mainly

depends on the speed of ultrasonic waves in space or air –which is a constant and the

flight time of the pulse. The module performs these calculations and outputs a pulse

width depends on the measured distance, this pulse is easily interfaced to any

microcontroller.

Figure 3-4 ultrasonic sensor

3.2.1.2 Features

• Supply voltage +5Vdc

• Supply Current 10mA

• Measurement distance Range from 2cm to 400cm.

• Input trigger pulse is 5V TTL compatible (5 μs minimum). Output echo

pulse is 5V TTL compatible. Size 44.5mm W x 20mm H x 15mm D.

• Interface connector 4-pin header SIP, 0.1” spacing.

• Operating temperature range 0° - 70° C.

43

3.2.1.3 Connection with Arduino

Figure 3-5 ultrasonic sensor connection with Arduino

3.2.1.4 Connection with Raspberry pi

Figure 3-6 ultrasonic sensor connection with Rpi

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3.2.2 Infrared line tracking sensor

3.2.2.1 Introduction

Line tracking is the most basic function of smart mobile robot. This new generation of

line tracking sensors is developed to be the robot's powerful copilot all the way. It

will guide it robot by telling white from black quickly and accurately, via TTL signal.

With a drawn path and good programming can ensure results that are far more

consistent than if the robot was simply told where to go without any reference.

Figure 3-7 infrared sensor

3.2.2.2 Theory of operation It consists two parts

1) IR emitting LED

2) IR sensitive phototransistor.

The IR Reflectance sensors work best when they are close to the surface of the

ground. It should be about 1/8" above the ground. This is an optimal distance for the

IR transmitter to illuminate the surface below and measure the reflected light.

It works by transmitting a beam of IR light downward toward the surface. If the

detector is over a white surface, the reflected light is received by the detector and

outputs a HIGH signal. When the sensor is over a black surface where the light is

absorbed or not reflected, the IR detector outputs a LOW signal. The IR Sensor

module provides a value inversely dependent to the amount of reflected IR light.

So it can output digital signal to a microcontroller so the robot can reliably follow a

black line on a white background, or vice versa

45

3.2.2.3 Features and Pins

Small size.

5V DC power supply.

Indicator LED.

Digital output.

Distance: up to 3 cm

Size: 3.5 x 1cm

Applicable to a variety of platforms including Arduino / AVR / ARM /PIC

** Pin Definition

GND: Ground

OUT: Output (HIGH when line is black and LOW when line is white)

VCC: 3.3-5 VDC

Figure 3-8 infrared sensor pins

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3.2.2.4 Arduino connection

Figure 3-9 1R sensor connection with Arduino

3.2.3 Optical encoder

Figure 3-10 optical encoder

3.2.3.1 Introduction

The encoder kit consists of two 8-pole magnets with rubber hubs and two hall-

effect sensors terminated with 150mm (6 inch) cable and 3 pin female servo

headers.

The magnets have 4 north poles and 4 south poles and are strong enough that the

sensor can detect the poles from more than 3mm (1/8inch). This means that the

sensors do not need to be precisely mounted to detect the poles. The rubber hubs will

press fit over most small motor and drive shafts used in low power gearboxes. A

small screw may also be used to attach the magnets in some cases.

47

The hall-effect sensors will work on voltages from 3V to 24V and include reverse polarity

protection. It can work with any Arduino compatible controller and use the processors internal

pullup resistors eliminating the need for any external components or wiring

3.2.3.2 Specifications

• Supply voltage: 3V-24V

• Supply current: 4mA per sensor

• Output voltage: 26V maximum

• Output current: 25mA continuous

• Output type: Open drain

48

Adaptive Cruise Control

Chapter 4

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4.1 Definitions

Adaptive Cruise Control (ACC): An enhancement to a conventional cruise control system

which allows the ACC vehicle to follow a forward vehicle at an appropriate distance.

4.2 Introduction

A big issue on busy roads is traffic jams. There are campaigns that ask people not to travel

during peak hours and to travel together in one car, but they don’t have the desired effect.

This raises the demand for a technical solution. If the amount of cars can’t be reduced, the

vehicle throughput of the road must be increased to solve the traffic jams. This can be done

by reducing the inter-vehicle distance, but this will immediately lead to unsafe situations

caused by the slow response time of human beings. To overcome this slow response time, a

technical solution can be introduced, which controls the throttle and brake of the car. An

implementation of such a system which is used more and more these days, is ACC.

The goal of such a system is to keep a constant distance to its predecessor, or to keep a

constant speed if the constant distance could only be achieved if the maximum speed must be

exceeded. Using this technique, it is possible to create a vehicle string with all cars driving

safely in the platoon while being comfortable for the passenger in the car

An ‘Adaptive Cruise Control’ (ACC) system developed as the next generation assisted the

driver to keep a safe distance from the vehicle in front. This system is now available only in

some luxury cars like Mercedes S-class, Jaguar and Volvo trucks the U.S. Department of

transportation and Japan’s ACAHSR have started developing ‘Intelligent Vehicles’ that can

communicate with each other with the help of a system called ‘Cooperative Adaptive Cruise

Control’

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4.3 Application

4.3.1 Theory of operation

Adaptive Cruise Control (ACC) is an advancement of cruise control system. It’s an

automotive feature allows the vehicle to adopt set vehicle's speed to the traffic environment.

A sensor system is attached to the front of the vehicle which is used to detect former slow

moving vehicles are in the ACC vehicle's path. If a foregoing slow moving vehicle detected

by the sensor system in the ACC vehicle’s path, then the ACC system will automatically

slows its speed to maintain a safe distance.

If the system detects that the ahead vehicle gets higher speed than ACC vehicle cruise speed

or no longer in the ACC vehicle's path, then automatically ACC system will stimulate back

the vehicle speed to its pre-set cruise control speed.

This action of control system allows the ACC vehicle to self-governing slow down and

speeds up with traffic without arbitration from the driver

Figure 4-1 Adaptive cruise control in real cars

4.3.2 Sensor options

currently four means of object detection are technically feasible and applicable in a vehicle

environment .They are

1. RADAR

2. LIDAR

3. Vision sensor

4. ULTRASONIC SENSOR

The first ACC system used LIDAR sensor.

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4.3.2.1 LIDAR (Light Detection and Ranging)

The first ACC system introduced by Toyota used this method. By measuring the beat

frequency difference between a Frequency Modulated Continuous light Wave (FMCW) and

its reflection

Figure 4-2 operation of LIDAR

Most of the current ACC systems are based on 77GHz RADAR sensors.

The RADAR systems have the great advantage that the relative velocity can be measured

directly, and the performance is not affected by heavy rain and fog. LIDAR system is of low

cost and provides good angular resolution although these weather conditions restrict its use

within a 30 to 40 meters range.

4.3.2.2 RADAR (Radio Detection and Ranging)

RADAR is an electromagnetic system for the detection and location of reflecting objects like

air crafts, ships, space crafts or vehicles. It is operated by radiating energy into space and

detecting the echo signal reflected from an object (target) the reflected energy is not only

indicative of the presence but on comparison with the transmitted signal, other information of

the target can be obtained. The currently used ‘Pulse Doppler RADAR’ uses the principle of

‘Doppler effect’ in determining the velocity of the target

4.3.2.3 Fusion sensor

The new sensor system introduced by Fujitsu Ten Ltd. and Honda through their PATH

program includes millimeter wave radar linked to a 640x480 pixel stereo camera with a 40

degree viewing angle. These two parts work together to track the car from the non-moving

objects. While RADAR target is the car’s rear bumper, the stereo camera is constantly

52

captures all objects in its field of view

Figure 4-3 operation of fusion sensor

The image processor measures the distances to the objects through triangulation method.

This method includes an algorithm based on the detection of the vertical edges and distance.

Incorporating both the 16-degree field of view of radar and 40-degree field of view of camera

enhances the performance in tight curves

4.3.2.4 Ultrasonic sensor

Depend on measuring distance between our car and front car by sending and receiving

ultrasonic waves and measure time taken by wave between sending and receiving

according to distance car takes action to speed up or slow down

Figure 4-4 operation of ultrasonic sensor

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Ultrasonic sensors are capable of detecting most objects — metal or nonmetal, clear or

opaque, liquid, solid, or granular — that have sufficient acoustic reflectivity. Another

advantage of ultrasonic sensors is that they are less affected by condensing moisture than

photoelectric sensors.

A downside to ultrasonic sensors is that sound absorbing materials, such as cloth, soft rubber,

flour and foam, make poor target objects.

In our project we used Ultrasonic sensor because it is the most suitable sensor according to

size, cost and availability

4.4 Algorithm

First a safe distance is set this is the distance our car wants to keep with the front vehicles

(the reference).

Second cruise speed is set this is the speed the car will maintain at free driving.

Figure 4-5 feedback control system

Figure 4-6 PID theory

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Controller here is Arduino microcontroller its function is to take values of distance read by

sensor and control speed and distance according to values set

When the car start and the function is activated it starts to take readings from ultrasonic

sensor and act according to these readings

we here have two cases:

1) If distance is bigger than safe distance

This means that the car can move freely with the cruise speed as there is no car in its way

speed is controlled by speed PID in order to make car maintain this speed while cruising

2) If distance is less than safe distance

This mean that the car will slow down to maintain safe distance

how much the car will slow down is controlled by PID

3) If distance is less than critical distance then the car will stop

4.5 Implementation and testing

4.5.1 Steps

The hardware used to implement this function is the DC motor and ultrasonic sensor

the steps we made was:

1) Testing accuracy of the sensor we have and writing code to calculate the distance that it

sees

2) Connecting DC motor , we started first with a simple code to move car and test different

speeds to see how the car responds

3) Then we made our code using fuzzy control so that for every range of distance the car will

move with a certain speed

4) After that we started adding a controller to our system as shown in flow chart

5) we started reading about different controllers then decided to use PID for our system

6) Then we started tuning parameters to reach the ones suitable for us after that testing its

response

7) Since our system has no model and considered a black box tuning need to be done

experimentally

8) There is different tuning methods in our system we used two methods

Distance speed PID we used trial and error method

Speed PID we used Ziegler-Nicholas

9) Then testing response

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4.6 Designing a control system

4.6.1 Design in real systems

To design any control system:

1) Choose design specification needed

2) Set a mathematical model for system

3) Then tune parameter and check performance of system, if the system specification reached

then the parameters are the right ones but if specifications not reached then retune parameters

4.6.2 Design in our system

1) We didn’t set any specification because what was important for us is speed of response

2) Our system doesn’t have a model so we consider it a black box

3) We will tune parameters by method that doesn’t need transfer function

4) Before tuning we need to choose the type of controller that we will use

4.6.3 Controllers

Before introducing various controllers, it is very important to know why we use controllers

and why they are important.

