A Smart-Dashboard Muhammad Akhlaq Augmenting safe & smooth driving Master Thesis

A Smart-Dashboard Muhammad Akhlaq Augmenting safe & smooth driving Master Thesis

Master Thesis

Computer Science

Thesis no: 2010:MUC:01

Month Year 02-10

A Smart-Dashboard

Augmenting safe & smooth driving

Muhammad Akhlaq

School of Computing

Blekinge Institute of Technology

Box 520

SE – 372 25 Ronneby

Sweden

This thesis is submitted to the School of Computing at Blekinge Institute of Technology in partial fulfillment of the requirements for the degree of Master of Science in Computer Science

(Ubiquitous Computing). The thesis is equivalent to 20 weeks of full time studies.

Contact Information:

Author(s):

Muhammad Akhlaq

Address: Mohallah Kot Ahmad Shah, Mandi Bahauddin, PAKISTAN-50400

E-mail: [email protected]

University advisor(s):

Prof. Dr. Bo Helgeson

School of Computing

School of Computing

Blekinge Institute of Technology

Box 520

SE – 372 25 Ronneby

Sweden www.bth.se/com

Phone : +46 457 38 50 00

Fax : + 46 457 102 45

ii

A

BSTRACT

Annually, road accidents cause more than 1.2 million deaths, 50 million injuries, and US$ 518 billion of economic cost globally [1]. About 90% of the accidents occur due to human errors [2] [3] such as bad awareness, distraction, drowsiness, low training, fatigue etc. These human errors can be minimized by using advanced driver assistance system (ADAS) which actively monitors the driving environment and alerts a driver to the forthcoming danger, for example adaptive cruise control, blind spot detection, parking assistance, forward collision warning, lane departure warning, driver drowsiness detection, and traffic sign recognition etc. Unfortunately, these systems are provided only with modern luxury cars because they are very expensive due to numerous sensors employed. Therefore, camera-based ADAS are being seen as an alternative because a camera has much lower cost, higher availability, can be used for multiple applications and ability to integrate with other systems.

Aiming at developing a camera-based ADAS, we have performed an ethnographic study of drivers in order to find what information about the surroundings could be helpful for drivers to avoid accidents. Our study shows that information on speed, distance, relative position, direction, and size & type of the nearby vehicles & other objects would be useful for drivers, and sufficient for implementing most of the ADAS functions. After considering available technologies such as radar, sonar, lidar, GPS, and video-based analysis, we conclude that video-based analysis is the fittest technology that provides all the essential support required for implementing ADAS functions at very low cost.

Finally, we have proposed a Smart-Dashboard system that puts technologies – such as camera, digital image processor, and thin display – into a smart system to offer all advanced driver assistance functions. A basic prototype, demonstrating three functions only, is implemented in order to show that a full-fledged camera-based

ADAS can be implemented using MATLAB.

Keywords: Ubiquitous Computing, Smart Systems, Context-Awareness, Ethnography,

Advanced Driver Assistance System (ADAS), Middleware, Driver-Centered Design,

Image Sensors, Video-Based Analysis, Bird’s-Eye View.

A

CKNOWLEDGEMENTS

First, I would like to thank my adviser Prof. Dr. Bo Helgeson at Blekinge Institute of Technology for his invaluable advice during the course of this thesis.

Second, I would like to thank Dr. Hans Tap and Dr. Marcus Sanchez Svensson – the former program mangers for Master in Ubiquitous Computing. Their continuous administrative support made it possible for me to complete this thesis.

Third, a special thank to my father Gulzar Ahmad, my mother Zainab Bibi, and my wife Sadia Bashir for their prayers and encouragement.

Muhammad Akhlaq,

Ronneby, 2009. ii

C

ONTENTS

1

 

INTRODUCTION ........................................................................................................................... 3

 

1.1

1.2

1.3

 

 

B

C

ACKGROUND

HALLENGES

........................................................................................................................... 3

............................................................................................................................ 3

 

R

ESEARCH

Q

UESTIONS

............................................................................................................. 4

 

 

 

1.4

 

S

MART

S

YSTEMS

....................................................................................................................... 5

 

1.4.1

 

Context-awareness............................................................................................................... 5

 

1.4.2

 

Intelligence .......................................................................................................................... 5

 

1.4.3

 

Pro-activity .......................................................................................................................... 5

 

1.4.4

 

Minimal User Interruption .................................................................................................. 6

 

1.5

 

R

ELATED

S

TUDIES

/ P

ROJECTS

................................................................................................. 6

 

1.5.1

 

Advanced Driver Assistance Systems (ADAS) .................................................................... 6

 

1.5.2

 

In-Vehicle Information Systems (IVIS) ............................................................................... 8

 

1.5.3

 

Warning Systems .................................................................................................................. 8

 

1.5.4

 

Navigation and Guidance Systems ...................................................................................... 9

 

1.5.5

 

Mountable Devices and Displays ........................................................................................ 9

 

1.6

1.5.6

 

Vision-based integration of ADAS .................................................................................... 10

 

 

A

NALYSIS OF THE

R

ELATED

P

ROJECTS

.................................................................................. 10

 

2

 

BASICS OF UBIQUITOUS COMPUTING .............................................................................. 11

 

2.1

 

W

HAT IS

U

BIQUITOUS

& P

ERVASIVE

C

OMPUTING

? ............................................................... 11

 

2.1.1

 

Ubiquitous vs. Pervasive Computing ................................................................................ 12

 

2.1.2

 

Related Fields .................................................................................................................... 13

 

2.1.3

 

Issues and Challenges in UbiComp .................................................................................. 13

 

2.2

 

D

ESIGNING FOR

U

BI

C

OMP

S

YSTEMS

...................................................................................... 16

 

2.2.1

 

Background ........................................................................................................................ 16

 

2.2.2

 

Design Models ................................................................................................................... 17

 

2.3

2.2.3

 

Interaction Design ............................................................................................................. 20

 

 

I

SSUES IN

U

BI

C

OMP

D

ESIGN

................................................................................................... 22

 

2.3.1

 

What and When to Design? ............................................................................................... 22

 

2.3.2

 

Targets of the Design ......................................................................................................... 22

 

2.3.3

 

Designing for Specific Settings – Driving Environment ................................................... 22

 

2.3.4

 

UbiComp and the Notion of Invisibility ............................................................................ 23

 

2.3.5

 

Calm Technology ............................................................................................................... 23

 

2.3.6

 

Embodied Interaction ........................................................................................................ 23

 

2.3.7

 

Limitations of Ethnography ............................................................................................... 24

 

2.3.8

 

Prototyping ........................................................................................................................ 24

 

2.3.9

 

Socio-Technical Gap ......................................................................................................... 24

 

2.4

2.5

2.3.10

 

 

C

 

U

BI

C

Hacking ......................................................................................................................... 24

OMP AND

S

MART

ONCLUSIONS AND

F

-D

ASHBOARD

UTURE

D

P

ROJECT

IRECTIONS

....................................................................... 24

.............................................................................. 25

 

 

 

3

 

ETHNOGRAPHIC STUDIES ..................................................................................................... 26

 

3.1

3.2

3.3

 

 

I

NTRODUCTION

O

UR

A

....................................................................................................................... 26

PPROACH

...................................................................................................................... 28

 

R

ESULTS

.................................................................................................................................. 29

 

 

 

3.3.1

 

Results from Ethnography ................................................................................................. 29

 

3.3.2

 

Video Results ..................................................................................................................... 30

 

3.4

3.3.3

 

Results from Questionnaire ............................................................................................... 32

 

 

C

ONCLUSIONS

......................................................................................................................... 34

 

4

 

GENERAL CONCEPT DEVELOPMENT ............................................................................... 35

 

4.1

 

N

EED FOR BETTER SITUATION AWARENESS

............................................................................ 35

 

4.1.1

 

Improving Context-awareness ........................................................................................... 35

 

4.1.2

 

Detecting Blind-spots ........................................................................................................ 36

 

4.1.3

 

Enhancing Object-Recognition ......................................................................................... 36

  iii

4.2

 

N

EED FOR AN UNOBTRUSIVE SYSTEM

..................................................................................... 36

 

4.3

4.4

 

N

EED FOR AN EASY USER INTERACTION

................................................................................. 37

 

 

C

ONCLUSIONS

......................................................................................................................... 37

 

5

 

TECHNOLOGIES ........................................................................................................................ 38

 

5.1

5.2

 

R

ADAR

.................................................................................................................................... 38

 

 

S

ONAR

..................................................................................................................................... 39

 

5.3

5.4

5.5

 

 

L

IDAR

...................................................................................................................................... 40

GPS ......................................................................................................................................... 41

 

V

IDEO

-B

ASED

A

NALYSIS

....................................................................................................... 42

 

 

 

5.5.1

 

CCD/CMOS Camera ......................................................................................................... 42

 

5.5.2

 

Working Principles ............................................................................................................ 44

 

5.5.3

 

Object Recognition (size & type) ...................................................................................... 46

 

5.5.4

 

Road Sign Recognition ...................................................................................................... 47

 

5.5.5

 

Lane Detection and Tracking ............................................................................................ 48

 

5.5.6

 

Distance Measurement ...................................................................................................... 49

 

5.5.7

 

Speed & Direction (Velocity) Measurement ..................................................................... 51

 

5.5.8

 

Drowsiness Detection ........................................................................................................ 52

 

5.5.9

 

Environment Reconstruction ............................................................................................. 52

 

5.5.10

 

Pros and Cons ............................................................................................................... 53

 

5.6

 

C

ONCLUSIONS

......................................................................................................................... 53

 

6

 

THE SYSTEM DESIGN .............................................................................................................. 54

 

6.1

6.2

 

I

NTRODUCTION

....................................................................................................................... 54

 

 

C

OMPONENTS OF THE

S

YSTEM

............................................................................................... 54

 

6.2.1

 

Hardware (Physical Layer) ............................................................................................... 55

 

6.2.2

 

Middleware ........................................................................................................................ 56

 

6.3

6.2.3

 

Applications ....................................................................................................................... 57

 

 

D

ESIGN

C

ONSIDERATIONS

...................................................................................................... 57

 

6.3.1

 

Information Requirements ................................................................................................. 57

 

6.3.2

 

Camera Positions .............................................................................................................. 57

 

6.3.3

 

Issuing an Alert .................................................................................................................. 57

 

6.3.4

 

User Interface .................................................................................................................... 58

 

6.3.5

 

Human-Machine Interaction ............................................................................................. 58

 

6.4

 

S

YSTEM

D

ESIGN

...................................................................................................................... 59

 

6.4.1

 

Adaptive Cruise Control (ACC) ........................................................................................ 60

 

6.4.2

 

Intelligent Speed Adaptation/Advice (ISA) ....................................................................... 62

 

6.4.3

 

Forward Collision Warning (FCW) or Collision Avoidance ........................................... 62

 

6.4.4

 

Lane Departure Warning (LDW) ...................................................................................... 63

 

6.4.5

 

Adaptive Light Control ...................................................................................................... 64

 

6.4.6

 

Parking Assistance ............................................................................................................ 65

 

6.4.7

 

Traffic Sign Recognition .................................................................................................... 65

 

6.4.8

 

Blind Spot Detection .......................................................................................................... 66

 

6.4.9

 

Driver Drowsiness Detection ............................................................................................ 67

 

6.4.10

 

Pedestrian Detection ..................................................................................................... 67

 

6.4.11

 

Night Vision ................................................................................................................... 68

 

6.5

6.4.12

 

Environment Reconstruction ......................................................................................... 69

 

 

I

MPLEMENTATION

................................................................................................................... 69

 

6.6

 

C

ONCLUSIONS

......................................................................................................................... 70

 

7

 

CONCLUSIONS ........................................................................................................................... 71

 

7.1.1

 

Strengths ............................................................................................................................ 71

 

7.1.2

 

Weaknesses ........................................................................................................................ 73

 

7.1.3

 

Future Enhancements ........................................................................................................ 74

 

APPENDIX A ......................................................................................................................................... 75

 

A1 – Q

UESTIONNAIRE

.......................................................................................................................... 75

A2 – R

ESPONSE

S

UMMARY

R

EPORT

.................................................................................................... 78

 

 

BIBLIOGRAPHY .................................................................................................................................. 83

  iv

L

IST OF

F

IGURES

Figure 2.1: Publicness Spectrum and the Aspects of Pervasive Systems [90] ....................... 12 

Figure 2.2: Classification of computing by Mobility & Embeddedness [95] ......................... 13 

Figure 2.3: The iterative approach of designing UbiComp systems [130]. ............................ 18 

Figure 3.1: Blind spots on both sides of a vehicle .................................................................. 31 

Figure 5.1: An example of in-phase & out-of-phase waves ................................................... 38 

Figure 5.2: Principle of pulse radar ........................................................................................ 38 

Figure 5.3: A special case where radar is unable to find the correct target [194]................... 39 

Figure 5.4: Principle of active sonar ....................................................................................... 40 

Figure 5.5: Principle of Lateration in 2D ................................................................................ 41 

Figure 5.6: Some examples of image sensors and cameras .................................................... 42 

Figure 5.7: Image processing in CCD [192] ........................................................................... 43 

Figure 5.8: Image processing in CMOS [192] ........................................................................ 43 

Figure 5.9: Camera-lens parameters ....................................................................................... 45 

Figure 5.10: Imaging geometry for distance calculation [202] .............................................. 49 

Figure 5.11: Distance estimation model [231]........................................................................ 50 

Figure 5.12: Radar capable CMOS imager chip by Canesta .................................................. 51 

Figure 5.13: Distance estimation using smearing effect [296] ............................................... 52 

Figure 6.1: Layered architecture of context-aware systems [315] .......................................... 54 

Figure 6.2: Smart-Dashboard system with five cameras ........................................................ 55 

Figure 6.3: Preferred places for a display ............................................................................... 56 

Figure 6.4: An integrated and adaptive interface of Smart-Dashboard .................................. 59 

Figure 6.5: Overview of the Smart-Dashboard system ........................................................... 60 

Figure 6.6: Adaptive Cruise Control system .......................................................................... 61 

Figure 6.7: Vehicle detection .................................................................................................. 61 

Figure 6.8: Intelligent Speed Adaptation system .................................................................... 62 

Figure 6.9: Forward Collision Warning system ...................................................................... 63 

Figure 6.10: Lane Departure Warning system ........................................................................ 64 

Figure 6.11: Adaptive Light Control system .......................................................................... 64 

Figure 6.12: Parking Assistance system ................................................................................. 65 

Figure 6.13: Traffic Sign Recognition system ........................................................................ 66 

Figure 6.14: Blind Spot Detection system .............................................................................. 66 

Figure 6.15: Driver Drowsiness Detection system ................................................................. 67 

Figure 6.16: Pedestrian Detection system .............................................................................. 68 

Figure 6.17: Night Vision system ........................................................................................... 68 

Figure 6.18: Environment Reconstruction system and the Display ........................................ 69 

Figure 6.19: Pedestrian Detection using built-in MATLAB model [317] .............................. 69 

Figure 6.20: Traffic Sign Recognition using built-in MATLAB model [317] ....................... 70 

Figure 6.21: Pedestrian Detection using built-in MATLAB model [317] .............................. 70 

Figure 7.1: Imaging without (a) & with (b, c) wide dynamic range (WDR) [316]. ............... 73  v

L

IST OF

T

ABLES

Table 2.1: Differences b/w Ubiquitous Computing & Pervasive Computing ........................ 12 

Table 2.2: Positivist approach Vs. Phenomenological approach ............................................ 17 

Table 5.1: Performance comparison of CCD and CMOS image sensors ............................... 44 

Table 5.2: A timeline for camera-based automotive applications by Mobileye.com ............. 53 

2

1 I

NTRODUCTION

Driving is a very common activity of our daily life. It is extremely enjoyable until we face a nasty situation, such as flat-tire, traffic-violation, congestion, need for parking, or an accident etc. However, accidents are the most vital situations and cause a great loss to human lives and assets. Most of the accidents occur due to human errors. A Smart-Dashboard could help avoid these unpleasant situations by providing relevant information for drivers in their car as and when needed. This would significantly reduce the level of frustrations, delays, financial losses, injuries, deaths etc caused by road-incidents.

1.1 Background

Annually, road accidents cause about 1.2 million deaths, over 50 million injuries, and global economic cost of over US$ 518 billion [1]. About 90% of the accidents happen due to the driver behavior [2] [3], such as bad awareness of driving environment, low training, distraction, work over-load or under-load, or low physical or physiological conditions etc. An advanced driver assistance system (ADAS) can play a positive role in improving driver awareness and hence performance by providing relevant information as and when needed.

New features are being introduced in vehicles daily to serve better the information needs of a driver. In the beginning, only luxury vehicles come with these new features due to their heavy cost. As time passes, these features become standard and start appearing in all types of vehicles. Some new features are now being introduced in ordinary vehicles from the very first day. These new features are based on innovative automotive sensors.

The automotive sensor market is growing rapidly. A large variety of automotive sensors or technologies are available which can provide data about car (such as fuellevel, temperature, tire-pressure, speed etc), weather, traffic, navigation, road signs, road surface, parking, route prediction, drivers’ vigilance, and situation awareness, etc.

Vehicles of this age combine a variety of sensor technologies to keep an eye on their environment. For example, a mid-range saloon may use about 50 sensors and a top class vehicle may use well over 100 sensors [69].

1.2 Challenges

In presence of variety of sensors technologies, system integration is a major concern of present developments. Although some latest developments already show improvements but a fully integrated system for driver-assistance is yet to come in few years. For example, the smart cars of future will come with many safety features integrated into a single system [4]. Even after full integration is achieved, system designers will have to solve a number of further issues, such as how to alert a driver to the forthcoming danger using either visual, audible or haptic warnings. The challenge is to avoid information overload when at a decisive time. Another issue is deciding

about the level of automation i.e. when should control be transferred from driver to the system. Additionally, our approach to interaction with automobiles is changing with the introduction of new technologies, information media, and human and environmental factors involved [5]. For example, auto-parking feature in latest BMW

3

cars require only a button pressed so that the car may find an available slot and automatically park itself into it.

Advanced driver assistance systems (ADAS) augment safe & smooth driving by actively monitoring the driving environment and producing a warning or taking over the control in highly dangerous situations. Most of the existing systems focus on only single useful service, such as parking assistance, forward collision warning, lane departure warning, adaptive cruise control, driver drowsiness detection, etc. Recently, many integrated ADAS have been proposed. These systems use a variety of sensors that makes them complex and costly. Any integrated ADAS [11] combines multiple services into a single system in an efficient and cost effective way.

Vision-based ADAS use cameras to provide multiple services for driver assistance.

They are becoming popular because of their low-cost and independence from infrastructure outside the vehicle. For example, an intelligent and integrated ADAS

[11] uses only 2 cameras and 8 sonars, and others make use of only cameras [71] [72]

[73] [74] [75] [76] [84]. They present information through an in-vehicle display.

Specialized devices are being introduced which can efficiently process visual data

[77] [78]. For better situation awareness for drivers, different systems have been introduced to display the surrounding environment of the vehicle [79] [80] [81] [82] [83].

These recent developments show that the future lies in vision-based integrated

ADAS. Advantages of vision based integrated systems include: their cost is lower; their performance is improving; they support innovative features; they can be used with new as well as old vehicles having no support for infrastructure; and they are easy to develop, install and maintain. That is why they are getting much attention from researchers in academia and automotive industry. Current research mainly focuses on introducing traditional driver assistance systems based on camera, and then combining these individual systems into an integrated ADAS.

However, despite much advancement in ADAS systems, the issues of informationoverload for drivers have been overlooked and remained unsolved. Little attention is given to the interface and interaction design for vision-based ADAS. It is important to note that a driver can pay only a little attention to the displayed information while driving [15]. Therefore, the system should provide only relevant information, in a distraction-free way, as and when needed. There is a sever need to design and evaluate an in-vehicle display for vision based ADAS which would be distraction-free, contextaware, usable and easy to interact with for a driver. It would augment safe & smooth driving and help reducing losses caused by road-incidents.

1.3 Research Questions

In this thesis, we consider the following closely related research questions:

1. What information about the surroundings should be provided to the drivers for better situation awareness?

2. How should this information be presented to drivers in a distraction-free way?

3. How should drivers interact with the proposed system?

This thesis provides answer to these research questions. As a result of this thesis, we expect to come up with an innovative & usable design of an in-dash display for drivers, called as Smart-Dashboard.

4

1.4 Smart Systems

A smart system is a system that is able to analyze available data to produce meaningful and intelligent responses. They use sensors to monitor their environment and actuators to reflect changes to the environment. Smart systems can utilize available context information to develop meaningful responses using some kind of

Artificial Intelligence techniques. They have very useful applications in real life, ranging from smart things to smart spaces to smart world. For example, a smart car always monitors the driver for drowsiness and alerts the driver well in time.

Smart systems are essentially context-aware, intelligent, proactive and minimally intrusive. A brief description of these basic features is given below.

1.4.1 Context-awareness

A system is context-aware if it uses some or all of the relevant information to provide better service to its users, i.e. it can adapt to its changing context of use. A context-aware system is expected to be more user-friendly, less obtrusive, and more efficient [315]. A system that needs to be minimally distractive has to be contextaware because a context-aware system is sensitive & responsive to different settings in which it can be used [318] and hence requires very little input from the user. It needs to capture context information, model it, generate an adaptive response and store context information for possible future use.

Context-aware systems need to maintain historical context information for finding trends and predicting future values of context [6]. For example, we can predict future location of an automobile if we know few of its recent locations.

1.4.2 Intelligence

The low-level context provided by sensors is called as primary context [70]. From primary context data, we can infer related context, which is known as secondary context. We can combine several primary contexts to infer secondary contexts. For an example, we can infer that the user is sleeping at home if primary context data shows that the user is lying in a sofa or bed, lights are off, it is nighttime, there is silence, and there is no movement. It is however not the ultimate inference because the user may not be sleeping but just relaxing for a few minutes in sleeping position.

The process of inference and extraction is very complicated because there is no single possible inference for one set of primary contexts. We need intelligent methods for context extraction and inference in order to make context-aware applications truly unobtrusive and smart [7]. Another major issue is the performance & time-complexity of reasoning process in the presence of huge amount of context data at hand [8].

1.4.3 Pro-activity

Context-awareness makes it possible to meet or anticipate user needs in a better way. It is, however, very challenging to predict user behavior because humans have very complex motivations [9]. We need a very intelligent & trustable prediction technique in order to avoid problems for the user. Context-aware systems in future will serve as per users’ expectations to bear out a new acronym WYNIWYG – What You

Need Is What You Get [10].

5

1.4.4 Minimal User Interruption

As we know that human attention capability is very limited [15], we need smart systems to assure minimal user interruption. A smart system minimizes the annoyance by lowering the level of input required of the user. It also learns from experience and uses its learning to inform future decisions.

In this way, incorporating smartness in the dashboard will make it distraction-free, context-aware, usable and easy to interact with for a driver. This would augment safe

& smooth driving and help reducing losses caused by road-incidents.

1.5 Related Studies / Projects

Road safety is an important and well-researched issue. This area is so vital that many governmental bodies in developed countries have issued a set of requirements for systems regarding road-safety. For the last many decades, a large number of projects or studies have been undertaken under the flag of road-safety, intelligent transportation, IVIS (In-Vehicle Information System) and DSS (Driver Support

Systems) etc. There are hundreds of active projects in industry, universities, and research centers. Most of these projects concentrate on single aspect of the system, such as LDW (Lane Departure Warning), while others consider only a few aspects.

In this section, we describe some representative studies/projects, which are the most important and relevant to our thesis.

1.5.1 Advanced Driver Assistance Systems (ADAS)

Driver assistance systems support drivers in driving a vehicle safely & smoothly.

They provide drivers with an extra ease, decreased workload, and more focus on the road, and hence reduce the risk of accidents [85]. In this way, they increase road safety in general. They are also known as Driver Support Systems (DSS) etc. Examples of such systems include [11]:

• Adaptive Cruise Control (ACC)

• Forward Collision Warning (FCW)

• Lane Departure Warning (LDW)

• Adaptive Light Control (ALC)

• Vehicle-to-Vehicle communication (V2V)

• Car data acquisition/presentation (e.g. fuel-level, temperature, tire-pressure, speed)

• Automatic parking or parking assistance

• Traffic Sign Recognition (TSR)

• Blind Spot Detection (BSD)

• Driver Drowsiness Detection (DDD)

• In-vehicle navigation system

• Intelligent Speed Adaptation/Advice (ISA)

• Night vision and augmented reality

• Rear view or the side view

• Object recognition (e.g. vehicle, obstacles and pedestrian)

• Etc…

6

Intelligent Car Initiative project (i2010) [12] [13] and Intelligent Vehicle Initiative

(IVI) [29] are the two famous examples of large projects covering many of these features.

1.5.1.1 Intelligent Car Initiative (i2010)

Intelligent Car Initiative project [12] [13] is funded by European Commission. The objective of this project is to encourage smart, safe and green system for transportation. It also promotes cooperative research in intelligent vehicle systems and assists in adopting research results. Many sub-projects are funded under this initiative, such as AWAKE, AIDE, PReVENT and eSafety etc.

The AWAKE project (2000-2004) [14] gives an integrated system for driver fatigue monitoring (sleepiness, inattention, stress, etc.). It set-up a multi-sensor system which fuses information provides by a number of automotive sensors, such as eyelid sensor, gaze sensor, steering grip sensor, and additional information, such as wheel speed and steering wheel movements etc. Other similar EU-funded projects include

SENSATION [16], DETER-EU [17], PROCHIP/PROMETHEUS program [18] and

SAVE-project [19].