1. Controllers improve steady state accuracy by decreasing the steady state errors.

2. As the steady state accuracy improves, the stability also improves.

3. Help in reducing the offsets produced in the system.

4. Maximum overshoot of the system can be controlled using these controllers.

5. They also help in reducing the noise signals produced in the system.

6. Slow response of the over damped system can be made faster with the help of these

controllers.

4.6.3.1 P Controller

P controller is mostly used in first order processes with single energy storage to stabilize the

unstable process. The main usage of the P controller is to decrease the steady state error of

the system. As the proportional gain factor K increases, the steady state error of the system

decreases. However, despite the reduction, P control can never manage to eliminate the

steady state error of the system. As we increase the proportional gain, it provides smaller

amplitude and phase margin and larger sensitivity to the noise. We can use this controller

only when our system is tolerable to a constant steady state error. In addition, it can be easily

concluded that applying P controller decreases the rise time and after a certain value of

reduction on the steady state error, increasing K only leads to overshoot of the system

response.

Mathematical equation:

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Advantages

1. Proportional controller helps in reducing the steady state error, thus makes the system

more stable.

2. Slow response of the over damped system can be made faster with the help of these

controllers.

Disadvantages

1. Due to presence of these controllers there are some offsets in the system.

2. Proportional controllers also increase the maximum overshoot of the system.

4.6.3.2 PI controller

P-I controller is mainly used to eliminate the steady state error resulting from P controller.

However, in terms of the speed of the response and overall stability of the system, it has a

negative impact. This controller is mostly used in areas where speed of the system is not an

issue.

Mathematical equation:

Removing the sign of proportionality we have,

Advantages

PI controller fuses the properties of the P and I controllers. It shows a maximum overshoot

and settling time similar to the P controller but no steady-state error.

Disadvantages

Since P-I controller has no ability to predict the future errors of the system it cannot decrease

the rise time and eliminate the oscillations.

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4.6.3.3 P-D Controller

The aim of using P-D controller is to increase the stability of the system by improving

control since it has an ability to predict the future error of the system response. In order to

avoid effects of the sudden change in the value of the error signal, the derivative is taken

from the output response of the system variable instead of the error signal. Therefore, D

mode is designed to be proportional to the change of the output variable to prevent the

sudden changes occurring in the control output resulting from sudden changes in the error

signal. In addition D directly amplifies process noise therefore D-only control is not used.

Mathematical equation:

Removing the sign of proportionality we have,

Advantages

Smaller maximum overshoot due to the 'faster' D action compared with other controller

types.

A steady-state error is visible, which is smaller than in the case of the P controller. This is

because the PD controller generally is tuned to have a larger gain Kc due to the positive

phase shift of the D action.

4.6.3.4 P-I-D Controller

P-I-D controller has the optimum control dynamics including zero steady state error, fast

response (short rise time), no oscillations and higher stability. The necessity of using a

derivative gain component in addition to the PI controller is to eliminate the overshoot and

the oscillations occurring in the output response of the system.

Mathematical equation:

Advantages

1-One of the main advantages of the P-I-D controller is that it can be used with higher order

processes including more than single energy storage.

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2- Zero steady state error.

The advantage of PID controller is its feasibility and easy to be implemented. The PID

gains can be designed based upon the system parameters if they can be achieved or estimated

precisely. Moreover, the PID gain can be designed just based on the system tracking error

and treats the system to be "black box" if the system parameters are unknown. However, PID

controller generally has to balance all three-gain impact to the whole system and may

compromise the transient response, such as settling time, overshoots, and oscillations. If the

system parameters cannot be precisely estimated or achieved, the designed PID gains may

not resist the uncertainties and disturbances, and thus present low robustness. Even though

the PID gains can be well-designed, the PID controller still has low robust ability compared

with the robust controller when the system encounters to multiple challenges from the

operating environment of the system, such as temperature, weather, power surge, and so on.

-PID is widely used in automotive and in general applications and has several sources.

So we decided to use PID controller. The PID controller has three tunable parameters, whose sum constitutes the adjustable

variable (AV). The summation of proportional, integral, and derivative terms gives the output

of the PID controller. Defining u(t) as the controller output, the final form of the PID design

Where,

Kp: Proportional gain, a tuning parameter

Ki : Integral gain, a tuning parameter

Kd: Derivative gain, a tuning parameter

E : Error = SP- PV

T : Time or instantaneous time

S : set point

C.S : control signal

O : Output

The basic idea behind a PID controller is to read a sensor, then compute the desired actuator

output by calculating proportional, integral, and derivative responses and summing those

three components to compute the output. First we need to know what a closed loop system is

and some of the terminologies associated with it.

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PID Theory

Figure 4-6 PID block diagram

Proportional Response The proportional component depends only on the difference between the set point and the

process variable. This difference is referred to as the Error term. The proportional gain (Kc)

determines the ratio of output response to the error signal. For instance, if the error term has a

magnitude of 10,

a proportional gain of 5 would produce a proportional response of 50. In general, increasing

the proportional gain will increase the speed of the control system response. However, if the

proportional gain is too large, the process variable will begin to oscillate. If Kc is increased

further, the oscillations will become larger and the system will become unstable and may

even oscillate out of control.

Integral Response The integral component sums the error term over time. The result is that even a small error

term will cause the integral component to increase slowly. The integral response will

continually increase over time unless the error is zero, so the effect is to drive the Steady-

State error to zero. Steady-State error is the final difference between the process variable and

set point. A phenomenon called integral windup results when integral action saturates a

controller without the controller driving the error signal toward zero.

Derivative Response The derivative component causes the output to decrease if the process variable is increasing

rapidly. The derivative response is proportional to the rate of change of the process variable.

Increasing the derivative time (Td) parameter will cause the control system to react more

strongly to changes in the error term and will increase the speed of the overall control system

response. Most practical control systems use very small derivative time (Td), because the

Derivative Response is highly sensitive to noise in the process variable signal. If the sensor

feedback signal is noisy or if the control loop rate is too slow, the derivative response can

make the control system unstable

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4.6.4 Definition of Terminologies

The control design process begins by defining the performance requirements. Control system

performance is often measured by applying a step function as the set point command

variable, and then measuring the response of the process variable. Commonly, the response is

quantified by measuring defined waveform characteristics such as:

-Rise Time is the amount of time the system takes to go from 10% to 90% of the steady-

state, or final, value.

-Percent Overshoot is the amount that the process variable overshoots the final value,

expressed as a percentage of the final value.

-Settling time is the time required for the process variable to settle to within a certain

percentage (commonly 5%) of the final value.

-Steady-State Error is the final difference between the process variable and set point.

Note that the exact definition of these quantities will vary in industry and academia.

Figure 4-7 graph showing PID definition of terminologies

After using one or all of these quantities to define the performance requirements for a control

system, it is useful to define the worst case conditions in which the control system will be

expected to meet these design requirements. Often times, there is a disturbance in the system

that affects the process variable or the measurement of the process variable.

In some cases, the response of the system to a given control output may change over time or

in relation to some variable. A nonlinear system is a system in which the control parameters

that produce a desired response at one operating point might not produce a satisfactory

response at another operating point. For instance, a chamber partially filled with fluid will

exhibit a much faster response to heater output when nearly empty than it will when nearly

full of fluid. The measure of how well the control system will tolerate disturbances and

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nonlinearities is referred to as the robustness of the control system.

Some systems exhibit an undesirable behavior called dead time. Dead time is a delay

between when a process variable changes, and when that change can be observed. For

instance, if a temperature sensor is placed far away from a cold water fluid inlet valve, it will

not measure a change in temperature immediately if the valve is opened or closed. Dead time

can also be caused by a system or output actuator that is slow to respond to the control

command, for instance, a valve that is slow to open or close.

Loop cycle is also an important parameter of a closed loop system. The interval of time

between calls to a control algorithm is the loop cycle time. Systems that change quickly or

have complex behavior require faster control loop rates.

Figure 4-8 graph showing dead time

4.6.5 Distance speed PID

When distance detected by sensor is less than the desired safe distance the car will slow

down but by what value …, this is why PID is used to control speed of car according to

distance

PID has a set point which is X , an input which is reading from ultrasonic , tuning parameters

Kp Ki Kd , and an output which is the speed of car

when distance decrease the speed decrease by a certain amount if this amount is not enough

to reach the safe desired distance the speed will decrease again and will increase if distance

detected increase

there is a limit of speeds set for the PID to have as an output

for example: if the cruise speed is Y

then the limits for PID output will be from 0 to a value less than Y

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Trial and error method Tuning method

Trial and Error Method: It is a simple method of PID controller tuning. While system or

controller is working, we can tune the controller. In this method, first we have to set Ki and

Kd values to zero and increase proportional term (Kp) until system reaches to oscillating

behavior. Once it is oscillating, adjust Ki (Integral term) so that oscillations stops and finally

adjust D to get fast response.

By following the previous steps using Arduino codes and Matlab to graph the performance of

the dc motor, we obtained the values of kp, ki and kd

kp=4, ki=0.02 and kd=0.01

4.6.6 Speed PID Theory of DC motor speed control

The speed of a DC motor is directly proportional to the supply voltage, so if we reduce the

supply voltage from 12 Volts to 6 Volts, the motor will run at half the speed. How can this be

achieved when the battery is fixed at 12 Volts?

The speed controller works by varying the average voltage sent to the motor. It could do this

by simply adjusting the voltage sent to the motor, but this is quite inefficient to do. A better

way is to switch the motor's supply on and off very quickly. If the switching is fast enough,

the motor doesn't notice it, it only notices the average effect.

When you watch a film in the cinema, or the television, what you are actually seeing is a

series of fixed pictures, which change rapidly enough that your eyes just see the average

effect - movement. Your brain fills in the gaps to give an average effect.

Now imagine a light bulb with a switch. When you close the switch, the bulb goes on and is

at full brightness, say 100 Watts. When you open the switch it goes off (0 Watts). Now if you

close the switch for a fraction of a second, then open it for the same amount of time, the

filament won't have time to cool down and heat up, and you will just get an average glow of

50 Watts. This is how lamp dimmers work, and the same principle is used by speed

controllers to drive a motor. When the switch is closed, the motor sees 12 Volts, and when it

is open it sees 0 Volts. If the switch is open for the same amount of time as it is closed, the

motor will see an average of 6 Volts, and will run more slowly accordingly.

As the amount of time that the voltage is on increases compared with the amount of time that

it is off, the average speed of the motor increases.

This on-off switching is performed by power MOSFETs. A MOSFET (Metal-Oxide-

Semiconductor Field Effect Transistor) is a device that can turn very large currents on and

off under the control of a low signal level voltage.