AIDE (2004-to date) [20] is an acronym for adaptive integrated driver-vehicle interface. The main objective of AIDE project are to maximize the efficiency of

ADAS, to minimize the level of distraction and workload enforced by IVIS, and to facilitate mobility & comfort by using new technologies and devices. AIDE aims at developing a special dashboard computer to display important information for drivers but it does not explain how the driver is required to process all the displayed information.

PReVENT (2004-2008) [21] is one of the major initiatives on road safety which spent €55 million for four years. It aimed at developing and demonstrating preventive safety systems for European roads, and creating awareness of preventive/active safety in people. eSafety [22] aims at reducing the number of road accidents in Europe by bringing

Intelligent Vehicle Safety Systems that use ICT (information & communication technologies) to market. A similar recent project is the Safety In Motion (SIM) [23], which targets motorcycle safety.

Some other relevant projects financed by EU/EC include ADASE (Advanced

Driver Assistance Systems in Europe) [24], APROSYS (Advanced Protection

Systems) [25], EASIS (Electronic Architecture Safety Systems) [26], GST (Global

System for Telematics) [27], HUMANIST (HUMAN-centred design for Information

Society Technologies) [28], and the SENECa [55] which proves usability of speechbased user interfaces in vehicles.

1.5.1.2 Intelligent Vehicle Initiative (IVI)

Intelligent Vehicle Initiative (IVI) [29] was funded by U.S. Department of

Transportation (1997-2005). It aimed at preventing driver distraction, introduction of crash avoidance systems, and studying the effects of in-vehicle technologies on driver performance.

7

1.5.2 In-Vehicle Information Systems (IVIS)

IVIS are also known as Driver Information Systems (DIS). An IVIS combines many systems, such as communication, navigation, entertainment, climate control etc into a single integrated system. They use LCD panel mounted on dashboard, a controller knob, and optionally voice recognition. IVIS can be found in almost all the latest luxury vehicles, such as Audi, BMW, Hyundai, Mercedes, Peugeot, Volvo,

Toyota and Mitsubishi etc.

One of the earliest researches in this area was sponsored by US Department of

Transportation, Federal Highway Administration in 1997. The goal of their In-Vehicle

Information Systems (IVIS) project [30] was to develop a fully integrated IVIS that would safely manage highway & vehicle information, and provide integrated interface to the devices in the driving environment. The implementation was done on personal computers connected via Ethernet LAN. However, it came up with useful results.

Similarly, HASTE [57] is a recent EU funded project that provides guidelines and tests the fitness of three possible environments (lab, simulator and vehicle) for studying the effects of IVIS on driving performance.

An IVIS can also make use of guidance & traffic information produced by the systems that are managed by the city administration in developed countries. Examples of such systems include Tallahassee Driver Information System [31], and California

Advanced Driver Information System (CADIS) [32] [33].

1.5.3 Warning Systems

Recently, a number of in-vehicle systems have been developed that either alert the driver of the forthcoming danger or try to improve his behavior. Such systems can be considered as a sub-set of IVIS/ADAS because they handle only one or few features.

In this section, we will briefly survey some of the prominent warning systems.

Night Vision Systems [34] use Head-up Display (HUD) to mark an object which is outside the field of vision of a driver. The mark on HUD follows the object until the point of danger is passed. A driver can easily know about the speed, direction and distance of the object. The next generation systems will also be able to recognize objects actively.

Dynamic Speedometer [35] addresses the problem of over-speeding. It actively considers current speed limit information and redraws a dynamic speedometer on dashboard display in red. Other similar projects include Speed Monitoring Awareness and Radar Trailer (SMART) [36] which displays the vehicle speed and the current speed limit, Behavior-Based Safety (BBS) [37] which displays the driver performance regarding speed, and the Intelligent Speed Adaptation project (ISA) [38] which displays current speed limit.

Road Surface Monitoring systems detect and display the surface condition of the road ahead. This is relatively new area of research in ADAS. A recent project, Pothole

Patrol (P

2

) [39] uses GPS and other sources to report path-holes on the route. Other examples include CarTel [40], and TrafficSense [41] by Microsoft Research.

Safe Speed And Safe Distance (SASPENCE) [42] aims at avoiding accidents due to speed and distance problems. This project was carried out in Sweden and Spain in the year 2004. It suggests visual, auditory & haptic feedback, and provides alternatives to develop a DSS for safe speed and safe distance. Similarly, Green Light for Life [54]

8

uses an In-Vehicle Data Recorder (IVDR) system to promote safe driving in young drivers. It uses messages, reports and an in-vehicle display unit to provide feedback to the young drivers.

Monitoring the driver vigilance or alertness is another important thing for road safety. A recent prototype system for Monitoring Driver Vigilance [43] uses computer vision (IR illuminator and software implementations) to find level of vigilance.

Automotive industry uses some other methods for monitoring driver vigilance. For example, Toyota uses steering wheel sensors and a pulse sensor [44], Mitsubishi uses steering wheel sensors and measures of vehicle behavior [44], Daimler Chrysler uses vehicle speed, steering angle, and vehicle position using a camera [46], and IBM’s smart dashboard analyzes speech for signs of drowsiness [47].

In-Vehicle Signing Systems (IVSS) may read the road signs and display them inside the vehicle for driver attention. A recent example of such systems is the one prototyped by National Information and Communications Technology Australia

(NICTA) & Australian National University [48]. The IVSS may possibly use one of the following three techniques: 1) image-processing or computer-vision [48] [49] [50],

2) digital road-data [51] [52], and 3) DSRC (Dedicated Short Range Communications)

[53].

Safe Tunnel project [56] simulates tunnel driving and recommends the uses highly informative display to inform drivers of the incidents. A highly informative display might increase the threat of distraction but it might significantly improve safety.

Recently, some warning systems have been developed which use existing infrastructure, such as GSM, GPS, and sensors deployed in the road, cars or the networks. Examples include NOTICE [58] that proposes architecture for the warning on traffic incidents, Co-Driver Alert [59] that provides hazard information, and

Driving Guidance System (DGS) [60] that provides information about weather and speed etc.

1.5.4 Navigation and Guidance Systems

Route guidance and navigation systems are perhaps the oldest and most commonly provided feature in luxury cars. They use interactive displays and speech technologies.

There exist hundreds of such systems or projects. Examples include systems accessible in US, such as TravTek, UMTRI, OmniTRACS, Navmate, TravelPilot, Crew Station

Research and Development Facility, and Army Helicopter Mission Replanning System etc [61].

On the other hand, parking guidance or automatic parking is very new area of research. Advanced Parking Guidance System (APGS) [62] lets a vehicle steer itself into a parking space. They use in-dash screen, button controls, camera and multiple sensors, but need very little input from the driver. Toyota, BMW, Audi and Lexus are already using APGS in their luxury cars, and others are expected to use it soon.

1.5.5 Mountable Devices and Displays

Users with ordinary vehicles, not older than 1996, may apply mountable devices and displays to supplement IVIS. These devices can be connected to the diagnostic port located under the dashboard. They can collect useful data about the vehicle and display it for the driver, such as speed, engine RPM, oxygen sensors, fuel economy,

9

air-fuel ratio, battery voltage, error codes and so on. Examples of such devices include

DashDyno SPD [63], CarChip Fleet Pro [64], DriveRight [65], ScanGaugeII [66], and

PDA-Dyno [67].

Virtual Dashboard [68] is an important device developed by Toshiba. It is perhaps the most promising solution for information needs and infotainment. It consists of a real-time display controller (TX4961) and a dashboard display. Virtual Dashboard can handle all the information according to the current context. It can change the display to show a speedometer, tachometer, rear-view, navigation maps, speed or fuel-level etc.

1.5.6 Vision-based integration of ADAS

As mentioned previously, vision-based integrated ADAS systems use cameras to provide multiple services for driver assistance. They are becoming very popular because of their low-cost and independence from infrastructure outside the vehicle.

For example, an intelligent and integrated ADAS [11] uses only 2 cameras and 8 sonars, and others make use of only cameras [71] [72] [73] [74] [75] [76] [84]. They present information through an in-vehicle display. Specialized devices are being introduced which can efficiently process visual data [77] [78]. For better situation awareness for drivers, different systems have been introduced to display the surrounding environment of the vehicle [79] [80] [81] [82] [83]. These recent developments show that the future lies in vision-based integrated ADAS. Current research mainly focuses on introducing traditional driver assistance systems based on camera, and then combining these individual systems into an integrated ADAS.

1.6 Analysis of the Related Projects

After a careful analysis of related projects described in the previous section (i.e. section 1.5), we find that the vision-based integrated ADAS [79] [80] [81] [82] [83],

AIDE [20] and Virtual Dashboard [68] are very close to our proposed project.

However, still leave a large number of research questions unanswered, for example:

1. Why to use single integrated display (multipurpose & adaptive) instead of several displays (one for each function)?

2. Where should we place this integrated display for best performance?

3. What level of details should be presented to the driver?

4. How the driver is required to process all the information displayed?

5. How to prioritize the information type to show?

6. How to alert the driver of the forthcoming danger using visual, auditory, and tactile warnings?

7. How to avoid information overload when at a decisive time?

8. When the control should be transferred from driver to the system for automatic execution of a function?

9. How to use history to make the system truly unobtrusive?

Based on this research gap, we formulate our three research questions (see section

1.3) which comprehensively cover all of the above issues. As a result of this thesis, we expect to come up with an innovative & usable design of Smart-Dashboard.

In the next chapter, we present the vision of ubiquitous computing (UbiComp) and a discussion of UbiComp systems design.

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2 B

ASICS OF

U

BIQUITOUS

C

OMPUTING

Computing began with mainframe-era where machines were really fixed. UNIX finger command was used to locate any machine. Then we saw portability-era where machines would move from place to place. The idea of profiles was introduced to serve the users in a better way. Recently, we have come across mobility-era where machines are being used while on the move. Mobile computers, such as PDA’s,

Ubiquitous Communicator terminals, cell phones, electronic tags, sensors and wearable computers are becoming popular [86]. The trends are very clear; computing is moving off the desktop; devices are becoming smaller in size but greater in number; and computation is moving from personal devices to the smaller devices deployed in our environment. Interaction with these embedded & mobile devices will become so normal activity that people will not even realize that they are using computers. This is an era of ubiquitous & pervasive computing where users can demand for services anywhere at any time, while they are on move [87].

2.1 What is Ubiquitous & Pervasive Computing?

Back in 1991, Mark Weiser [88], the father of Ubiquitous Computing (UbiComp), gave an idea of invisible computers, embedded in everyday objects replacing PCs. He emphasized the need for unifying computers and humans seamlessly in an environment rich with computing. In such environment, computers would be everywhere, vanished into the background, and serving the people without being noticed. Traditional computers are frustrating because of the information overload.

Ubiquitous computing can assist us in solving the issue of information overload, which would make “using a computer as refreshing as taking a walk in the woods” [88].

UbiComp brings computing into our environment to support everyday life activities. Computers are becoming small and more powerful. As described by Moore in 1960’s, number of transistors per chip and power of microprocessors doubles every

18 months [45]. At the same time, we have seen tremendous developments in sensor technologies. These sensors can sense our environment and correspond to the five senses (i.e. sound, sight, smell, taste & touch). We can embed these small sensors into the real life objects to make them smart. These smart objects will put ambient intelligence in every aspect of our life. In this way, computing will be everywhere to augment our daily life activities in homes, bathrooms, cars, classrooms, offices, shops, playgrounds, and public places etc. The enabling technologies for ubiquitous and pervasive applications are wireless networks and mobile devices.

National Institutes of Science & Technology (NIST), in 2001, defined pervasive computing as an emerging trend towards [89]:

• Numerous, casually accessible, often invisible computing devices

• Frequently mobile or imbedded in the environment

• Connected to an increasingly ubiquitous network structure

However, NIST definition attempts to give a generic explanation for the two distinct terms i.e. pervasive computing and ubiquitous computing.

Kostakos et al [90] describe features of ubiquitous & pervasive systems in urban environments based on location, technology, information and degree of publicness

(private, social, or public).

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Figure 2.1

: Publicness Spectrum and the Aspects of Pervasive Systems [90]

According to Figure 2.1, for example, park is a public place where video-wall can be used to display train-timetable; office is a social place where television can be used to display business strategies; bedroom is a private place where PDA can be used to see personal information. In this thesis, we take car as a social place where a Smart-

Dashboard will be used to display context information for drivers.

2.1.1 Ubiquitous vs. Pervasive Computing

Ubiquitous computing and pervasive computing are two different things but people are using these terms interchangeably nowadays. They seem like somewhat similar things but actually, they are not [91]. Table 2.1 gives an account of differences between ubiquitous computing and pervasive computing.

Table 2.1

: Differences b/w Ubiquitous Computing & Pervasive Computing

Ubiquitous Computing Pervasive Computing

Meanings

Devices involved

Computing everywhere

Computing devices embedded in the things we already use

Computing diffused throughout every part of environment

Small, easy-to-use, handheld devices

Purpose Computing in the background Accessing information on something like Embedded or invisible or transparent computing

Mobile computing feature High level of mobility and embeddedness

Low mobility but high level of embeddedness

Initiators Xerox PARC (Xerox Palo Alto

Research Center) [92]

IBM Pervasive Computing division

[93]

Example(s) Dangling String, dashboard, weather beacon, and Datafountain. [94]

Information access, pervasive devices, smart badges etc.