The time that it takes a motor to speed up and slow down under switching conditions is

dependent on the inertia of the rotor (basically how heavy it is), and how much friction and

63

load torque there is. The graph below shows the speed of a motor that is being turned on and

off fairly slowly.

Figure 4-9 On and off switching method of motor

You can see that the average speed is around 150, although it varies quite a bit. If the supply

voltage is switched fast enough, it won’t have time to change speed much, and the speed will

be quite steady. This is the principle of switch mode speed control. Thus the speed is set by

PWM – Pulse Width Modulation.

Ziegler Nicholas tuning method

Ziegler–Nichols Rules for Tuning PID Controllers. Ziegler and Nichols proposed rules for

determining values of the proportional gain integral time and derivative time based on the

transient response characteristics of a given plant. Such determination of the parameters of

PID controllers or tuning of PID controllers can be made by engineers on-site by experiments

on the plant. There are two methods called Ziegler–Nichols tuning rules: the first method and

the second method

First Method

In the first method, we obtain experimentally the response of the plant to a unit-step input, as

shown in Figure 4-10 .If the plant involves neither integrator(s) nor dominant complex-

conjugate poles, then such a unit-step response curve may look S-shaped, as shown in Figure

4-11 .This method applies if the response to a step input exhibits an S-shaped curve. Such

step-response curves may be generated experimentally or from a dynamic simulation of the

plant. The S-shaped curve may be characterized by two constants, delay time L and time

constant T. The delay time and time constant are determined by drawing a tangent line at the

inflection point of the S-shaped curve and determining the intersections of the tangent line

with the time axis and line c(t)=K, as shown in Figure 4-11

Figure 4-10 Unit-step response of a plant.

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Figure 4-11 S-shaped response curve.

The transfer function C(s)/U(s) may then be approximated by a first-order system with a

transport lag as follows:

Ziegler and Nichols suggested to set the values of and according to the formula

The PID controller tuned by the first method of Ziegler–Nichols rules gives:

Thus, the PID controller has a pole at the origin and double zeros at s=–1/L.

Second Method. In the second method, we first set and Using the proportional control

action only see Figure 4-12 increase Kp from 0 to a critical value Kcr at which the output

first exhibits sustained oscillations.(If the output does not exhibit sustained oscillations

for whatever value Kp may take, then this method does not apply.)

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Figure 4-12 Closed-loop system with a proportional controller.

Figure 4-13 Sustained oscillation with period Pcr

Thus, the critical gain Kcr and the corresponding period are experimentally determined

(see Figure 4-13). Ziegler and Nichols suggested that we set the values of the parameters

and according to the formula shown

The PID controller tuned by the second method of Ziegler–Nichols rules gives:

Thus, the PID controller has a pole at the origin and double zeros at s =4/Pcr .

Thus, we used first method of Ziegler Nicholas tuning as the system step response is S-

shaped and followed its steps by using Arduino and Matlab

1-we give dc motor pwm=255 by Arduino code to draw RPM with time by using Matlab

code.

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Figure 4-14 step response of motor.

2-use Matlab code to smooth the curve and draw tangent to get T and L

Figure 4-15 step response with tangent.

T=164 L =6.7

3-calculate kp,ki and kd

we get from equations Kp=29.3 Ki=2.18 Kd=98

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4-put calculated values of PID constants in PID code to control the motor to check the motor

response with these values by Matlab graph.

Figure 4.16 system performance.

4.7 Application in real cars

The driver selects the desired speed and sets the distance to be maintained to the vehicle

ahead. This gap can be set at several distances, adapting to the driving situation and

individual driving style. Standard ACC can be activated from speeds of around 30 km/h (20

mph) upwards and can support the driver, primarily on cross-country journeys or on

freeways.

A radar sensor monitors the driving situation ahead of the vehicle. The sensor transmits radar

waves that are reflected by objects in front of the vehicle. Based on the reflected signals,

ACC can detect preceding vehicles by calculating distance, direction and relative speed of

the vehicle ahead. In addition, ACC predicts the course of its own vehicle and can then

decide whether any of the vehicles ahead are relevant for distance control.

As long as the road ahead is clear, ACC maintains the speed set by the driver. If ACC detects

a preceding vehicle at lower speed, it adjusts the speed to match the vehicle ahead, ensuring

that the predefined minimum distance is kept. At lower speed deviations this can be realized

by reducing acceleration. If the detected vehicle is slower, ACC moderately brakes the

vehicle

If the preceding vehicle increases speed or clears the lane, ACC accelerates up to the desired

preset speed.

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Lane Keeping

Chapter 5

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5.1 Introduction

Many single-vehicle roadway departure (SVRD) accidents take place in uncritical traffic

situations and good weather conditions. According to accident data over the last 10 years

from German roadways that 14.5 percent of all accidents were due to unintended lane

departure, 35 per cent of these with deadly consequences. Reasons for lane departure are

many and range from the driver’s inability to control the vehicle (for example, on account of

severe weather conditions) to lack of attention, drowsiness, and intoxication or illness

leading to the loss of lane- keeping control.

Lane Keeping is a feature that in addition to Lane Departure Warning System automatically

takes steps to ensure the vehicle stays in its lane. Some vehicles combine adaptive cruise

control with lane keeping systems to provide additional safety.

A lane keeping mechanism can either

reactively turns a vehicle back into the lane if it

starts to leave or proactively keep the vehicle in

the center of the lane. Vehicle companies often

use the term "Lane Keeping" to refer to both

reactive Lane Keep and proactive Lane

Centering but the terms are beginning to be

differentiated.

5.2 History

The first production lane departure warning system in Europe was developed by the United

States company Iteris for Mercedes Actros commercial trucks. The system debuted in 2000,

and is now available on most trucks sold in Europe.

In 2002, the Iteris system became available on Freightliner Trucks' North American vehicles.

In both these systems, the driver is warned of unintentional lane departures by an

audible rumble strip sound generated on the side of the vehicle drifting out of the lane. No

warnings are generated if, before crossing the lane, an active turn signal is given by the

driver.

5.3 Timeline of available systems

1992: Mitsubishi Motors began offering a camera-assisted lane-keeping support system on

the Mitsubishi Debonair sold in Japan.

2001: Nissan Motors began offering a lane-keeping support system on the Cima sold in

Japan.

2002: Toyota introduced its Lane Monitoring System on models such as the Cardina

and Alphard sold in Japan; this system warns the driver if it appears the vehicle is beginning

to drift out of its lane. In 2004, Toyota added a Lane Keeping Assist feature to the Crown

Majesta which can apply a small counter-steering force to aid in keeping the vehicle in its

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lane. In 2006, Lexus introduced a multi-mode Lane Keeping Assist system on the LS 460,

which utilizes stereo cameras and more sophisticated object- and pattern-recognition

processors. This system can issue an audiovisual warning and also (using the Electric Power

Steering or EPS) steer the vehicle to hold its lane. It also applies counter-steering torque to

help ensure the driver does not over-correct or "saw" the steering wheel while attempting to

return the vehicle to its proper lane. If the radar cruise control system is engaged, the Lane

Keep function works to help reduce the driver's steering-input burden by providing steering

torque; however, the driver must remain active or the system will deactivate.

2003: Honda launched its Lane Keep Assist System (LKAS) on the Inspire. It provides up to

80% of steering torque to keep the car in its lane on the highway. It is also designed to make

highway driving less cumbersome, by minimizing the driver's steering input. A camera,

mounted at the top of the windshield just above the rear-view mirror, scans the road ahead in

a 40-degree radius, picking up the dotted white lines used to divide lane boundaries on the

highway. The computer recognizes that the driver is "locked into" a particular lane, monitors

how sharp a curve is and uses factors such as yaw and vehicle speed to calculate the steering

input required.

2004: In 2004, the first passenger-vehicle system available in North America was jointly

developed by Iteris and Valeo for Nissan on the Infiniti FX and (in 2005) the M vehicles. In

this system, a camera (mounted in the overhead console above the mirror) monitors the lane

markings on a roadway. A warning tone is triggered to alert the driver when the vehicle

begins to drift over the markings.

2005: Citroën became the first in Europe to offer LDWS on its 2005 C4 and C5 models, and

its C6. This system uses infrared sensors to monitor lane markings on the road surface, and a

vibration mechanism in the seat alerts the driver of deviations.

2007: In 2007, Audi began offering its Audi Lane Assist feature for the first time on the Q7.

This system, unlike the Japanese "assist" systems, will not intervene in actual driving; rather,

it will vibrate the steering wheel if the vehicle appears to be exiting its lane. The LDW

System in Audi is based on a forward-looking video-camera in its visible range, instead of

the downward-looking infrared sensors in the Citroën. Also in 2007, Infiniti offered a newer

version of its 2004 system, which it called the Lane Departure Prevention (LDP) system.

This feature utilizes the vehicle stability control system to help assist the driver maintain lane

position by applying gentle brake pressure on the appropriate wheels.

2008: General Motors introduced Lane Departure Warning on its 2008 model-

year Cadillac STS, DTS and Buick Lucerne models. The General Motors system warns the

driver with an audible tone and a warning indicator on the dashboard. BMW also introduced

Lane Departure Warning on the 5 series and 6 series, using a vibrating steering wheel to

warn the driver of unintended departures. In late 2013 BMW updated the system with Traffic

Jam Assistant appearing first on the redesigned X5, this system works below 25

mph. Volvo introduced the Lane Departure Warning system and the Driver Alert Control on

its 2008 model-year S80, the V70 and XC70 executive cars. Volvo's lane departure warning

system uses a camera to track road markings and sound an alarm when drivers depart their

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lane without signaling. The systems used by BMW, Volvo and General Motors are based on

core technology from Mobileye.

2009: Mercedes-Benz began offering a Lane Keeping Assist function on the new E-class.

This system warns the driver (with a steering-wheel vibration) if it appears the vehicle is

beginning to leave its lane. Another feature will automatically deactivate and reactivate if it

ascertains the driver is intentionally leaving his lane (for instance, aggressively cornering). A

newer version will use the braking system to assist in maintaining the vehicle's lane. In 2013

on the redesigned S-class Mercedes began Distronic Plus with Steering Assist and Stop &Go

Pilot.

2010: Kia Motors offered the 2011 Cadenza premium sedan with an optional Lane Departure

Warning System (LDWS) in limited markets. This system uses a flashing dashboard icon and

emits an audible warning when a white lane marking is being crossed, and emits a louder

audible warning when a yellow-line marking is crossed. This system is canceled when a turn

signal is operating, or by pressing a deactivation switch on the dashboard; it works by using

an optical sensor on both sides of the car.