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We can classify computing on the bases of different features, such as mobility and embeddedness as shown in the figure 2.2 below.

Embeddedness

Pervasive Computing

High

Ubiquitous Computing

Low High

Traditional Computing

Low

Mobility

Mobile Computing

Figure 2.2

: Classification of computing by Mobility & Embeddedness [95]

It is clear from figure 2.2 that ubiquitous computing puts together pervasive computing functionality with high level of mobility. In this way, they are related to each other. Most of the researchers nowadays do not differentiate between ubiquitous computing and pervasive computing. That is why they use these two terms interchangeably without any concern. This point onwards, we will also use these two terms interchangeably.

2.1.2 Related Fields

Ubiquitous & Pervasive Computing is also referred as sentient computing, contextaware computing, invisible computing, transparent computing, everyday computing, embedded computing, and social computing [128]. Distributed Systems and Mobile

Computing are the predecessors of Ubiquitous & Pervasive Computing. They share a number of features, strengths, weaknesses and problems [96]. Other closely related fields of research are “augmented reality” [97], “tangible interfaces” [98], “wearable computers” [99], and “cooperative buildings” [100]. What these technologies have in common is that they move computing beyond the desktop and into the real world environment. The real world is complex and has dynamic context of use that does not follow any predefined sequence of actions. The main focal points of ubiquitous computing are:

1. To find mutual relationship between physical world and the activity, and

2. To make computation sensitive & responsive to its dynamic environment

Designing and development of ubiquitous systems require a broad set of skills, ranging from sensor-technologies, wireless communications, embedded systems, software agents and interaction design to computer science.

2.1.3 Issues and Challenges in UbiComp

When Mark Weiser [88] gave the vision of ubiquitous computing, he also identified some of the potential challenges in making it reality. In addition to these challenges, Satyanarayanan [96] and others [101] have identified a number of issues

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and challenges in pervasive computing. Here is a comprehensive, but not exhaustive, list of issues and challenges in ubiquitous and pervasive computing.

2.1.3.1 Invisibility

Invisibility requires that a system should behave as per user expectations, while considering individual user preferences, and maintain a balance between proactivity and transparency. Ubiquitous and pervasive systems need to offer right service at right time by anticipating user needs with minimal user interruption. Examples include sending a print command to the nearest printer and switching mobile phone to silent mode when user enters into a library. Applications need to adapt to the environment and available resources according to some “adaptation strategy”.

2.1.3.2 Scalability

Ubiquitous and pervasive systems need to be scalable. Scalability means enabling large-scale deployments and increasing the number of resources and users whenever needed.

2.1.3.3 Adaptation

Context-aware systems need sensors to read changes in the environment (hardware or software sensors). They can either poll sensors (periodically or selectively), or subscribe for any changes in context. They may use different polling rate for different contexts. For example, the location of a printer need not be checked as frequently as that of a person.

2.1.3.4 Effective Use of Smart Spaces

The smart-spaces bring real world and computing together by embedding devices into environment, for example, automatic adjustment of room temperature based on person’s profile.

2.1.3.5 Localized Scalability

The localized scalability can be attained by decreasing interactions between remote entities. The intensity of interaction with pervasive computing environment has to decrease when one moves away from it. Interactions between close entities are of more relevance.

2.1.3.6 Heterogeneity and Masking Uneven Conditioning

In ubiquitous computing environment, the mobile clients are usually thin, less powerful and have limited battery capacity. Some neighboring infrastructure may have very powerful computing facilities. Similarly, some environments may be equipped with better computing facilities than others. We need to fill these differences in smartness of environments by utilizing, for example, personal computing space. This requires “cyber foraging”, which means proactively detecting possible surrogates,

14

negotiating quality of service, and then moving some of the computation tasks to these surrogates. A very intelligent tracking of “user intent” is needed.

2.1.3.7 Seamless Integration of Technologies

A number of technologies are available for developing ubiquitous & pervasive systems. We may need to use several technologies in our system. For example, we may use RFID, biometrics and computer vision in a single system. Their variant features make one technology more appropriate for one kind of environment when compared to others. Therefore, existence of various technologies in a pervasive environment is inevitable, and so is their seamless integration.

2.1.3.8 Context-Awareness

There is no standard definition of ‘context’. However, any information which is relevant and accessible at the time of interaction with a system can be called as

‘context’ [102] [103] [104] [105] [106] [107] [108]. Context-aware systems use some or all of the relevant information to provide better service to their users. A pervasive system that needs to be minimally distractive has to be context-aware. That is, it should be sensitive and responsive to different social settings in which it can be used.

Context-aware systems are expected to provide following features [109]:

Context discovery: locating and accessing possible sources of context data.

Context acquisition: read context data from different sources using sensors, computer vision, object tracking, and user modeling etc.

Context modeling: defining & storing context data in a well-organized way using any context model, such as key-value model, logic based model, graphical model, markup scheme, object oriented model, or ontology based model [109]. If different models are used in the same domain & semantics, context integration is required to combine the context.

Context-fusion or aggregation: combining interrelated context data acquired by different sensors, resolving conflicts and hence assuring consistency.

Quality of Context (QoC) indicators: showing Quality of Context (QoC)

[110] from different sources in terms of accuracy, reliability, granularity, validity-period etc [111].

Context reasoning: deducing new context from the available contextual information using, for example, first-order predicates and description logics.

Context query: sending queries to devices and other connected systems for context retrieval.

Context adaptation: generating an adaptive response according to the context using, for example, IF-THEN rules.

Context storage and sharing: storing context data in a centralized or distributed place, and then distributing or sharing it with other users or systems [112].

It is important to note that the lack of standard definition of ‘context’ makes it difficult to represent and exchange context in a universal way [113].

2.1.3.9 Privacy and Trust

Many users join a pervasive system on ad-hoc basis. A pervasive system has a very rich collection of information about user patterns. We need to share this

15

information with others for a better service. For example, sharing my location with others may help them locate me quickly when needed. We need to provide reasonable privacy and trust to the users. This may be done by using authentication, allowing users hide their identity, or even turning off monitoring for a reluctant user.

2.1.3.10 Ubiquitous Interaction Design

Ubiquitous & pervasive systems incorporate a variety of devices, ranging from handheld PCs to wall-sized displays. Interfaces are transferable and are used in changing locations by a mobile user. This has created new challenges for Human-

Computer Interaction (HCI) and Interaction Design.

2.2 Designing for UbiComp Systems

Ubiquitous computing systems are used in real world environment to support dayto-day activities. These systems should have a very careful and well-informed design.

A poorly designed system will simply be rejected by the people. Applications that are introduced after a careful study of user needs & requirements are more successful.

Different methods are available for capturing user needs, such as requirementsworkshop, brainstorming, use-case modeling, interviewing, questionnaires, and roleplaying etc. Some innovative methods are also available especially for the design and development of ubiquitous computing systems, such as ethnography, participatory design, and rapid prototyping etc [131].

2.2.1 Background

Ubiquitous computing systems are essentially context-aware systems. The design of UbiComp systems depends on how we conceive the notion of context. There exist two contrary views of context [106] [116]. One comes from positivist theory – context can be described independently of the activity or action. Think about a discussion happening in a classroom, for example; the discussion is an activity, while the time, location & identity of participants are features of the context. Another view comes from phenomenological theory – context is an emergent property of activity and cannot be described independently of that activity. Most of the early context-aware systems follow positivist approach, while phenomenological approach is becoming more popular nowadays.

Phenomenological approach has a very strong position. Winograd [117] says that something is considered as context because of the way it is used in interpretation.

Dourish [106] considers “how and why” as the key factors of context which make activities meaningful. Zheng and Yano [115] believe that activities are not isolated; they are linked to the profiles of its subject, object and tools used. The phenomenologist consider practice – what people actually do and what they experience in doing – as a dynamic process [118] [119] [120]. Users learn new things during the performance of an activity. New aspects of environment may become relevant for the activity being performed, which extends the scope of context. We can say that practice combines action and meaning; and context provides a way for making actions meaningful [106]. Ishii and Ullmer [98] put the idea of embodied-interaction, which is related to the methods in which meaning of objects, come up out of their use inside systems of practice. The invisibility of ubiquitous computing technology is not ensured by its design, but by its use inside systems of practice [121]. That is, invisibility can be assured by augmenting and enhancing what people already do (using pre-existing

16

methods of interaction) [159]. This makes applications un-obtrusive, unremarkable and hence effectively invisible. Table 2.2 summarizes assumptions underlying the notion of context in both approaches.

Table 2.2: Positivist approach Vs. Phenomenological approach

Positivist Approach

(representational model)

What is context? describes a setting

Context is something that people actually do, and what they experience in the doing

What we look for?

Main issue

Features of the environment within which any activity takes place

Representation – Encoding and representation of context

Relational property that holds between objects or activities

Interaction – ways in which actions become meaningful

Relationship between context

& activity

Activity is described by

Who, What, When, and Where i.e. user ID, action, time, and location respectively.

Scope of the context

Remains stable during an activity or an event (independent of the actions of individuals)

Phenomenological Approach

(interactional model)

Particular to each occasion of activity or action

Why and How are also used in addition to Who, What, When, and

Where [158].

Why and How represent user’s intention and action respectively.

Defined dynamically and not in advance

Modeling & encoding

Can be encoded and modeled in advance – using tables

Arises from activity and is actively produced, maintained and enacted during activity – using interaction

Example(s)

[123], and Ryan et al [124].

Dourish [106], Winograd [117], and

Zheng & Yano [115].

We can conclude that context is not a static description of setting; it is an emergent property of activity. The main design opportunity is not related to using predefined context; it is related to enabling ubiquitous computing application to produce, define, manage and share context continuously. This requires following additional features:

Presentation – displays its own context, activity and resources around; Adaptation – infers user patterns and adapts accordingly [125]; Migration – moves from place to place and reconfigures itself according to local resources [126]; and Information-

centric model of interaction – allows users interact directly with the information objects and information structure emerges in the course of users’ interaction [127].

2.2.2 Design Models

We cannot use traditional models of software engineering for ubiquitous systems.

There are two main approaches for designing context-aware ubiquitous and pervasive systems [106]: Representational model of context (positivist theory) which considers context as static description of settings, independent of the activity or action; and

Interactional model of context (phenomenological theory) which considers context as an emergent property of activity.

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Most of the early systems follow representational model, while interactional model is becoming more popular nowadays. In this section, we describe both approaches, but our main focus will be on the second approach i.e. interactional model.

2.2.2.1 Representational Model

Dey [114] has identified a very simplified process for designing context-aware systems that consists of the following five steps:

1. Specification: State the problem at hand and its high-level solution. This step can be further divided into two parts: i. Find out the context-aware actions to be implemented. ii. Find out what context data is needed and then request it

2. Acquisition: Install the essential hardware or sensors to acquire context data from the environment.

3. Delivery (Optional): Make it easy to deliver acquired context to the context-aware systems.

4. Reception (Optional): Get the required context data and use it.

5. Action: Select an appropriate context-aware action and execute it.

This model assumes that the context is a static description of settings, separate from activity at hand, and can be modeled in advance. If we follow this model, we end up with a rigid system that cannot fit into the dynamic environments to support real life activities.

2.2.2.2 Interactional Model

In interactional model (phenomenological theory), context is considered as an emergent property of activity and is described in relation to the activity at hand

[106] [115]. Interactional model is used in most of the modern systems [106]. In this model, the design can enable UbiComp system to constantly produce, define, mange and share context.

As we know that UbiComp systems exist in the natural environment, it is very important to understand human activity so that we can support natural interactions of humans in UbiComp environment. We can use iterative approach of designing ubiquitous systems [129] [130] to have better understanding of a complex problem space. In an iterative approach, steps are repeatedly applied until we are satisfied with the results. These steps are briefly explained below and are shown in figure 2.3 below.

Domain understanding

Prototyping

Idea formation

Figure 2.3: The iterative approach of designing UbiComp systems [130].

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2.2.2.2.1 Domain Understanding (Research)

Domain understanding is a key to successful development and implementation of a system [133]. It requires detailed study of user environment and real life setting in which the technology or application will be used. Ethnography [132] helps us in this first phase of the system development. It consists of observations, interviews, and other useful tools, such as field notes, digital photographs, artifacts, and video recordings etc [130]. We need to focus on the aspects that can be easily implemented by system designers. This helps us avoid gap between the ethnography and design of the systems. Ethnography can also help us in identifying any socio-technical gap i.e. a gap in social practices and technology available to support them. Identification of this gap can help us in designing innovative technologies to fill it [137].

Ethnography involves the study of how people do their work in real world settings.

A careful ethnographic study can inform a better design and implementation of a system. It also helps in identifying how people handle exceptions, cooperate or compete, do something, and the design of a system itself. Ethnography involves sociologists, cognitive psychologists, and computer scientists in the design process

[134]. In this way, it creates synergic effect that brings many different aspects of the system. The ethnographic study of the system is more useful for designers if the ethnographer has some knowledge of designing and developing the system. However, ethnography alone is not sufficient for successful design & development of a system

[135].

Some researchers think that ethnography is a time consuming & costly process that is of less use for designer [136]. It emphasizes on data collection through first hand participation, and organizing data by giving meaningful explanations. These explanations are too lengthy to make designers understand user requirements for a system. Therefore, they recommend using Rapid Ethnography or ‘quick and dirty

ethnography’ to complete it in shorter time

[183].

It is important to observe actual work practices, identify any exceptions and find how people resolve them. To address these problems, a different style of design used by Scandinavia, is recommended. This is called as Participatory Design or

Scandinavian Design [138]. It aims at involving intended users in the system design process, and expects an effective, acceptable and useful product at the end.

The data collected during ethnographic studies must be analyzed carefully. This will produce clear understanding of the domain. Video recordings, if any, may be helpful to better access the richness and complexity of interactions taking place in that domain. After performing ethnographic study of people in a natural environment, we should be able to describe the actions they do, information they use, technology that might help them complete their tasks, and understand relationship between different activities [130].

2.2.2.2.2 Idea Formation (Concept development)

This phase shows how ethnographic descriptions are able to inform the design of

UbiComp system or device. The ethnographic study enables us to form a rough sketch of the system that can serve the user needs. It is, however, very complicated to move from ethnographic study to the design of a new system [139].

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We need to uncover the interesting aspects of the environment and then envision a new system that may better serve the users in that environment. We need to decide about many components of the system, such as devices to be used, sensors to be deployed, and information to be provided etc. We may come up with a number of different technological solutions. By observing the user activities, we can understand how the technology is used and how it changes the activity itself.

2.2.2.2.3 Prototyping

Many ideas may emerge from the primary ethnographic study. The designers should match a prospective solution to the target humans and their environment. After domain understanding and idea formation, we can build some mockup prototypes, drawings, sketches, interactive demos, and working implementations etc [129]. We can test these prototypes and find their usability & utility. The finalized prototype may be considered for full-scale system implementation.

We can experiment with existing technologies and design new technologies & systems. The proposed system should be novel in its design, be able to serve the user needs, and must be minimally intrusive. The proposed system should not only support but also improve the user activity. The designer should keep in mind that the environment will affect the task and therefore provide an interaction that is suitable for the environment.

2.2.2.2.4 Evaluation and Feedback

Prototyping and role-playing [140] can help in getting user feedback and determining the usability of new technology. The finalized prototype of the system can be offered to the users for evaluation. A rapid prototype can help users play with the system and provide feedback in a better way. A soft prototype can be helpful in better design and successful implementation of the system.

If the prototype system is well designed, users may find it interesting and easy to use. A continuous use of the system may make the users mature and they may suggest some additional features to be included in the systems.

2.2.3 Interaction Design

UbiComp has forced us to revise the theories of Human Computer Interaction

(HCI). It extends interaction beyond the desktop containing mouse, keyboard and monitor. New models of interaction have shifted focus from desktop to the surroundings. Desktop is not like the way humans interact with the real world. We speak, touch, write and gesture that are driving the flourishing area of perceptual interfaces. An implicit action, such as walking into an area is sufficient to announce our presence and should be sensed & recognized as an input. We can use radio frequency identifications (RFIDs), accelerometers, tilt sensors, capacitive coupling and infrared range finders to capture user inputs. We can make the computing invisible by determining the identity, location and activity of users through their presence and usual interaction with environment. Output is distributed among many diversified but properly coordinated devices requiring limited user attention. We can see new trends in display design. These displays require less attention like ambient displays (Dangling

String [149], Ambient ROOM [148], Audio Aura [147] etc). We can also overlay electronic information on the real world to produce augmented reality [97]. Physical

20

world objects can also be used to manipulate the electronic objects as in graspable or tangible user interface (TUI) [98]. All these things have made it possible to have a seamless integration of physical and virtual world [128].

Theories of human cognition and behavior [150] have informed interaction design.

The traditional theory of Model Human Processor (HMP) stressed on internal

cognition pushed by three autonomous but co-operating units of sensory, cognitive, and motor activity. However, with the advances in computer applications, designers now take into account the relationship between internal cognition and the external

world. Three main models of cognition are providing bases for interaction design for

UbiComp: activity theory, situated action, and distributed cognition [153].

2.2.3.1 Activity Theory

Activity theory [151] realizes notions of goals, actions, and operations; which is very close to the traditional theory. However, goals and actions are flexible, and operation can shift to an action depending on changing environment. For example, cardriving operation does not require much attention from an expert driver; but in rush hours & bad weather, it needs more attention that results in a set of careful actions.

Activity theory also highlights transformational properties of artifacts. This property says that objects, such as cars, chairs, and other tools hold knowledge and traditions, which determine the users’ behavior [152]. An interaction design based on activity theory focuses on transformational properties of object, and the smooth execution of actions and operations [128].

2.2.3.2 Situated Action

Situated action [154] highlights unplanned human behavior and says that knowledge in the world constantly forms the users’ actions i.e. actions depend on the current situation. A design based on this theory would intend to impart new knowledge to the world that would help forming the users’ actions, for example, by constantly updating the display.

Our proposed Smart-Dashboard design is also based on situated action theory. A driver constantly changes her behavior given the changing road conditions, traffic information, road signs, and weather conditions etc.

2.2.3.3 Distributed Cognition

Distributed cognition [155] [157] considers humans as part of a bigger system and stresses on collaboration, where many people use many objects encoded with necessary information to achieve system goals. For example, many people

(crewmembers) use many tools to move a ship into port.

An interaction design based on distributed cognition stresses on designing for larger system goals, encoding information in objects, and translating that information by different users [128].

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2.3 Issues in UbiComp Design

Designing is an act of making the form of something either form start or by improving an existing object/process. There are many types of design, such as user interface design, graphic design, web design, interaction design, industrial design, and user centered design etc.

In this section, we discuss several issues regarding designing for UbiComp systems.

2.3.1 What and When to Design?

When we observe that there is an urgent need or want for something, we have an opportunity to carry out a design to satisfy that need or want. In UbiComp, the design must be informed by the user need or want, and not that of the designer. Although, there is a space for creativity, but the designer should take care of user’s need or want and the system goals.

2.3.2 Targets of the Design

The first and the prime target of UbiComp system design is the anticipated user. It is very useful to let the anticipated users draw a sketch of the device/system they want

[141]. This sketch can be useful for designer to realize a system that is simple and useful.

A second target of design is the user-environment that directly affects the user.

Different environments have different characteristics, such as open (where information can flow), close, harsh, gentle etc. The designer has to know the user-environment that may change from time to time.

The last target of design is the device. The designer should make sure that all devices serve their purpose for the user unobtrusively. The devices which require much of the user attention, e.g. mobile phone, are obtrusive and do not allow users to pay attention to any other task.

2.3.3 Designing for Specific Settings – Driving Environment

A system for drivers should have a design that is easy to use, require very less user attention and time to complete a task [143]. A distraction of only a few seconds may result in a vital road-accident. For example, a large text message on a screen requires much attention from user to read it, hence be avoided.

Secondly, not all the drivers are well educated and computer literate. Therefore, the system should require little or no training, troubleshooting and administration.

Thirdly, it should not have a major affect on driving activity itself, i.e., it should let drivers drive their cars as they have always done unless there is a major problem. The system should fit into the driver environment rather than enforcing it like an office system. It should not only accommodate the wide range of driver’s activities but also support them. The system should provide an interface to connect different devices that may be used in cars, such as mobile phone, to make drivers’ life easy.

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Fourthly, such a system should have low cost especially when it is being introduced as an add-on; an expensive system may not be bought and used by drivers.

Finally and the most importantly, the system must follow the guidelines set by different governmental bodies issuing a set of requirements for systems regarding road-safety [142] [143].

2.3.4 UbiComp and the Notion of Invisibility

Ubiquitous means omnipresent or everywhere whereas invisible means unremarkable or un-noticed. At first, ubiquity and invisibility look two conflicting terms but actually, they are not. Ubiquity entails embedding computing into everyday objects and invisibility entails using pre-existing & unobtrusive methods of interaction to augment what people already do [159].

Invisibility implies offering right service at right time by anticipating user needs with minimal user interruption. Designers should keep in mind that the system should be literally visible but so un-obtrusive that it becomes effectively invisible or unnoticeable.

2.3.5 Calm Technology

Some technologies are so obtrusive that they do not fit to our lives e.g. a videogame, alarms etc. However, some are calm & comfortable, such as comfy pair of shoes, a nice pen etc. What makes the difference is how they engage our attention.

A calm technology [146] [149] uses both the centre and the periphery of our attention and moves back and forth between the two. Periphery is what we are used to but it requires no explicit attention e.g. noise of the engine when driving a car. A thing in our periphery at one moment may be at our centre of attention at the next e.g., an odd noise of the car engine catches our attention. Calm technology will easily move between the periphery and the centre, making it possible to use many more things at a time. We can take control of something by re-centering it from the periphery.

We need to design for the periphery so that we can access and use technology without being dominated by it.

2.3.6 Embodied Interaction

We have seen transition from electrical interface to symbolic interface to textual interface to graphical user interface (GUI). The improved power of computers and increasing context of their use calls for new ways of interaction with computers that are better tuned to our abilities and needs.

Dourish Paul [145] gave the idea of embodied interaction. Tangible, physical and social approaches to computing suggest interacting directly through physical objects in a way we experience the everyday world rather than GUI and interface devices, such as mouse. This gives the idea of embodiment, which says that the things are embodied in the world and hence interaction depends on the settings in witch it occurs.

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2.3.7 Limitations of Ethnography

The designers should be aware of the limitations of ethnography [136] to avoid pitfalls. Despite its limitations, ethnography provides us better understanding of domain, tells us about how people use a system, and what additional features they need in a system.

2.3.8 Prototyping

Prototyping provides anticipated users an opportunity to use a system before its full-scale implementation. However, prototyping is expensive, time-consuming, and confusing for designers. A better prototype can be designed through better domainunderstanding, selecting real users, and real settings for testing the prototype.

2.3.9 Socio-Technical Gap

A gap in social practices and technology available to support them is called as socio-technical gap. This gap should be known to the designers as well as users so that they realize what available technology cannot support. Sometimes a supporting technology may be available but it is so obtrusive that it cannot be used in an

UbiComp system.

2.3.10 Hacking

Sometimes, users explore devices or systems to find some innovative uses that were not even perceived by their designers; this is called as hacking. Here, hacking does not mean to let users breach the security or take illegal control of the devices or systems.

A good design allows hacking i.e. it allows users to find some innovative use of the device or system. A user feels emotional attachment to a device or system that he/she has hacked for some innovative uses [144]. One example of hackable systems is

Email, where users have found many uses other than sending a message, such as using email space as online file storage, sending junk messages, spreading viruses, unsolicited ads, and scam etc.

2.4 UbiComp and Smart-Dashboard Project

Ubiquitous Computing strives to bring computing into every part of human life.

From current trends in computing [86] [87], we can predict that computing will be everywhere in our life after few years. The automobiles have also benefited a lot from advancements in computing and sensing technologies. A modern car comes with a number of microprocessors and sensors embedded [69]. To make driving safer and more enjoyable, new systems for in-car use are being introduced daily. In our design, we plan to use sensors to keep an eye on driving environment and provide relevant information for drivers in their car as and when needed. It can play a positive role in improving driver awareness and performance.

In the next chapter, we perform ethnographic study of how people drive their cars and the factors affecting their actions while driving.

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2.5 Conclusions and Future Directions

Ubiquitous Computing suggests the natural way of human-computer interaction. It also encourages the system designers to consider new interaction models, such as gesture, sound and touch etc. It brings computers into human world that already exists instead of pulling human to the virtual world of computers. It is becoming a technology that will be calm and comfortable for its users.

Mobile computers, such as PDA’s, cell phones, electronic tags, sensors and wearable computers are becoming popular. Wireless networking technologies, such as

Bluetooth, GSM, WLAN, WiFi, and WiMax are becoming more ubiquitous.

Almost all the future applications, services and devices will be context-aware.

Currently, there are no standard context modeling and query languages available.

Resource discovery, use of historical context, learning, and security are the least supported features of current context-aware systems. We need to develop standard context-modeling scheme, communication protocol, system architecture, and interaction model.

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3 E

THNOGRAPHIC

S

TUDIES

In order to design technologies for natural interactions of humans, it is very important to understand human activities in real world settings. A number of methodologies have been developed in social sciences, such as ethnography in anthropology, for better understanding of activities & social settings. Ethnography deals with the study of human activities in real world settings. It gives a detailed description of “context and evolution” of human interaction, i.e. it describes human activity as well as the human experience while doing it. Ethnography is a participatory approach in which the ethnographer becomes part of a setting and observes the activity that is taking place in the setting for an extended period of time and then reports it in writing. An ethnographer applies a number of tools to capture rich understanding of real life setting and activities taking place in it. These tools include observations, interviews, field notes, questionnaires, digital photographs, artifacts, and video recordings etc [130]. Although ethnography is time-consuming, confusing, insufficient and costly [135] [136], a careful analysis of ethnographic results can provide very useful ‘hints’ for UbiComp system design.