Fiat is also launching its Lane Keep Assist feature based on TRW's lane keeping assist

system (also known as the Haptic Lane Feedback system). This system integrates the lane-

detection camera with TRW's electric power-steering system; when an unintended lane

departure is detected (the turn signal is not engaged to indicate the driver's desire to change

lanes), the electric power-steering system will introduce a gentle torque that will help guide

the driver back toward the center of the lane. Introduced on the Lancia Delta in 2008, this

system earned the Italian Automotive Technical Association's Best Automotive Innovation of

the Year Award for 2008. Peugeot introduced the same system as Citroën in its new 308.

5.4 Application in real cars Lane keeping (LK) system is one of the important technologies in the development of

advanced safety vehicle (ASV).

The lane keeping system mainly consists

of three parts: lane departure detection,

lane keeping control algorithm and

electric power steering system.

The lane departure detection module of

the lane keeping system uses a

CCD camera fixed on the windscreen

behind the rearview mirror to take

roadway image in front of the car. Such

that these images are being processed to extract the lane marking and decide the essential

lane model such as lateral

displacement and curvature.

Figure 5-1 Lane-keeping system structure

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The processed image is 2D that is being transformed into 3D space information, since the

system has to locate lane markings and the preceding vehicle in the image coordinates.

Those images are considered to be an input to the lane keeping controller. Lane keeping

controller will process them and calculate the steering angle and torque value through lane

keeping control algorithm. Finally, the motor of electric power steering will be controlled

according to the steering angle and torque command. This electric power steering system

consists of a torque sensor, an electric motor, a reduction gear, a column and a rack–pinion

mechanism.

5.5 Limitations of Lane keeping Systems in real cars

Modern lane departure warning systems are more reliable than earlier iterations of the

technology, but even the most advanced examples have limitations. These systems often rely

on visual information to track the relative position of a vehicle within its lane, so anything

that obscures the lane markers will render the technology useless. That means you usually

can’t depend on your LDW or LKS in heavy rain, snow, or if there is excessive glare from

the sun. Also lane keeping cannot be used in some other conditions because doing so may

result in several accidents such as: roads with sharp edges, when entering a sharp curve into

an interchange or junction, the stereo cameras’ field of view is obstructed.

5.6 Prototyping of Lane-keeping

Our prototype is a simulation of what happens in a real car by using an electrical car with

some electrical components.

5.6.1 Basic components:

1. Servo motor.

(Changes the angle of rotation to take the correct decision among road curvatures)

2. DC motor.

3. Dual H-bridge.

4. 2- Line tracking sensor (Detecting lane markings).

5.6.2 Algorithm

In the following lines, we are going to state the algorithm that we are going to follow to

implement this feature.

We have two infrared line tracking sensors that are handled in the two front sides of the car

(one on the front left side and one on the front right side). Those two sensors are always

checking the white lines in both sides. When any of the two sensors detects a white line (The

white line is an indication for the lane markings), an action is taken according to this

detection. This action is set inside an interrupt service routine so that the sensors give fast

response. For example, if the right sensor detects the white line, the car turns left till moving

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away from the line and then move forward until being straight. If the other left sensor detects

the line, it performs the same action as the right sensor but turn right. While if the car is

moving straight and none of the two sensors detects the white line, the car do not enter the

interrupt service routine and keep moving forward with the set speed of the function and do

not turn right or left. The following figure shows the flowchart of our algorithm.

Figure 5-2 Lane-keeping flowchart

5.6.3 Implementation and testing

First, we tested the most important hardware components that are used to fulfill the feature in

a right performance. And as mentioned above, this function is mainly based on the servo

motor and the IR line tracking sensor.

The type and the theory of operation of the servo motor was typically as mentioned in

chapter two and testing it was by checking its zero position and getting its maximum left and

right angles. Concerning the IR line tracking sensor, we have two sensors that were fixed on

both front sides of our car beside the wheels. The testing phase of the sensor, was by

checking if it is correctly can differentiate between the white and black colors of the track

model. This type of sensor reads 1 if it detects white and 0 if it detects black.

Second, we decided to implement this feature firstly on only one side of the car and after

approaching this implementation and overcoming all the problems faced we did the same

steps and solutions on the other side.

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Third, we started working on this function by the first car mechanical design that was long

and unstable. Concerning the track we worked on leather and drew the initial path of the

track as shown:

Figure 5-3 Showing first car design length.

Figure 5-4 first track material

Unfortunately, this step faced a problem that the sensor response was very slow and not

accurate such that whenever it detects the lane markings it takes some time before taking the

decision to go back to the lane or its center. We overcame this problem by adding some

modifications in the code. Those modifications were adding the reaction to the sensor

reading in an interrupt service routine so whenever it detects a line it directly goes and apply

what is required in the routine. The following is a shot of our code which shows how we

solved this problem.

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Fourth, we continued working with the same car with the same material but there occurred

another problem which was because the car goes out of lane when steering or taking the

curvature of the path and also the car was very slow. We found that those problems were

because of two main reasons. The first reason, the car design length was very long and it was

not similar to any aspects ratio of any real car while the second reason was that the car

wheels friction to the path material was not suitable to implement our feature. So overcoming

those problems was by two things. The solution of the first problem was changing the car

design by minimizing its length to meet the aspects ratio of a real car. While for the second

problem was by changing the track material to vinyl banner which helped to solve the

previous problem. The following figures will show the final car design and final track

material.

Figure 5-5 final track path and material

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Figure 5-6 final car design length.

Fifth, we worked with the previous results but there was a final problem (steering problem)

which appears clearly when the car is taking the shown curvature when it turns, it stops. So it

was obvious that this problem is a result of low torque of the Dc motor such that it does not

have enough power to push the motor to take the curvature and move with the set speed.

Before mentioning this problem solution, we firstly have to talk about the source of current

given to Dc motor. Dc motor mainly gets its current from the Dual h bridge that was

mentioned before in chapter 3 by connecting our motor to one channel only but we changed

the connection of the motor to the driver by connecting the two channels together then taking

a wire to the battery which means that the total current that is supported by the bridge is

totally given to the motor. By increasing current the torque problem is solved. The following

figure shows the new connection of the Dual H-bridge:

Figure 5-7 new connection of Dual H-bridge.

After solving all the above problems, the feature performance was typically as required.

Finally, we implemented the function of lane keeping on the right side of the car. It

performed well as the left sensor.

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5.6.4 Limitations of the implemented model

1. The inaccuracy of the used IR sensor.

2. The system will not perform well when facing sharp edges and this problem typically

appears in real cars as Subaru motors and they are working to solve this problem in

the near future.

5.7 Conclusion

The previous implemented prototype has succeeded in performing a good analogy between

the real system in passenger cars and our model. This analogy was validated by the means of

software and hardware components. Also the implemented system results show the system

can keep our car model driven within the lane center area, and further more prevent it from

any collisions caused by the unintentional lane departure.

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Lane departure

Chapter 6

79

6.1 Definition

A lane change has been defined as a deliberate and substantial shift in the lateral position of a

vehicle (Chovan et al., 1994). Worrall and Bullen (1970) described a lane change in three

parts: the head portion is the time and distance required for a vehicle to move from a straight-

ahead path to the first intercept of the lane line. The actual lane change starts when a vehicle

first encroaches on the lane line between the original and destination lanes. The maneuver

ends once the vehicle has completely crossed that line. The tail portion of the maneuver is the

time and distance required for a vehicle to return to a straight-ahead path in the destination

lane after crossing the lane line. Another view, offered by Van Winsum et al. (1999),

describes three sequential phases of the lane-change maneuver based on steering. The first

phase is an initial turn of the steering wheel to a maximum angle. The second phase begins

when the steering wheel is turned in the opposite direction and ends when the vehicle

heading approaches a maximum that occurs when the steering wheel angle passes through

zero (straight-ahead). During the third phase, the steering wheel is turned to a maximum

angle in the opposite direction to

stabilize the vehicle in the new

lane. This definition is useful when

only steering data is available.

However, lane changes performed

in curves may not be captured if

the driver does not reverse the

angle of the steering wheel.

Consideration for road geometry is

also required to operationally define a lane change. Wierwille (1984) described a lane change

in two parts. A heading deviation is introduced by a steering input that results in buildup of

lateral deviation. As the vehicle approaches the correct lateral position in the adjacent lane,

the heading deviation is removed by applying a steering correction in the direction opposite

that of the initial steering input. Lane changes can occur for a variety of reasons, such as

entering the roadway (merging), preparing to exit the roadway, anticipating vehicles merging

onto the roadway, anticipating a slowing lead vehicle, a change in the number of lanes

available, and swerving to avoid a forward crash threat such as a rapidly decelerating lead

vehicle. One of the most common types of lane change is a maneuver in which a driver

changes lanes to pass a slower lead vehicle to maintain current speed (Fancher, 1999;

Hetrick, 1997).

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6.2 Introduction

Transportation researchers estimate that lane change crashes account for 4 to 10% of all

crashes (Barr & Najm, 2001; Eberhard et al., 1994; Wang & Knipling, 1994; Young,

Eberhard & Moffa, 1995). Research has revealed that most drivers in lane change crashes did

not attempt an avoidance maneuver; this suggests that the driver did not see or was unaware

of the presence of another vehicle or crash hazard (Chovan, Tijerina, Alexander &

Hendricks, 1994; Eberhard et al.; Tijerina, 1999). According to Knipling (1993), 75% of lane

change/merge crashes involve a recognition failure by the driver. Thus, to reduce lane change

crashes, it would seem that drivers must be made aware of impending hazards before lane

change initiation (Chovan et al.; Tijerina). The general objective of our project was to

perform lane change according to some data. This data was sent from some sensors to the car

and as a result our car takes the correct decision.

6.3 Basic components

1. Servo motor

2. Dc motor

3. Dual H-bridge.

4. Five ultrasonic sensors

*The front sensor is used to check the distance of the front target

*The two right side sensors are used to check the clearance distance of the right lane.

*The two left side sensors have the same function of the right ones but for the left

lane.

5. Two infrared line tracking sensors

*One sensor in the left and one in the right side. If any of them becomes high the

function of the lane departure stops according to the direction of motion.

6.4 Implementation

We implemented the lane departure code alone before merging without taking into

consideration other functions or codes:

First we began to implement a code just to make the car go straight in the middle of the lane

then turn right to find out the angle fits changing the car lane, we tried several angles to find

the perfect angle fits several positions of changing the lane.

Second we thought about how to stop the car from turning right, how the car will know that

it is in the new lane and should go straight in the middle of it. This could be implemented by

using the white line separates between the new lane and the old one as when the car turns

right to change the lane it steps above the white line and the IR sensor gives high so at this

moment the car goes straight for a while and then left to be in the middle of the new lane.

After this step we have already reached how the car will change its lane correctly.

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Third we added the condition or the cause that will make the car change its lane, when the

distance measured by the front ultrasonic is small (less than 30 cm) for two successive times

it takes the action of lane departure, we checked the condition with changing the lane.

Fourth we did the same steps for the left side of the car.