Ethnography is briefly introduced in the next section in order to develop better understanding of the origin of ethnography and its role in HCI & UbiComp system design.

3.1 Introduction

Sociology, anthropology and ethnography are related disciplines, which hold an overlapping relationship. Sociology is a social science that deals with the study of the development, structure, and functioning of human society; anthropology is a social science that deals with the study of humankind, especially the study of societies and cultures and human origins; and ethnography is a branch of anthropology that provides scientific description of peoples and cultures [167]. Where, anthropology passively records what members of other cultures do, ethnography requires active participation in everyday life to realize what members of those cultures actually experience by their actions. Ethnography urges to use long-term and devoted fieldwork through participatory observation instead of surveys and interviews.

Anthropology started in 19 th

century during the Western expansionism when it was used for recording the quickly shrinking cultures of original Americans. Within the discipline of anthropology, ethnography started in the early part of 19 th

century, during

World War 1, primarily by Bronislaw Malinowski in his work on the Trobriand

Islands [168]. He lived the life of Trobrianders for few years and studied the culture and practices of the local population. He experienced what they experienced; how they experienced; and their reaction to such experiences. In this way, he was able to find

“the member’s point of view”, and this fieldwork provided foundations for modern ethnography. Since then, ethnography is being used in many other fields with different flavors and intensities. One historical example of such use is in the work of the

Chicago School sociologists (Robert Park and others) in which they conducted an inquiry into the American urban life [160]. Other recent examples include research into crimes [169], drugs [170], politics [171], and technology [172] etc.

Recently, ethnographic methods have been used by researchers in HCI and

UbiComp design [161]. Ethnography was first used in Computer-Supported

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Cooperative Work (CSCW) to understand social organization of activity, and then in

Participatory Design (PD) to find employ’s views on changes in working conditions due to cybernation. Through PD, and CSCW, ethnographic methods became popular with HCI and UbiComp researchers. What makes ethnographic methods popular in these fields is their potential to capture the complexity of real world settings and use of technology in that context.

Ethnographic study can offer major insight and benefit for HCI research including implications for design. However, emphasis on implications for design should be

avoided because the valuable material lies somewhere else. Ethnographic studies may lose many of their potential benefits when performed for a specific purpose such as

“implications for design”. Ethnography is a multi-sited process and should be used for multi-sited processes [173]. It is possible that some ethnographic work may not present any “implications for design” but still presents valuable guidance for how to think about the implications for design [174] [175] [176] [177] [178] [179].

Ethnography has two types of contributions: empirical (e.g. observations) and analytic (e.g. interpretations). The implications for design are derived from analytic

aspects of ethnography and not that of empirical, i.e. a careful analysis of ethnographic results can provide useful ‘hints’ for system design. In this way, the movement from ethnography to design is conceptual and creative move.

Dourish Paul [161] has identified the following four major problems with ethnography when it is used (or intended to be used) for design:

1. The marginalization of theory: Ethnography is commonly mistaken as a field technique for gathering and organizing qualitative data. However, ethnographies are basically interpretive texts and give us not only observations but also the relationships between them. Ethnography helps us understand member’s experience through their interactions with the ethnographer. Therefore, we can say that ethnographies are descriptive texts about the culture, the cultural view from which it is scripted, and the target audience.

2. Power relations: The importance of ethnography has been undervalued, politically. There is a difference of power between engineering and social sciences, which is clearly visible in the relative size of research funding in these two fields. Nonetheless, we should not ignore interdisciplinary role of ethnography where it is really in service to the other disciplines.

3. Technology & practice: Ethnography is mistakenly assumed as a point of mediation between everyday ‘practice’ and the technical ‘design’. However, ethnography rejects this separation and assumes the fact that practice provides a form & meaning to technology. A good design in HCI will not only give a form & meaning to technology but also cover appropriation (a dynamic process of inclusion & evolution of technologies, practices & settings). Only poorly designed technologies need adaptation & appropriation.

4. Representation & interaction: It is generally believed that ethnography can highlight the practices of specific people. However, ethnography can do even more i.e. it also finds the operational principles by which these practices are produced, shared, re-produced and changed. Moreover, ethnography is often viewed as “scenic fieldwork” which focuses on moments i.e. it describes what happened in the past. We can extract different conclusions from these historical tales. However, the alternative view of ethnography is that it is “a model for

understanding social settings”. Therefore, important is not the description of what

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happened, but the descriptive form which organizes & connects these past moments.

There are some methods (mistakenly) labeled as “discount ethnographies” and proposed as an alternative to ethnography, such as interview-based Contextual Inquiry

[180] and survey-based Cultural Probes [181] [182]. At first, these two methods look similar to ethnography but actually, they are much different. They focus only on implications for design, and one can directly move to design phase after this step.

However, in reality, these methods are very limited and they fail to get what an ethnographic study can get. Therefore, we should not consider them as an alternative to ethnography.

In short, ethnography can be very useful in HCI and UbiComp design research.

Although ethnographies are descriptive texts and may not provide us a list of implications for design, the analytic aspects of ethnography (i.e. a careful analysis of ethnographic results) can provide us valuable guidance for how to think about the implications for design. However, this shift from ethnographic study to design practice requires imagination, creativity and analytical skills.

The work done by early ethnographers has guided us to include ethnography in the modern fields such as CSCW, PD, interactive system design, HCI and UbiComp. That is why we have performed ethnographic studies to explore how drivers try to ensure safe and smooth driving in order to inform the design of our Smart-Dashboard. After performing ethnographic study of drivers, we should be able to describe the actions they do, information they use, technology that might help them complete their tasks, and understand relationship between different activities… Our key research challenges in this thesis are to find what information about the surroundings should be provided to the drivers for better situation awareness, and how this information should be presented unobtrusively. Our ultimate aim is to design a Smart-Dashboard system which may augment safe and smooth driving.

3.2 Our Approach

Ethnography, like other participatory approaches, is very much revealing, trustworthy, direct, and produces better results than other approaches such as laboratory-based study, questionnaires, or interviews etc. However, ethnography is time-consuming, confusing, insufficient and costly [135] [136].

In order to save time, money, and efforts, we used minimal participation &

observations which is also known as “quick & dirty ethnography

[183] in which short ethnographic studies are conducted to provide a general understanding of the setting for designers. In addition to this, we used questionnaires, interviews, and video recordings as supporting tools. Such a mixed-methods approach [162] [163] was used to compensate for weaknesses of one method by the strengths of other methods.

We performed ethnographic study of ten young drivers, mostly our friends, and engaged them in face-to-face interviews. We captured on video four of them as they drove. We also handed over questionnaires to them and to many other people around the world through Internet. This selection of respondents was somehow biased, as a random selection would not work here. The results of this study are reported in the next section.

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3.3 Results

This section describes results from ethnographic study and other supporting tools such as questionnaires, interviews and video recordings.

3.3.1 Results from Ethnography

3.3.1.1 Introduction

This section reports ethnographic study of ten drivers, mostly our friends. We spent 2-3 hours daily with them for two weeks in order to understand their behavior on the road. We also involved them in ad-hoc discussions or interviews. We were specifically interested in finding what information about the surroundings could be helpful for drivers to avoid accidents.

3.3.1.2 Background

Annually, road accidents cause about 1.2 million deaths, over 50 million injuries, and global economic cost of over US$ 518 billion [1]. About 90% of the accidents happen due to the driver behavior [2] [3], such as bad awareness of driving environment, low training, distraction, work over-load or under-load, or low physical or physiological conditions etc. This ethnographic study was conducted to find how a driver support system (DSS) can play a positive role in improving driver awareness and hence performance by providing relevant information using a smart dashboard as and when needed.

3.3.1.3 Patterns discovered

From our initial study, we had identified three different occasions when drivers had different concerns: 1) in the parking area, 2) on the highway, and 3) inside a town.

Therefore, we performed a detailed study to find out the type of contextual information drivers needed in order to avoid accidents on each of these occasions.

We started out observing drivers in the parking area when they were driving their cars in or out of the parking lots. We observed that they were driving very slowly & carefully in the parking areas because cars were parked very close to each other and any mistake would result in a collision. They seemed to make best estimate of the distance of their car from others’ using rear-view & side-view mirrors or any other available technological support such as sonar and rearview camera etc. This shows that parking is an activity when drivers need to know the distance of their car form others’ and technology can play an important role here. Any technological solution for distance estimation will be beneficial for new drivers and an additional support for experienced ones. For example, one of the respondents told that parking was a challenging job for him and he had hit other cars many a times in the past because he could not estimate exact distance using side-view and rear-view mirrors. He explained that these mirrors were useful but any additional support such as sonar or rear-view camera would really help a lot. On the other hand, a mature driver with luxury car was feeling easy with parking and told us that he had never committed any accident in the

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parking area because his car had sonar which beeped to warn him when he was very close to another car. However, he had to rely on side-view mirrors in order to avoid any collision with vehicles on other sides.

To observe drivers on the highways, we accompanied our friends when they were traveling to the nearby town. This gave us an opportunity to observe drivers closely on the highways where vehicles move at a higher speed. We found that drivers tried to keep a safe speed and distance; they were keeping an eye on the direction of the movement of neighboring vehicles; and they were especially careful about heavy vehicles such as buses & trucks, and small-sized objects such as obstacles & motorcyclists. One of the new drivers told us that he was really annoyed by sudden appearance of objects, and that he felt difficulty in judging the distance, speed & direction of other objects on the road. For a safer journey, he used to take somebody with him while going on a long trip so that the other person would keep him aware of the crazy vehicles around him, especially in the blind spots. On the other hand, a professional driver was worried about some other factors such as size of the neighboring vehicles and the decreased visibility. He told us that he tried to stay away from heavy-vehicles because they might not move or stop quickly when needed.

Another thing that made him crazy was the decreased visibility due to weather conditions such as fog, dust, heavy rain, and snow etc, and due to the time of the day such as sunrise and sunset when some drivers leave their headlights off (decreased visibility) while others kept them bright even when crossing (dazzling). We also noticed that experienced drivers could recall locations of road-defects & other obstacles and pre-planed to avoid them.

On entering a town, we observed a clear change in drivers’ attitude perhaps due to the volume and type of traffic inside a town. They reduced their speed because of lower speed limits, and became alert as they were expecting more crossings, bridges, congestion, traffic signals, pedestrians, cyclists, motorcyclists, and animals on the road. These features of urban traffic required drivers to be aware and respond quickly.

For example, one of our respondents told us that he found it tedious to drive inside a town, and explained that even though speed was low, sudden appearance of any object might result in an accident because vehicles were too close to each other that there was not enough space available to change the lane quickly or apply breaks to avoid collisions.

In short, it would be useful for drivers if they know speed, distance, relative

position, direction, and size & type of the neighboring vehicles or other objects. We verified these observations and discovered some more facts by analyzing a number of video recordings and by using questionnaires & interviews, where our questions were mainly based on these observations. In the next section, we describe some of the valuable findings from video analysis.

3.3.2 Video Results

We captured four of our respondents on video as they drove. These videos helped us in confirming our earlier findings and discovering some more patterns. However, our video recordings could not catch any accidents. For analysis of accidents, a number of video recordings from traffic surveillance cameras were obtained from online resources such as YouTube.com

, a very famous video sharing website.

Examples of such videos include “Road accidents (Japan)” [165] and “Karachi

Accidents” [166].

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In our video recordings, a driver starts from driving his car out of the parking area, travels on the highway for about half an hour, enters a town, and parks his car in a parking lot. We saw in the videos that drivers mostly kept focused on the windscreen in front of them. Occasionally, when needed, they switched their attention to other locations for short time. However, they turned their attention back to the windscreen as quickly as possible. In a certain situation, drivers spent more attention on a place where they expected relevant information. For example, rearview mirrors got more attention in the parking area, whereas side mirrors got more attention when changing a lane on the highway. From the video recordings of drivers, we calculated Visual

attention

[164] which shows “for how many times (frequency) & for how long

(duration) a driver looked at certain locations”. We found that drivers kept focused on the windscreen in front of them for about 80% of the time while driving. Occasionally they switched their attention to the button control area (8%), speedometer (2%), rearview mirror (3%), side view mirrors (3%) and other areas (4%). However, these ratios may change with changing context such as traffic conditions, weather, route, time and the driver etc. This observation has very important application; that is the designer should consider drivers’ visual attention and avoid putting useful information away from their visual approach.

In the videos on road-accidents, we found that many accidents occurred in the

blind-spots – which are areas of the road on right and left of the vehicles that are not covered by any of the side-mirrors, forward vision, or rearview mirror (see figure 3.1).