Fifth we thought about what if the next lane is not empty or there is a car in it and our car

couldn’t change its lane, we used four sensors (two for the right side and two for the left side)

in the car body to add a condition for checking the lanes to find whether they are empty and

the car can make lane departure or not, so the car should stay on its lane and follow ACC

commands, if the front sensor’s distance is less than thirty cm it goes and check both sides’

sensors to find which lane is empty and goes to it (right lane or left lane) by following lane

departure steps if there is no empty lane it stays in its lane and doesn’t change it.

Sixth we merged all the conditions and the lane departure implemented correctly and did its

function as supposed.

6.5 Algorithm

6.5.1 Concept

-In real life a car change lanes if there is another car in front of it and distance between the

two cars is very small or that car is really slowing down the driver then checks next lanes if

one of them is empty he changes lane if not then he stays in his lane

-Similarly this is what our car is doing as shown in figure

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6.5.2 Algorithm

Figure 6-1 lane departure flow chart

Microcontroller checks front distance periodically .If it’s in the range between 50 and 30 it

increases the counter by one each time but if it is not in this range it will put counter to zero.

As counter equals to 10 (10 successive times distance between 30 and 50 ) the car will

change lanes but before it does it has to decide which lane it will turn to so it checks first its

left sides if it is empty then change to left lane happens and then assign counter value to

zero , if it isn’t empty then checks right side if it is empty then change to right lane happens

and assign counter value to zero. If both lanes are busy then car remain in its lane moving

according to ACC

Change lane function

In change lane left the car want to move to its left lane so car starts turning left until the right

IR sensor detects lane which means that the whole car left its lane then car moves right then

straight to adjust its position

6.6 Applications in real life

Lane departure has not been implemented yet in real cars only lane departure warning for

lane keeping was implemented and most of the modern cars has it.

Companies are working hard to implement lane departure in the near future by research and

experiments; we will see lane departure in our real life very soon.

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Integrating Functions

Chapter 7

84

Merging

Integration of three functions to work properly together and protect the car from collision by

adaptive cruise control (ACC), keeps car inside its lane by lane keeping and avoid traffic jam

by lane departure without any conflict between the codes.

7.1 Algorithm

Figure 7-1 lane keeping and lane departure flag flow chart

As IR sensor in right or left side reads high (on white lane line) Arduino executes ISR and

check whether lane_dep flag is 0 or 1.If lane_dep=1 the car does nothing and resume what it

was doing. If lane_dep=0 it makes lane keeping and follow its algorithm.

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Figure 7-2 merging flow chart

The main function by which the car goes is ACC and according to the distance of front

ultrasonic decisions are made, interrupt_en is a variable act as a flag to differentiate between

moving straight with set speed or make lane keeping with fixed speed as this speed is

assigned by ACC.

If distance is bigger than set point it goes to check interrupt_en flag whether it is 0 or 1. If

interrupt_en=1 and IR sensor is high it follows lane keeping algorithm with constant speed .If

interrupt_en =0 car moves with set speed in straight direction according to ACC.

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7.2 Implementation

First we started to merge lane keeping and ACC by using variable as a flag to differentiate

between ACC and lane keeping, making ACC responsible for speeds and lane keeping

responsible for directions.

Second we added lane departure, to stop lane keeping while lane departure. We used variable

as a flag always low for lane keeping and becomes high when lane departure condition

fulfilled.

According to these flags car knows which track to follow and no conflicts occur.

By integration between 3 functions we succeeded in making a car that is almost capable of

driving itself without a driver and do its functions well.

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Auto parking

Chapter 8

88

8.1 Definitions

Too lazy to park (or just not very good at it)? Then let an automatic parking system remove

the effort and stress.

The aim of this function is to design and implement self-parking car system that moves a car

from a traffic lane into a parking spot through accurate and realistic steps which can be

applied on a real car.

8.2 Introduction

Self-Parking is an automatic parking system found in modern cars. It makes use of various

sensors, cameras, ...etc., to get information about the surroundings of a car and then use

complex processing algorithms to park the car parallel, perpendicular or angle parking based

on space without the help of the drive .Our project will be a miniature model of a real life car

which will sense the availability of parking space by using sensor and park itself

automatically parallel and perpendicular in the available parking area.

8.3 Application

Many car drivers today face a problem in parking their cars. Self-parking car can park into

smaller spaces than most drivers can manage it on their own. This makes it easier for people

to find parking spaces, and allows the same number of cars to take up small spaces. When

someone parallel parks, they often block a lane of traffic for at least a few seconds. If they

have problems getting into the spot, this can last for several minutes and seriously hold up

traffic. We have tried to handle this problem in our project by making an automated parking

car which parks itself. Auto parking is function in Google-self driving car.

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8.4 Hardware

8.4.1Materials needed

L298N Dual H-Bridge Motor Controller module

Male to Male jumper wires

Raspberry Pi.

A DC Motor , 7-12v

Figure 8-1 H-bridge used with auto parking

Figure 8-2 male to male jumpers

Figure 8-3 Raspberry pi

Figure 8-4 second car DC motor

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Ultrasonic sensor.

4 x AA or AAA battery holder and batteries.

1kΩ Resistor

2kΩ Resistor

Mini breadboard

Figure 8-5 ultrasonic sensor of second car

Figure 8-6 battery holder and batteries

Figure 8-7 resistance in second car connection

Figure 8-8 mini breadboard

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8.4.2 Connection DC Motor

It consists of two DC motors were the first type widely used, since they could be powered

from existing direct-current lighting power distribution systems.

8.4.3 Connection through H-bridge

We'll start with the Dual H-Bridge Motor driver.

Figure 8-9 second car DC motor connection

So, depending on which GPIO pin we activate, it will out out +12V or -12V on the A output

or the B output. In the picture above, the motor is connected to the A output. If we wanted to

use a DC motor, we could use both A and B outputs, and change the code to pulse the GPIO

pins accordingly. For now, we're just using one output to a plain 5-12V DC motor.

Pins:

Out 1: Motor A lead out

Out 2: Motor A lead out

Out 3: Motor B lead out

Out 4: Mo (Can actually be from 5v35v, just marked as 12v)

GND: Ground

5v: 5v input (unnecessary if your power source is 7v35v, if the power source

is 7v35v then it can act as a 5v out)

EnA: Enables PWM signal for Motor A In1: Enable Motor A

In2: Enable Motor A

In3: Enable Motor B

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In4: Enable Motor B

EnB: Enables PWM signal for Motor B

What is an "H-Bridge" and how does a DC motor change direction?

When you hook a dc motor up to + wire on one lead and - wire on the other lead, the

motor turns a certain direction. When you switch the wires to other leads, the motor turns the

other direction. But how do we "electronically" switch the electric polarity using

circuits. ANSWER: H-Bridge.

The circuits for H-Bridges are non-trivial for the beginner. The L298N module is a simple

ready to rock and roll "dual" H-bridge circuit board. That means you have the ability to

control TWO motors in either direction.

8.4.4 Connection of the battery

By using the power from batteries to run motors too. Purchase 6V DC batteries, (NOTE:

batteries are already DC power) to drive motors.

NEVER USE RASPBERRY PI built in power to DRIVE motors.

Dc motor sizing:

The power to your motor needs to match. The motors we have are 6V DC hobby motors. If

you're building something for real rather than just a quick learning tutorial like this one.

Make sure you match you power drive to the motor requirements. Every motor needs

different drive voltages. If you have less voltage than the motor needs then the motor will be

under powered and may not turn what you want or might burn your power supply out, or

worst start melting stuff. If your power supply is too high like for example you put 9 or 12

volts into a 6 volt motor, then the motor will turn really fast and hard. You see people hack

their kids toy race cars with bigger batteries, the cars go fast and have a ton more power than

the default design. These tricks always come at a costs for safety, reliability, and component

breaking (melting, fires etc.).

In our simple getting a motor to turn tutorial, a 5vDC old phone charger will suffice to drive

a 6v motor.

8.4.5 Connector wires for driving the direction on the motor with Raspberry Pi:

The "GPIO" pins on the Raspberry PI will be used to "trigger" the l298n direction.

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Figure 8-10 Raspberry pi GPIO

There are TWO pins per motor. One pin drives the power, + to one lead when the pin is

activated.

If you have a PI2 or the newer B+ then there is a different pin layout for that. I did this one

with the older base model B.

I like the pin outs that show the actual unit so you can rotate it to match the one on your desk

next to your machine and get the correct pins. I'm always spatially screwing up, so I'll end up

hooking up the wrong pins and on the raspberry pi, the two wrong pins together could cause

the poor thing to burn out or at least only crash and reboot (this has happened to me many

times, so far lucky not the burn the PI up yet).

We used a bread board to make the wire connections easier.

1- Hook Raspberry USB micro charger up to your standard PI input.

2- Hook 2nd power supply for your DC motor up to the + - on the bread board.

8.4.6 Control Motor speed by PWM

By using Pulse Width Modulation (or PWM) is a technique for controlling power. We use it

here to control the amount of power going to the motor and hence how fast it spins.

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Figure 8-11 PWM

Every 1/500 of a second, the PWM output will produce a pulse. The length of this pulse

controls the amount of energy that the motor gets. No pulse at all and the motor will not

turn, a short pulse and it will turn slowly. If the pulse is active for half the time, then

the motor will receive half the power it would if the pulse stayed high until the next

pulse came along

Using PWM in RPi.GPIO To create a PWM instance:

p = GPIO.PWM (channel, frequency)

To start PWM:

p.start (dc) # where dc is the duty cycle (0.0 <= dc <= 100.0)

To change the frequency:

p.ChangeFrequency (freq) # where freq is the new frequency in Hz

To change the duty cycle:

p.ChangeDutyCycle (dc) # where 0.0 <= dc <= 100.0

To stop PWM:

p.stop ()

Note that PWM will also stop if the instance variable 'p' goes out of scope.

Concept of software PWM:

By changing voltage of battery then change voltage of motor and control its speed by duty

cycle

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8.4.7 Connection of ultrasonic sensor Range Sensor on the Raspberry Pi

A basic ultrasonic sensor consists of one or more ultrasonic transmitters (basically speakers),

a receiver, and a control circuit. The transmitters emit a high frequency ultrasonic sound,

which bounce off any nearby solid objects. Some of that ultrasonic noise is reflected and

detected by the receiver on the sensor. That return signal is then processed by the control

circuit to calculate the time difference between the signal being transmitted and received.

This time can subsequently be used, along with some clever math, to calculate the distance

between the sensor and the reflecting object.