The major reason for these accidents was that a sudden appearance of any object in the blind spots went unnoticed and resulted in an accident. Any technology that can make drivers aware of the objects appearing in the blind spots would help reducing such accidents.

Forward Vision

Blind Spot Blind Spot

Side

Mirror

Vision

Rearview

Mirror

Vision

Side

Mirror

Vision

Figure 3.1: Blind spots on both sides of a vehicle

In these videos, we also noted that many accidents occurred because of a sudden

change in the speed or direction (a.k.a. acceleration) of some neighboring vehicle.

That is, a moving vehicle either suddenly stopped or took a turn, or a stationary vehicle

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suddenly moved. For example, while moving on an average speed on the highways, drivers assumed that the cars in front of them would continue moving with the same speed. However, when a car in the front suddenly stopped or took a turn, accident occurred because of other drivers’ inability to react quickly. Again, here technology can help drivers in reducing accidents by identifying a sudden change in the speed or direction of the neighboring vehicles.

We noted another important factor – miss-judgment & unawareness – which accounted for a large number of road accidents and appeared in different forms. For example, the driver was unable to recognize smaller objects such as obstacle, human, bicycle, and motorcycle etc; the driver failed to judge other’s path, size, position or speed; the driver failed to keep in proper lane or ran off the road; and the driver failed to recognize a road sign such as a stop signal on the crossing. In all of these situations, a driving support system would improve drivers’ awareness and augment their decision capabilities by identifying smaller objects, unclear road signs, lane & path, and other objects nearby.

To obtain some statistical data on our observations, we used questionnaire. The results from questionnaire are presented in the next section.

3.3.3 Results from Questionnaire

In the questionnaire, we were particularly keen to investigate three issues: 1) how many cars had installed modern safety features; 2) what were the causes of distraction

& road accidents; 3) and how could we augment humans for safe and smooth driving.

We designed a questionnaire (Appendix A1) consisting of 15 questions for drivers. We launched our survey using an online tool www.surveygizmo.com

. This survey was a great success which received 192 responses from around the world including Europe,

North America, Middle East, Far East, and Australia. The results of this survey

(Appendix A2) are briefly described in this section.

We had an initial assumption that all the most recent cars (2008 and newer models) would have at least one of the modern safety features such as night vision, parking assistant, active cruise control, traffic sign recognition, and blind spot detection etc, but it was found that only 60% of them had some safety features. These modern safety features are usually available in modern luxury cars, but only a small ratio of ordinary cars come with any of these features that would make them more expensive. Our survey results show that 85% of our respondents had somehow a new car but only 31% had installed any of the modern safety features. One of the basic devices in road-safety systems is the in-vehicle display which is usually used to show any relevant information for drivers, and also used for other purposes such as GPS navigation, CD/DVD display, and speedometer etc. We found that these in-vehicle displays are not very popular yet; only 31% of the respondents had any kind of display mounted on their car’s dashboard.

One of the major reasons for serious road accidents is the driver’s distraction

[186] i.e. drawing her attention away from the road. It is very important to find out distracting things so that proper solution could be provided through a well-designed driver support system. One-third of our respondents (i.e. 33%) think that the most distracting things for them are the “things outside the car” such as too much traffic, heavy traffic (trucks & busses), people and animals crossing the road, vehicles which are too close, vehicles which are too fast, motorcycles & bicycles, and uneven, curvy

& damaged roads. Some of the respondents think that unclear road signs and the

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vehicles that are moving too slow are also distracting. Another major reason for distraction is the “driver’s personal state” such as tiredness, sleepy, and being depressed or upset etc. Some of the optional activities taking place inside the vehicle, such as the use of mobile phones etc, are also very much distracting. We found that

82% of our respondents use mobile phones, laptops or other hand-held computers while driving. Although mobile devices are commonly used to make voice calls

(96.53%) and messages (31.21%), few people (7.51%) use it for playing games, photography and audio/video recording which can be highly dangerous while driving.

It is important to note that 89% of the respondents think that reading an SMS while driving requires much of their attention i.e. it is obtrusive and causes distraction. These results suggest that long text messages should be avoided for conveying information about surrounding vehicles to the drivers. Instead, we can use standard icons and symbolic representations for quick understanding.

There are three major factors which contribute to road accidents: human factors, road defects, and vehicle defects [185]. However, in the last few decades we have seen a significant improvement in the quality of roads and vehicles which leave human factor as the only dominant factor in road accidents. In our survey, 84% of the respondents think that the most common reason for road accidents are human factors such as drowsiness, tiredness, fatigue, inattention, over-speeding, drinking, changing lanes without warning, and inability to recognize a road sign or an object etc. It is important to note that the drivers’ fatigue, tiredness or drowsiness is one of the major reasons for “fatal accidents” [186]. Although most of our respondents were experienced drivers, only 31% of them could drive for more than 4 hours continuously without taking any rest or break. A continuous long drive can be tiring and boring which can result in a fatal accident.

As human factors are the most common reason for road accidents, technology can be used to augment drivers for safe & smooth driving by improving their awareness of the settings. Our respondents think that the information about neighboring objects that can help in avoiding accidents includes speed (65%), distance (52%), relative position (39%), direction (34%), and size & type (26%); and a combination of all would best serve the purpose. In dangerous situations, rather than to actively takeover the control from drivers, it would be much better to passively present this information to the drivers for proper action and issue an alert. Here, an important question is to find the best location for displaying this information inside the vehicle. This location should be chosen while considering drivers’ visual attention and should be within their visual approach [164]. For majority of our respondents, speedometer (after windscreen) is the easiest location to see while driving. This gives us a nice hint for location of our proposed system. In addition to displaying contextual information, any proactive action such as issuing a warning/alert is very helpful in avoiding accidents.

Many modern vehicles include some kind of warning or alert system for this reason. It is interesting to note that auditory alert is preferred by majority of our respondents (i.e.

54%), while 51% prefer automatic or takeover the control from driver (e.g. automatically apply brakes to avoid collision etc). However, this automatic option can be even more dangerous in some situations. Other kinds of possible alerts include haptic (e.g. shake the driver seat if sleeping) and textual/visual alert. It is important to note that a combination of different alerts can better serve the purpose. We’ll preferably use a combination of only auditory and visual alerts in our proposed system.

In short, these results suggest that we’d incorporate our system into the speedometer to show information on speed, distance, relative position, direction, and size & type of vehicles around, and to issue auditory and visual alerts when needed.

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3.3.3.1 Comments from the respondents

We included one open-ended question at the end of our questionnaire to get any comments from the respondents. Some of the interesting comments are given here:

1. Best way is that drivers keep control and stay focused and technology may be introduced for better results simultaneously.

2. Drivers need to be taught the importance of patience.

3. Everyone believes he/she is a better driver than he/she actually is. Notice that everyone driving slower than you is an idiot and everyone faster is a maniac.

4. One should have a fully fit vehicle, one should leave early so not to drive fast, be in a comfortable state of mind and physique, should not use mobile phone while driving, watch out for others making mistakes and constantly keep looking in side and back view mirrors.

5. Knowing about your surroundings, e.g. person standing back of car while you are driving back will be helpful. However, at the same time please note that only give information which is needed and only when it is needed.

6. Most accidents happen when driver is assuming something and it didn't happen.

For example, car in front stopped suddenly, or didn’t start moving.

7. The questions of this survey are specific to normal drivers, and cannot be applicable to heavy traffic driver.

8. In third-word countries, you always have to drive with the supposition that your neighboring drivers are reckless and will suddenly make a mistake - endangering you or others around you. Therefore, you should be able to react quickly to avoid any damage.

9. Making drivers aware of their environment can significantly reduce chances of accidents. Accidents mostly occur due to negligence of drivers somehow.

3.4 Conclusions

Ethnography can be applied to capture the complexity of real world setting and use of technology in it. We applied “quick & dirty ethnographyto find what information is needed by the drivers to avoid any forthcoming collision in order to inform the design of our Smart-Dashboard.

We have found that the modern safety features such as night vision, parking assistant, traffic sign recognition, blind spot detection etc are still avoided in ordinary cars that would make them more expensive. About 90% of the road accidents happen due to the driver behavior. Our study shows that it will be very useful for drivers if we provide them with the information on speed, distance, relative position, direction, and

size & type of the vehicles or other objects around them.

Based on our findings, we’ll propose a simple & inexpensive system that would provide the relevant information, and produce alerts when needed.

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4 G

ENERAL

C

ONCEPT

D

EVELOPMENT

Moving from ethnography to the design phase is very complex. However, concept development stage makes this movement easier by serving as a mediate between ethnography and design phase. It also helps us forming the “rough sketch” of the required system.

The concept development stage started as soon as we received first response to our questionnaire and completed the first interview. We developed concepts around the observations, survey-responses, video-analysis, and the interviews.

We know that about 90% of the accidents happen due to the driver behavior [2] [3], such as bad awareness of driving environment, low training, distraction, work overload or under-load, or low physical or physiological conditions etc. We also know that modern safety features such as night vision, parking assistant, traffic sign recognition, blind spot detection etc can be useful in making drivers aware of their surroundings so as to avoid any forthcoming accident. However, only a small ratio of new cars (other than luxury cars) comes with modern safety features that would make them more expensive. This calls for an inexpensive, easy to use, and effective driver support system which could be used as an add-on.

In this chapter, we describe some of the interesting aspects of driving environment and predict a simple & inexpensive Smart-Dashboard system that would help drivers in safe and smooth driving.

4.1 Need for better situation awareness

From the analysis of videos on road-accidents and the statistical data obtained from our survey, we find that the major reasons for road accidents are human factors which include, among others, bad awareness of driving environment, inability to recognize other objects, and miss-judgments etc. These human errors can be minimized and hence performance can be improved by making drivers aware of their context. This can be done by providing them all the relevant information inside the vehicle as and when needed.

4.1.1 Improving Context-awareness

After careful analysis of the results of ethnographic study, we find that in order to avoid any forthcoming accident, drivers need the following five pieces of information about vehicles or other objects around them:

1. Their distance,

2. Relative position,

3. Relative speed ,

4. Direction of movement, and

5. Size & type.

A combination of all the five parameters will provide a meaningful piece of information in a certain context. For example, consider a scenario in which a relatively fast moving (speed) bus (size & type) suddenly appears in your left blind spot

35

(position), quickly overtakes you, and enters into your lane (direction) just in front of you (distance). An accident may occur if you are unaware of the situation or if you react slowly. However, this situation is not dangerous if there is a safe distance or if the distance is increasing instead of decreasing. Therefore, a combination of all the five parameters will be used to detect dangerous situations in a certain context.

4.1.2 Detecting Blind-spots

Moreover, we find that many accidents occur in the blind spots because drivers are not well aware of the objects suddenly appearing in that area. Therefore, blind spots should be specially taken care of.

A simple solution to the blind-spot problem can be provided by around-view

mirrors – convex mirrors which can provide mirror-view of these blind spots.

However, around-view mirrors are not much useful because:

1. They don’t work in darkness.

2. They (being convex) very much reduce the size of objects in the mirror image.

3. It is hard to guess distance, speed & direction of objects in the mirror image.

The blind-spot problem has been addressed by many researchers and some of the proposed technological solutions to this problem include:

1. use of a camera attached to the back bumper of car that provides view of the area behind the car when in reverse,

2. vehicle on-board radar (VORAD) [184] that uses a radar system to detect other objects around a heavy-vehicle,

3. lane-changing alarm which uses infrared or ultrasound sensors to detect objects in the blind-spot while changing a lane, and

4. other systems for blind-spot detection and warning using mobile devices, GPS, and road infrastructure etc. (see section 1.5 for more details)

However, none of these methods provide a comprehensive solution to the blindspot problem. Our proposed system would provide a complete picture of the surroundings in order to make drivers aware of their context.

4.1.3 Enhancing Object-Recognition

Good judgments and reactions are, by and large, based on better recognition and situation awareness. From the analysis of videos on road-accidents, we also find that many accidents occur because of the drivers’ inability to recognize smaller objects such as pedestrians and bicycles etc. These objects are relatively harder to notice while driving; bad weather conditions make it even worse.

It is generally observed that any collision with a smaller object usually results in a fatal accident. Our proposed system would be smart enough to identify these smaller objects, and warn the driver of their presence in very close vicinity.

4.2 Need for an unobtrusive system

From the results of ethnographic study, we find that drivers need to keep focused on the road in front of them while driving. Although they may switch their attention to

36

other places for short time when needed, they cannot keep their attention away from the road for more than a few seconds that may cause a road-accident. Therefore, the proposed system would consider visual attention of drivers and display the useful information within their visual approach [164]. This will assure minimal user interruption.

We also find that reading a text message is highly obtrusive activity, which needs a lot of attention from the reader. Therefore, long text messages should be avoided, and information should be conveyed to drivers using some standard symbols (such as standard road signs, and commonly used symbols for different objects or events etc) or other methods.

Keeping in mind the visual attention of drivers, we propose a Smart-Dashboard system that would use an in-vehicle display to show required contextual-information to drivers. This would place useful information within the drivers’ visual approach in an unobtrusive way.