The HC-SR04 Ultrasonic sensor we’ll be using for the Raspberry Pi has four pins:

ground (GND), Echo Pulse Output (ECHO), Trigger Pulse Input (TRIG), and 5V Supply

(Vcc). We power the module using Vcc, ground it using GND, and use our Raspberry Pi to

send an input signal to TRIG, which triggers the sensor to send an ultrasonic pulse. The pulse

waves bounce off any nearby objects and some are reflected back to the sensor. The sensor

detects these return waves and measures the time between the trigger and returned pulse, and

then sends a 5V signal on the ECHO pin. ECHO will be “low” (0V) until the sensor is

triggered when it receives the echo pulse. Once a return pulse has been located ECHO is set

“high” (5V) for the duration of that pulse. Pulse duration is the full time between the sensor

outputting an ultrasonic pulse, and the return pulse being detected by the sensor receiver. Our

Python script must therefore measure the pulse duration and then calculate distance from

this.

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Using Voltage Dividers:

The sensor output signal (ECHO) on the HC-SR04 is rated at 5V. However, the input pin on

the Raspberry Pi GPIO is rated at 3.3V. Sending a 5V signal into that unprotected 3.3V input

port could damage your GPIO pins, which is something we want to avoid! We’ll need to use

a small voltage divider circuit, consisting of two resistors, to lower the sensor output voltage

to something our Raspberry Pi can handle.

A voltage divider consists of two resistors (R1 and R2) in series connected to an input

voltage (Vin), which needs to be reduced to our output voltage (Vout). In our circuit, Vin

will be ECHO, which needs to be decreased from 5V to our Vout of 3.3V.

The following circuit and simple equation can be

applied to many applications where a voltage

needs to be reduced. If you don’t want to learn the

techy bit, just grab 1 x 1kΩ and 1 x 2kΩ resistor.

So, we’ll use a 1kΩ for R1 and a 2kΩ resistor as R2

as shown:

Figure 8-12 voltage divider circuit

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Assemble the Circuit

We’ll be using four pins on the Raspberry Pi

for this project: GPIO 5V [Pin 2]; Vcc (5V

Power), GPIO GND [Pin 6]; GND (0V Ground), GPIO 23 [Pin 16]; TRIG (GPIO Output)

and GPIO 24 [Pin 18]; ECHO (GPIO Input)

:

Figure 8-13 ultrasonic sensor connection with Rpi

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Steps

1. Plug four of your male to female jumper wires into the

pins on the HC-SR04 as follows: Red; Vcc, Blue; TRIG,

Yellow; ECHO and Black; GND.

2. Plug Vcc into the positive rail of your breadboard, and

plug GND into your negative rail.

3. Plug GPIO 5V [Pin 2] into the positive rail, and GPIO

GND [Pin 6] into the negative rail

4. Plug TRIG into a blank rail, and plug that rail into GPIO

23 [Pin 16]. (You can plug TRIG directly into GPIO 23 if

you want).

5. Plug ECHO into a blank rail, link another blank rail

using R1 (1kΩ resistor)

6. Link your R1 rail with the GND rail using R2 (2kΩ

resistor). Leave a space between the two resistors.

7. Add GPIO 24 [Pin 18] to the rail with your R1 (1kΩ resistor). This GPIO pin needs to sit

between R1 and R2

That's it! Our HC-SR04 sensor is connected to our Raspberry Pi as shown:

Figure 8-15 ultrasonic real shot connection with Rpi.

Figure 8-14 ultrasonic sensor pins

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Sensing with Python

Now that we’ve hooked our Ultrasonic Sensor up to our Pi, we need to program a Python

script to detect distance.

The Ultrasonic sensor output (ECHO) will always output low (0V) unless it’s been triggered

in which case it will output 5V (3.3V with our voltage divider!). We therefore need to set one

GPIO pin as an output, to trigger the sensor, and one as an input to detect the ECHO voltage

change.

First, import the Python GPIO library, import our time library (so we make our Pi wait

between steps) and set our GPIO pin numbering.

Import RPi.GPIO as GPIO

import time

GPIO.setmode (GPIO.BCM)

Next, we need to name our input and output pins, so that we can refer to it later in our Python

code.

We’ll name our output pin (which triggers the sensor) GPIO 23 [Pin 16] as TRIG, and our

input pin (which reads the return signal from the sensor) GPIO 24 [Pin 18] as ECHO.

TRIG = 23

ECHO = 24

we’ll then print a message to let the user know that distance measurement is in progress. . . .

Print "Distance Measurement in Progress"

Next, set your two GPIO ports as either inputs or outputs as defined previously.

GPIO.setup (TRIG, GPIO.OUT)

GPIO.setup (ECHO, GPIO.IN)

then, ensure that the Trigger pin is set low, and give the sensor a second to settle.

GPIO.output (TRIG, False)

print "Waiting for Sensor to settle"

time. Sleep(2)

The HC-SR04 sensor requires a short 10uS pulse to trigger the module, which will cause the

sensor to start the ranging program (8 ultrasound bursts at 40 kHz) in order to obtain an echo

response. So, to create our trigger pulse, we set out trigger pin high for 10uS then set it low

again.

GPIO.output (TRIG, True)

time. Sleep (0.00001)

GPIO.output(TRIG, False)

Now that we’ve sent our pulse signal we need to listen to our input pin, which is connected to

ECHO.

The sensor sets ECHO too high for the amount of time it takes for the pulse to go and come

back, so our code therefore needs to measure the amount of time that the ECHO pin stays

high. We use the “while” string to ensure that each signal timestamp is recorded in the

correct order.

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The time.time () function will record the latest timestamp for a given condition. For example,

if a pin goes from low to high, and we’re recording the low condition using the time.time ()

function, the recorded timestamp will be the latest time at which that pin was low.

Our first step must therefore be to record the last low timestamp for ECHO (pulse_start) e.g.

just before the return signal is received and the pin goes high.

While GPIO.input (ECHO) ==0:

pulse_start = time.time ()

Once a signal is received, the value changes from low (0) to high (1), and the signal will

remain high for the duration of the echo pulse. We therefore also need the last high

timestamp for ECHO (pulse_end).

While GPIO.input (ECHO) ==1:

pulse_end = time.time ()

we can now calculate the difference between the two recorded timestamps, and hence the

duration of pulse (pulse_duration).

pulse_duration = pulse_end - pulse_start

with the time it takes for the signal to travel to an object and back again, we can calculate the

distance using the following formula.

The speed of sound is variable, depending on what medium it’s travelling through, in

addition to the temperature of that medium. However, some clever physicists have calculated

the speed of sound at sea level so we’ll take our baseline as the 343m/s. If you’re trying to

measure distance through water,

This is where you’re falling down – make sure you’re using the right speed of sound!

We also need to divide our time by two because what we’ve calculated above is actually the

time it takes for the ultrasonic pulse to travel the distance to the object and back again. We

simply want the distance to the object! We can simplify the calculation to be completed in

our Python script as follows:

We can plug this calculation into our Python script:

distance = pulse_duration x 17150

Now we need to round our distance to 2 decimal places (for neatness!)

Distance = round (distance, 2)

Then, we print the distance. The below command will print the word “Distance:” followed

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by the distance variable, followed by the unit “cm”

print "Distance:” distance,"cm"

Finally, we clean our GPIO pins to ensure that all inputs/outputs are reset

GPIO.cleanup ()

Using three ultrasonic sensor to get Information about the surroundings of a car

Two in the front side and back side of the car the last one in the back of

Figure 8-16 second car final shot

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Testing:

Figure 8-17 testing ultrasonic code

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8.5 Parking types

The first thing you have to consider is the size of the car you are driving. This is major for

calculating distances. Depending on whether you are doing parallel or perpendicular parking.

1- Parallel parking

Parallel parking is a method of parking a

vehicle parallel to the road, in line with other

parked vehicles. Parallel parking usually

requires initially driving slightly past the

parking space, parallel to the parked vehicle in

front of that space, keeping a safe distance,

then followed by reversing into that space.

Subsequent position adjustment may require

the use of forward and reverse.

2- Perpendicular parking

Perpendicular parking is done at

a 90-degree angle to the curb.

Perpendicular spaces make

maneuvering the vehicle more

difficult than angle parking, but

the procedure requires fewer

steps than parallel parking.

Perpendicular parking is similar

to angle parking, but requires

greater care in turning. You will

find perpendicular parking in

most parking lots, which

normally

provide spaces marked in a 90-

degree angle to the curb or a

building.

Figure 8-18 parallel parking

Figure 8-19 perpendicular parking

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3- Angle parking.

Angle parking is similar to perpendicular parking,

except the cars are aligned in an angle. Normally

the angle is aligned with the direction cars

approach the parking space. It makes it a lot easier

to drive into the parking space in contrast to

perpendicular parking, where the parking space is

at a 90 degree angle. With angle parking there is a

gentler turn. Not only is it easier to park, but it’s

also faster and the parking spaces are smaller,

making it possible to add more parking spaces in

the same size area. Normally you see angle paring

inside parking garages and on streets that are wide

enough to have room left for the traffic to pass.

8.6 Parallel Parking

Parallel parking is considered to be one of the

hardest skills for new drivers to learn and is a

required part of most road tests. Parallel parking enables the driver to park a vehicle in a

smaller space than would be true of forward parking. Driving forward into a parking space on

the side of a road is typically not possible unless two successive parking spaces are empty.

New drivers learn to use reference points to align themselves in relation to the car in front of

the space, to determine the proper angle for backing, and to determine when to turn the

steering wheel while backing.

8.6.1 Parallel Parking steps

Detecting Space

When front-side and back-side sensors detect enough space at the same time

Figure 8-21 finding parking area

Find a suitable space. Look for a space you can safely get your car into without hitting

another car. It's best to find a space that's a few feet (a meter or so) longer than your car.

Steps for parking:

Figure 8-20 angle parking

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1- Stop at the same side in the front car by sensing two side sensors

Claim the space and position yourself to back in. When you see the space, signal toward it

and slow down. Approaching quickly, or in a truck, in which case you should brake gently at

first so they can see the lights and slow down safely. Pull up next to the car in front of the

space (or, at the end of the row of cars, where that car would be), close beside it and with the

middle of your car (the "B-pillar" between the doors) even with the rear of the next car.

2- Turning motor back-right by 45 degree.

Figure 8-23 step 2 for parallel parking

Turn and prepare to back in. Shift your vehicle into reverse to make sure the street behind

you is clear of traffic before you begin to back up.

Figure 8-24 rest of step 2 for parallel parking

Look over your other shoulder at the space to assess the gap. Turn the steering wheel hard

right, creating a 45 degree angle. When reversing, think of pushing (the top of) the wheel the

way you want the car to go.

Figure 8-22 step 1 for parallel parking

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3- Stop turning motor back when its corner was in the same line with the front car

Figure 8-25 step 3 for parallel parking

Back most of the way in. Release the brakes and slowly begin turning somewhat sharply and

backing in to the space. While backing into the space often check in front of and around your

car. Continue until the rear wheel facing the curb is nearly as close as the final distance

desired from it (no more than 1 foot (30 cm) ultimately being desirable) and the rear end of

the car is still a few feet (about a meter) from the car behind.

4- Turning motor back-left until detecting Space Park in the right way

Figure 8-26 step 4 for parallel parking

Straighten out as you finish pulling in. Turn the steering wheel to the left once the rear of

your vehicle is mostly in the space, still going backward. (In a loose space, a good time to

make the switch is when your front bumper is next to the rear bumper of the car in front.)

You'll maneuver completely into the space and straighten out your car at the same time.

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8.6.2 Algorithm:

Detect Space

Figure 8-27 parallel parking detect space state diagram

parking Algorithm:

8.7

Figure 8-28 parallel parking state diagram

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8.7 Perpendicular parking

Remember the two important rules when parking is move slow and turn fast. Also remember

to always look behind when your car is moving in reverse.

8.7.1 Steps:

1. Detecting space by using side-front sensor if it detects enough space for parking, the

car will start parking.

2. Turning motor Front-left until back sensor measures small distance.

3. Turning motor Back-right until side-back sensor detects good space far from the

another car.

4. Turning motor front until the back sensor detect the end of parking space.

8.7.2 Perpendicular Parking Algorithm

Figure 8-29 perpendicular parking state diagram

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8.8 Tuning method

We made a lot of Trials and there are a lot of error methods because of Problems:

o Due to mechanical design the turning curve is big with respect to the car

size!!

o Also RC car has small turning wheels which makes the curve inaccurate.

Solved by:

o Moving forward for enough space.

These will require plenty of extra room to complete the turn. Semi-trucks/vehicles with

trailers will turn backwards opposite the direction you turn your wheel

Figure 8-30 car moving forward for enough space

General sense of the dimensions of the area you will be parking in, Evaluate the size of the

parking spot. If you feel that the spot might not be a good fit.

Figure 8-31sensing perpendicular parking area dimensions.

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Indoor Positioning

Chapter 9

111

9.1. Introduction

GPS (Global Positioning System ) is system to find location and position of the

objects ,receives the signal from the satellites and makes a triangulation process to

determine the locations of the objects.

Figure 9-1 Positioning Satellite in the orbit

As shown in figure9.1 The GPS is high accuracy in outdoor positioning However high

sensitivity GPS in positioning but the signal is attenuated and reflected by the building

materials in some indoor positioning, so GPS in indoor position system is very low accurate.

Indoor position systems used to locate the object inside the buildings and closed

environments. These systems use optical tracking's, wireless concepts or ultrasonic

techniques.

There are many methods for positioning system with different architectures to determine the

2D and 3D positions of objects. They have different accuracies, configurations, and

reliabilities.

We divided the localization system into active and passive system, the active system when

the persons or objects carry electronic device to send the signal or information to positioning

system to help it for knowing the position of object .In passive system, the persons or objects

don’t carry electronic device.

The position is estimated based on the variance of a measured signal .

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Figure9-2 Localization systems

The most techniques used in indoor positioning are: Time of Arrival (TOA), Received Signal

Strength Indicator (RSSI), Time Difference of Arrival (TDOA) and Angle of Arrival (AOA)

. The following list represents a range of possible positioning technologies:

(1) Ultra-wideband (UWB)

(2) Wireless local area networks (WLANs)

(3) Field strength systems

(4) Next-generation indoor positioning systems

(5) Radio Frequency Identification (RFID).

There are mainly four technique are used in indoor positioning Algorithms.

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9.2 Indoor positioning Algorithms

9.2.1 Angle of arrival (AOA Algorithm)

The angle of arrival (AoA) algorithm detect the angle of arrival of the mobile signal coming

from a known location at which it is received at multiple reference point.

Figure 9-3Triangulation

You are at A, from where you can see B and C.

To calculate the position by measuring two directions towards two reference points. Since

the positions of the reference points are known, it is hence possible to construct a triangle

where one of the sides and two of the angles are known, with the observer at the third point.

This information is enough to define the triangle completely and hence deduce the position

of the observer.

Using triangulation with transmitters requires the angle of incidence (angle of arrival, or

AoA) of a radio signal to be measured. This can be done using several antennas placed side

by side (an array of antennas, and to measure the phase difference between the signals

received by the antennas.

Figure 9-4 an antenna array

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9.2.2 Time of arrival (TOA Algorithm)

TOA algorithm needs to measure the transmission time between the receiver and the

transmitter signal. The distance of the receiver and the transmitter can be got from the speed

and the measured time of the signal.

Figure 9-5 the length of the arrows corresponds to the arrival time at receiver P

Then the location of target estimates by using triangulation .ToA method needs precise

knowledge of the transmission start time. Due to this all receiving beacons along with mobile

devices are accurately synchronized with a precise time source.

ToA is the most accurate technique used in indoor environment which can filter out multi-

path effects one of the disadvantages of TOA approach is the requirement for precise time

synchronization of all the devices.

9.2.3 Time difference of arrival (TDOA Algorithm) Measure the relative position of the mobile transmitter by inspects the difference in time at

which the signal arrives at multiple measuring point.

Figure 9-6 positioning based on TDOA measurements.

The transmitter must lie on a hyperboloid with a constant range difference between the two

measuring points. The equation of the hyperboloid is given by

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Where (xi, yi, zi) and (xj, yj, zj ) represent the fixed receivers i and j; and (x, y, z) represent

the coordinate of the target.

Target location estimated by the intersection of two or more TDOA measurements.

9.2.4 Received Signal Strength (RSS)

(RSSI) was employed to estimate the distances between transmitters and receivers.

Usually RF signals are used .The location of the objects are determine by calculating the

distance of the object from the transmitters using triangulation or tri-alteration technique or

collect the signal strength from three Access Pointes at positioning location and calculate

distance between positioning location to each AP by Log-distance path loss model. Then the

positioning location is estimated by triangulation algorithms .

Log-distance path loss model

Because signal strength will lost with increasing of the transmit distance. Log-distance path

loss model is described as Pr (d) =Pt - PL (d) In this format, Pr (d) [dBm] is signal strength

of receiver. Pt [dBm] is the signal strength of transmitter. d :is the distance from transmitter

to receiver which is the distance between AP and user. PL(d) [dB] is the path loss of signal

strength between transmitter and receiver PL(d)=PL(d0)+ 10nlog(d/d0)+ Xg In this

format, Xg is random variable which has Gaussian distribution with standard deviation and

zero mean. n is the rate of path loss increases with distance. d0 is the distance of a “close-in”

point, usually 1m.

The values of n depend on the building layout and construction material, they are derived

empirically.

Distances between positioning location and AP1,AP2,AP3 are written as (d1,d2,d3), then

three intersecting circles with radius of (d1,d2,d3) are constructed, the center of circles are

the locations of three APs, the intersection of these circles will be the positioning

location(X,Y). The condition illustrates in Figuer8.2.2.5

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Positioning Equation can be written as:

(X-xi) 2+(Y-yi) 2=di 2,i=1,2,3. In this format, (xi, yi) indicates the coordinate of ith AP, (X,

Y) indicates coordinate of positioning

Location.

9.3 Available Positioning Systems

9.3.1 Infrared base system

(IR) positioning systems are one of the most common positioning systems that use wireless

technology. The spectral region of infrared has been used in various ways for detection or

tracking of objects or persons and available in various wired and wireless devices such as

mobile phone, PDAs, and TV. Most IR based Wireless devices uses line-of-sight (LOS)

communication mode between transmitter and receiver without interference. From strong

light source

Figure 9-7 position measuring

Figure 9-8 positioning systems

117

Figure 9-9 infrared base system positioning

The previous figure is to have a GPS-like system composed of several beacons that send out

infrared signals together with ultrasonic bursts. The infrared flash of a single beacon almost

arrives instantaneously at the robot that is equipped with a combined infrared/ultrasonic

receiver. But the ultrasonic signal only arrives with a certain delay, due to the "slow" sound

velocity, in comparison to the speed of light. The receiver is able to measure the time lag

between both signals and deduce the distance between the robot and the beacon

Disadvantages

Accuracy was limited to about 4cm

Multi-robot applications weren't possible

Neither the IR-sender nor the ultrasonic transmitter were optimal in terms of power

efficiency, with the result that the system was too much directionally sensitive and

thus could not be used in larger rooms

Synchronization between the beacons was too difficult

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9.3.2 Ultrasound base system

Is a technology based on the nature of bats and operates in the low frequency band compared

to the other two signaling technologies.

The ultrasound signals are used to estimate the position of the emitter tags from the receivers.

Ultrasound is unable to penetrate walls but reflects off most of the indoor obstructions.

Ultrasound it has a lower level of accuracy (in centimeters) and suffers a lot

Of interference from reflected ultrasound signals propagated around by other sources such as

the collision of metals.

9.3.2.1 Ultrasound system application

Figure 9-10 ultrasound system application

Basically will have mic mounted on the target and will be listening for a signal from the

speakers. There will be four ultrasound speakers in each corner emitting sound. At a start the

target will send by RF signal to first speaker to send sound and then will measure time and

calculate distance. Then will do this process with second speaker and so on. When it will

come to the end it will take all distance and calculate where exactly it is on field.

119

9.3.3 Ultra-wideband (UWB)

Ubisense is based on Ultra-wideband technology, very high accurate, 15 cm for 95% of the

readings due to the use of the active tags signal triangulation, use the techniques TDOA and

AOA to know the position of specific tag. The main components of a Ubisense systems are:

the tags to be tracked, the sensors, and the software platform. Ubisense does not require Line-

of-Sight due to the UWB technology.

The signal is filtered and multipath effect reduced .This is huge advantage because the

multipath effect represents the main cause for low accuracy indoors. The environment is

organized into cells with at least four sensors/readers.

A problem of Ubisense is the timing cable required for every Ubitag which can become

challenging in some environments.

Ubisense can determine the location of the Ubitags with very high accuracy. However the

price to deploy such a system is high, about $16.875 for the research package.

9.3.4 Applying Radio Frequency base system

Radio frequency based system is popular through the ages. It is widely available across the

globe especially in most households and in cars. FM radio uses the frequency-division

multiple access (FDMA) approach which splits the band into a number of separate frequency

channels that are used by stations. FM band ranges and channel separation distances vary in

different regions .There are only a few works dedicated to FM radio based positioning.

Recently presented Research that worked on indoor positioning using FM radio signals.

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9.3.4.1 Indoor positioning system using RFID

RFID is a means of storing and retrieving data through electromagnetic

Transmission to an RF compatible integrated circuit and is now being seen as a means of

enhancing data handling processes.

Hardware

RFID system has several basic components including a number of RFID readers and RFID

tags.

1. RFID tags

Active RFID tags are small transceivers, which can actively transmit their ID (or other

additional data) in reply to an interrogation

Figure 9-11 RFID

There are two type of tags can used

active tags

passive tags

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Passive RFID tags operate without a battery. They are mainly used to replace the traditional

barcode technology and are much lighter, smaller in volume, and less expensive than active

tags. They reflect the RF signal transmitted to them from a reader and add information by

modulating the reflected signal.

2. RFID readers

Figure 9-12 RFID reader

The RFID reader is able to read the data emitted from RFID tags. RFID readers and tags use

a defined RF and protocol to transmit and receive data.

3. Arduino yun as a microcontroller

Figure 9-13 Arduino yun

122

RFID system explanation

Figure 9-14 Layout of the 3 by 3 Grid RFID Positioning System

As when an object is placed inside one of the cells within the selected experimental area,

the 9‐Cell RFID Positioning System will notify the RFID readers and then record the RSSI of

the object picked up by the four readers. Each cell has a fixed set of four average values of

RSSI. The system will then calculate the Euclidean distance of that object to the readers. The

cell that has the smallest Euclidean distance would mean that it is nearest to the current

position of the detected object.

Problems of RFID system implementation

To cover large area need to large number of tags

The reader needs to be at a small distance from the tag

9.3.4.2 Indoor positioning system using ZigBee

The ZigBee technology is an emerging wireless technology standard which provides solution

for short and medium range communications due to its numerous benefits. It is mainly

designed for applications which require low-power consumption but do not require large data

throughput. The signal range coverage of a ZigBee in indoor environments is typically 20m

to 30 m. Distance calculation between two ZigBee nodes is usually carried out from RSSI

values. ZigBee

is open to interference from a wide range of signal types using the same frequency which can

disrupt radio communication because it operates in the unlicensed ISM bands.

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Hardware

Figure 9-15 Zigbee modules

As shown in figure we used four xbee pro module

Three xbee modules are used as a receiver.

One xbee module is used as a transmitter

Arduino yun

ZigBee system explanation

Network of wireless sensors, based on sensor nodes with XBee communication interfaces.

Each sensor node is placed in a well determined geographical position. The sensor of the

target is composed of an XBee pro connected to an Arduino yun platform and a battery pack.

This unit can be installed, for instance,

Within a safety helmet of a worker. Antenna orientation, the presence of walls and metal

objects are among many factors that affect the localization Accuracy when measuring RSSI.

As consequence, the obtained distance might be unreliable.

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Connected Car

Chapter 10

125

10.1 Connected car

A connected car is a car that provide Internet access, and also with a wireless LAN.

Often, the car is also equipped with special technologies that tap into the internet or wireless

LAN and provide additional benefits to the driver This makes the car able to share internet

access with other devices both inside as well as outside the vehicle Concierge features

provided by automakers or apps alert the driver of the time to leave to arrive on time from a

calendar and send text message alerts to friends or business associates to alert them such as

BMW Connected NA that also helps find parking or gas stations. Examples include:

automatic notification of crashes and notification of speeding.

The car today has the computing power of 20 personal computers, features about 100 million lines

of programming code, and processes up to 25 gigabytes of data an hour. Yet while automotive

digital technology has traditionally focused on optimizing the vehicle’s internal functions, attention

is now turning to developing the car’s ability to connect with the outside world and enhance the in-

car experience. This is the connected car—a vehicle able to optimize its own operation and

maintenance as well as the convenience and comfort of passengers using onboard sensors and

Internet connectivity.

10.2 Function description

The aim of this function is:

Take a picture using a webcam connected to the Arduino Yun

Upload the this image through wifi to server (dropbox) using Python

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10.3 Implementation tools

10.3.1 Arduino YUN

The Yún distinguishes itself from other Arduino boards in that it can communicate with the

Linux distribution onboard, offering a powerful networked computer with the ease of

Arduino. In addition to Linux commands like cURL, you can write your own shell and

python scripts for robust interactions.

The board has built-in Ethernet and WiFi support so no need to use external WiFi shield.

Figure 10-1 Arduino Yun WiFi connection

10.3.2 Web cam (Logitech HD Webcam C270):

HD video quality, at a resolution of 1280 x 720 pixels.

HD video recording, at resolutions of up to 720p.

Photos can be taken at a maximum resolution of 2048 x 1536 pixels, in a 4:3 format.

Pan, tilt and zoom controls.

Face tracking.

Motion detection.

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10.4 Steps of implementation

Insert SD card into the Arduino .

Install open-wrt OS .

Plug the web cam into the Arduino usb slot.

Install fswebcam, “a simple webcam app.

Create Dropbox API app.

Install pip, a Python package manager (Dropbox has an easy to use Python

SDK).

From the command line on shell environment

Upload a file to Dropbox from the Arduino Yun in Python .

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10.5 Result:

Figure 10-2 is showing the uploaded image on dropbox .

Figure 10-2 uploaded image on dropbox

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Conclusion

Chapter 11

130

The car model developed throughout the duration of this project proved to be a resounding

success. The vehicle is able to hover in its own right and receive and execute commands as

required.

As mentioned in the introduction we had six specific features needed to be fulfilled at the end

of the project duration, and although we faced a lot of obstacles, problems and challenges, we

successfully achieved them and during this period we thought about more but due to lack of

time, we could not start implementing them.

Achieved features:-

1- Adaptive cruise control

Using ultrasonic sensor and PID controller helped in achieving this feature. The car model

succeeded in changing its speed according to the target vehicle.

2- Lane-keeping

Using the Infrared sensors, we were able to detect the lane markings and any sudden

deviation in the planned path. According to this, the car takes the action to return back to its

lane safely.

3- Lane departure

By using Ultrasonic and Infrared sensors, the car was able to take the decision of changing

lanes when there is an object remaining in front of it for a period of time.

4- Auto-parking

By the use of number of ultrasonic sensors, the aim of this function is achieved which was

moving a car from a traffic lane into a parking spot.

5- Indoor positioning

There were available methods and trails to achieve our goals by using X Bee and RFID

6- Security camera

Implementing this function is achieved correctly by using a webcam such that the taken

image is uploaded through WiFi to server (drop box). This feature mainly provides security

to the car, to avoid being stolen or something like that.

Last but not least, Our project can be considered just a step to reach fully autonomous cars

and in order to do so more features need to be implemented also cars need to be connected

together through wireless links and clouds so they can start communication with each other

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Reference

Chapter 12

132

11.1 References

Chapter 1

1. The Pathway to Driverless Cars. Claire Perry

2. wordpress.com

3. theguardian.com

Chapter 2

1- store.fut-electronics.com/

2- Graduation Project Autonomous Driving System

Chapter 3

1- Store.Fut-Electronics.Com/

2- Homeudino Graduation Project

Chapter 4

1- Chassis Systems Control Adaptive Cruise Control: More comfortable driving

2- Adaptive Cruise Control and Driver Modeling/Johan Bengtsson/Department of

Automatic Control/ Lund Institute of Technology

3- Electronics|projects|focus/The Working Principle of a PID Controller

4- Playground.arduino.cc/Code/PIDLibrary

5- Adaptive Cruise Control Systems for Vehicle Modeling Using Stop and Go

Manoeuvres V. V Sivaji1, Dr. M. Sailaja

6- DRIVING WITH ADAPTIVE CRUISE CONTROL IN THE REAL WORLD ir.

H.M. Jagtman & drs E. Wiersma

7- Adaptive Cruise Control System Overview/5th Meeting of the U.S. Software System

Safety Working Group

8- Modern Control Engineering Fifth Edition/Katsuhiko Ogata

9- PID Tuning Methods An Automatic PID Tuning Study with MathCad Neil

Kuyvenhoven Calvin College ENGR. 315 December 19, 2002

10- www.studymafia.org/ A Seminar report On ADAPTIVE CRUISE CONTROL

Chapter 5

1- Design of an Automotive Lane Keeping System Based on the Structure of Electric

Power Steering. Jing-Fu Liu, Tsung-Hsien Hu, Tsung-Hua Hsu.

2- www.unece.org/fileadmin/DAM/trans/doc/2004/itc/itcrt/LKAS_presen_040218_final.

pdf

3- techinfo.subaru.com/proxy/98666/pdf/ownerManual/098666_2016_WRX/MSA5M16

15ASTISV1_6.pdf

133

4- A LANE KEEPING ASSIST SYSTEM FOR PASSENGER CARS, DESIGN

ASPECTS OF THE USER INTERFACE. Jochen Pohl, Jonas Ekmark.

5- www.arduino.cc/en/Reference/Interrupts

Chapter 6

1- http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.331.2840&rep=rep1&type=

pdf

2- http://www.psych.utah.edu/AppliedCognitionLab/LC.pdf

3- http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.259.4263&rep=rep1&type=

pdf

4- https://its.mit.edu/sites/default/files/documents/DRIVIN.PDF

Chapter 8

1. Raspberry PI L298N Dual H Bridge DC Motor-Newsletter © 2016 Autodesk, Inc.

2. Adafruit's Raspberry Pi Lesson 9. Controlling a DC Motor Created by Simon Monk

3. ModMyPi | HCSR04 Ultrasonic Range Sensor on the Raspberry Pi

4. STEP BY STEP GUIDE TO REVERSE PARKING reference

5. Moore, Matthew (2009-12-20). "Women worse at parking than men, study shows".

London: telegraph.co.uk. Retrieved 2010-01-02.

6. © 1997-2016 DriversEd.com Home / Driving Information / Driving Techniques /

Perpendicular Parking

7. Parking Network Webinars reference

8. Driving Tips.ORG Reference

Chapter 9

1- Electrical engineer.com

2- Wireless indoor positioning techniques Rilind Ballazhi _Supervisor: Prof.

Dr.Károly Farkas.

3- Survey of Wireless Indoor Positioning Techniques and Systems Hui Liu, Student

Member, IEEE, Houshang Darabi, Member, IEEE, Pat Banerjee, and Jing Liu

4- Recent Advances in Wireless Indoor Localization Techniques and System. Zahid

Farid, Rosdiadee Nordin, andMahamod Ismail

5- The Study on Using Passive RFID Tags for Indoor Positioning. S.L. Ting, S.K.

Kwok, Albert H.C. Tsang and George T.S. Ho

6- An Integrated Xbee Arduino with Group Search Optimizer Approach for

Localization in Wireless Sensor NetworksR. Harikrishnan1* and V. Jawahar

Senthil Kumar2

7- Localization in a Wireless Sensor Network based on RSSI and a decision tree.

Amine CHERIET, Mohamed OUSLIM, Kamel AIZI

134

8- Condell J.A. Survey of active and passive indoor localization system

Chapter 10

1- arduino.cc/en/Main/ArduinoBoardYun

2- twilio.com