4.3 Need for an easy user interaction

We know that a driver can’t engage in any time-consuming or obtrusive activity while driving. Furthermore, it is not necessary for all drivers to be educated and computer literate. Therefore, the proposed system should be easy to use & interact with. However, installation and configuration might require some expertise. These limits should be kept in mind while designing any system for drivers.

In our proposed system, the users will be able to start quickly with default settings.

However, they will be able to customize the settings by inputting the level of their driving expertise (i.e. learner, beginner, experienced, or expert), type of warnings to be issued (i.e. none, auditory, visual, or both audio-visual), and volume of the sound for auditory alerts (i.e. anywhere from silent to loud). These settings will be remembered or saved for future use until changed by the user again.

4.4 Conclusions

Our proposed system would make drivers aware of their surroundings by detecting blind spots, recognizing smaller objects, identifying dangerous situations and alerting drivers well in time. It would provide a complete picture of the surroundings in order to make drivers aware of their driving context. The proposed system is expected to be unobtrusive and easy to interact with for drivers. This would augment safe & smooth driving and help reducing losses caused by road-incidents.

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5 T

ECHNOLOGIES

In this chapter, we will provide a brief survey of technologies that can support our proposed system which will implement a number of ADAS functions such as adaptive cruise control, lane keeping or departure warning, forward collision warning, intelligent speed adaptation, automatic parking, and blind spot detection etc. For implementation of these functions, we need to capture the information on speed,

distance, relative position, direction of movement, and size & type of the neighboring vehicles or other objects on the road. For this purpose, several technologies are available in the market having their own pros and cons. Most commonly used technologies include RADAR (Radio Detection And Ranging), LIDAR (Light

Detection And Ranging), Sonar (Sound Navigation And Ranging), GPS (Global

Positioning System), and Video-Based Analysis.

We will provide a brief description of these technologies and explain how they can be used in capturing the required information for ADAS. However, vision-based technology will be explained in more detail because we will use this technology in our proposed system.

5.1 Radar

Radar stands for “radio detection and ranging”. It uses radio waves (frequency range about 300 MHz to 30 GHz) to find the distance, height, direction and speed of any stationary or moving object. It is an object detection system which is used for airplanes, vehicles, ships, ocean waves, weather monitoring, landscape, and other physical objects. A radar system has a transmitter which transmits in-phase radio waves (see figure 5.1(a)). These radio waves are spread in all directions after hitting any object on their way. Therefore, some part of the signal is reflected back to the sender (see figure 5.2). Due to Doppler Effect, the wavelength, and hence frequency, of this reflected signal is modified to some extent if the object is in motion.

(a) In-phase waves (b) Out-of-phase waves

Figure 5.1: An example of in-phase & out-of-phase waves

Figure 5.2: Principle of pulse radar

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The received or reflected signal is usually very week and hence needs to be amplified before processing. This amplification makes the radar system able to find objects even at large distances. Through computer processing, it can find distance, speed, direction and size of any target object. A radar detection beam used in vehicles is normally 150 meters long and 3-4 degree wide to each side of the vehicle.

The distance of any object from the radar transceiver can be calculated by using

time of flight (ToF) method which takes into account the time it takes the reflected signal to reach the receiver. Following formula is used for distance calculation:

Distance

=

Speed of radio

2

wave

×

Time

, where speed of the radio wave is almost equal to the speed of light (i.e. 300,000 km/sec). We know that the speed is defined as the rate of change of distance. Therefore, the speed of a target can be measured from few successive measurements of distance. However, modern radar systems combine other principles with the Doppler Effect to find the speed of moving objects. A radar transmitter is either fixed or rotates by up to 360 o

while sending out the radio waves.

After hitting an object, the signal is reflect back to the receiver at the same location.

This clearly tells us the direction of target object. Similarly, the larger object will reflect more waves than smaller object. In this way, we can also estimate the size &

type of the target objects. One example of radar-based systems is VORAD

[184] – vehicle on-board radar - that uses a radar system to detect other objects around a heavy-vehicle. Radar is the most feasible detection & ranging technology when cost is not an issue.

The main advantages of radar technology are its reliability, accuracy in finding speed & direction etcetera by using Doppler shift analysis, and its ability to work in any weather conditions. The main disadvantages of radar technology are its high cost, inability to work in presence of radar absorbent materials, creation of ghost objects due to multi-path reflections, inability to differentiate vehicles from other obstacles, and limited field-of-view (i.e. up to 16 o

only) & low lateral-resolution which may cause bad positioning of the target vehicle in some cases as shown in figure 5.3 below.

Figure 5.3: A special case where radar is unable to find the correct target [194]

5.2 Sonar

Sonar stands for “sound navigation and ranging” and is also known as acoustic radar. It is usually used by watercrafts (submarines and vessels etc) to navigate, communicate with, or to identify other vessels. It can also be used in air for robot

39

navigation, and for atmospheric research (where it is known as SODAR – sonic

detection and ranging). The working principles of sonar are similar to radar but it uses sound waves (infrasonic to ultrasonic) instead of radio waves. The speed of a sound wave is almost 340.29 meters per second at sea level in normal weather. An active sonar sends out sound waves which may be reflected by some object on their way (see figure 5.4 below), whereas a passive sonar only listens without sending out any wave.

Figure 5.4: Principle of active sonar

By measuring strength and round-trip time of these reflected sound waves, it can measure distance, speed, direction and size of any target object. A sonar detection beam used in vehicles is usually very short range and can detect other objects around a vehicle in very close vicinity. One example of such systems is Dolphin SonarStep

[187] that uses a sonar system to detect other objects within 7 feet.

The main advantages of sonar technology are its ability to find speed & direction etc by using Doppler shift analysis, and its ability to work under water and on the surface as well. The main disadvantages of this technology are its inability to work in presence of sound absorbent materials, and inaccurate distance measurements during wind gusts, snow and rain because of the fact that speed of sound varies in water, snow, and air.

5.3 Lidar

LIDAR stands for “light detection and ranging” or “laser infrared detection and

ranging”. It is also known as LADAR (laser detection and ranging). It uses either laser or infrared light to create an image of the environment. However, the basic working principles are same in both cases. Lidar has many applications in scientific research, defense, sports, production, and automotives etc. A lidar sends out hundreds of light pulses in a second which may hit some object on their way and a part of it is reflected back to the origin. It measures the characteristics of reflected light to calculate speed, distance and other information of the target object. A powerful lidar based on laser light may have a range of up to 25 kilometers.

It uses time of flight (ToF) method to calculate speed and distance of the target object. It may also use Doppler Effect technique for calculating speed and direction of the target object. A lidar can create image of the surrounding environment so as to make object recognition possible and to be used as night-vision support. Lidars are being used in vehicles in order to find distance from other vehicles in front of them.

An example of such systems is the one produced by Sick AG [193].

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The main benefits of lidar are its accuracy, low cost, ability to distinguish between the relevant traffic from irrelevant things such as obstacles and tin cans etc, and its ability to produce the image of environment for night-vision. The major disadvantages of lidar are its limited resistance to the interference by light in the surroundings, infeasibility for bad weather conditions due to dependence on lighting and limited field of view, and performance degradation by snow reflections as laser-based lidar operates in the optical range.

5.4 GPS

GPS [188] stands for “global positioning system” and was developed by US

Department of Defense. GPS consists of 24-32 satellites worldwide, at least four of which are always visible from any point on the earth. These satellites find their own location very precisely by communicating with ground stations at known places on earth and with each other. These satellites send their location as radio signals to the earth. GPS covers the whole earth and is the most widely used location system, especially, in navigation and tracking applications.

All GPS-enabled devices have a receiver which uses trilateration to find out its current position using satellite data. Lateration measures distance of the object from some known reference points using time-of-flight, direct touch, or signal attenuation information. Location information in 2D needs three reference points, while location information in 3D needs four reference points. Lateration in 2D is explained in figure

5.5 where a black dot represents the object, three white dots are the known reference points (i.e. position of satellites), and R1, R2, and R3 are distances between the object and known reference points.

Figure 5.5: Principle of Lateration in 2D

Any GPS-enabled device receives signals from three or more satellites and calculates its location using Lateration at an accuracy of 1-5 meters in open areas

(outdoor). A sequence of readings can be used by a mobile object to find its speed, direction of movement, and distance from a specific point.

GPS has been the prevailing location system in navigations, path finding, and tracking applications. It can be used to find the speed, direction, and location etc of our own vehicle but cannot directly find these characteristics for other vehicles. However, a cooperative environment can be established to share this information among all neighbors on the road. It is important to note that this information provision is not so fast that it could be used in driver assistance systems such as forward collision warning. However, intelligent speed adaptation can be implemented by using a GPS which provides current location of the vehicle, and a digital map of the area to determine speed limits for the current location.

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The main advantages of GPS are its global availability. The main drawbacks of

GPS are: GPS does not work fine in urban areas with high buildings etc; accuracy of location information provided by GPS is not very good; GPS is controlled by the US military which can degrade the service (Selective Availability); and receivers have high cost and fairly long start-up time (<45 seconds) [189].

5.5 Video-Based Analysis

Recently, vision-based driver assistance systems are becoming more popular. They are innovative, low-cost, high-performance, usable with new as well as old vehicles, independent from infrastructure outside the vehicle, and easy to develop, install, and maintain. They use either cameras – charge-coupled device (CCD) or complementary

metal-oxide semiconductor (CMOS) – to get a digital image of the surroundings of a vehicle. The captured video can be processed in real-time so as to calculate speed, direction, distance, and size & type of objects appearing in any image or frame. This information is sufficient to implement most of the functions of an advanced driver assistance system (ADAS). Additionally, a vision-based system opens many other possibilities such as road sign recognition, and driver’s drowsiness detection etc. A number of ADAS have been implemented using video-based analysis, such as

Wraparound View by Fujitsu [190] and EyeQ2™ by Mobileye [191].

In this section, we briefly explain the functioning of CCD & CMOS imagers, and how image-processing techniques can be used to implement a vision based ADAS.

5.5.1 CCD/CMOS Camera

A digital camera uses an image sensor device – either CCD or CMOS – that changes an optical image to an electrical signal. (Some examples of available image sensors and cameras are shown in figure 5.6 below). Both CCD & CMOS image sensors consist of an array of photo-diodes made from silicon and can sense only the amount of light but not its color, and then convert this light into electrons. For colored image, a colored filter (red, green or blue) is used for each pixel. After changing an optical image to an electrical signal in the first step, the next step which differs in CCD and CMOS is to read the value of charge stored in each cell of the image.

(a) 1/4-inch

CMOS Image

Sensor by Sony

(b) A small 8-mp

CMOS Camera by Samsung

(c) 1/3-inch

CCD Image

Sensor by

Kodak

(d) A small 2mp CCD

Camera by

Sharp

(e) An infrared enabled CMOS camera by Yanlab

Figure 5.6: Some examples of image sensors and cameras

(f) 7x7 pixel

CMOS camera with ultrawideband radar.

5.5.1.1 CCD

A charge-coupled device (CCD) is an analog device which stores light as tiny charges in each photo sensor. This electric charge is shifted across the chip at one

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corner and is read one pixel at a time. Here an additional circuitry with analog-todigital converter changes the voltage into digital value as shown in figure 5.7 below.

Figure 5.7: Image processing in CCD [192]

5.5.1.2 CMOS

A complementary metal-oxide semiconductor (CMOS) is an active pixel sensor in which each photo sensor has extra circuitry to convert light energy into voltage. On the same chip, an additional circuitry with analog-to-digital converter changes the voltage into digital value as shown in the figure 5.8 below. A CMOS has everything it needs to work within the chip making it “camera-on-a-chip”.

Figure 5.8: Image processing in CMOS [192]

5.5.1.3 Performance comparison

A number of parameters are used to compare the performance of different image sensors. These parameters include dynamic range – the limits of luminance range it can capture, signal-to-noise ratio (SNR or S/N) – the ratio of a signal power to the noise power, and light sensitivity – ability to work in darker environments, etc. CCD is more mature technology than CMOS. The performance of CCD was much better in the past. However, CMOS are improving to a point where they are performing almost equal to the CCD. A comparison is provided in the table 5.1:

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Table 5.1: Performance comparison of CCD and CMOS image sensors

Performance Parameters CCD CMOS

Dynamic range High

Noise

Light sensitivity

Low

High

Uniformity Better

Windowing (sub-regions) Limited support

Image rate (speed)

Image quality

Lower

High

Age of technology

Power consumption

Reliability

Pixel size

System size

Architecture

Moderate

Noisier (getting better)

Lower

Worse (getting better)

Fully supported

Higher

Lower (comparable now)

Mature

High (100 times)

Moderate

Smaller (better)

Newer

Low

High

Larger

Larger Smaller

External circuitry required All circuitry on one chip

Flexibility High

Signal type Analog

Low

Digital

Manufacturing

Cost / Price

Complex Simple

Expensive (little bit) Inexpensive

Example Applications Digital photography, broadcast-tv, industrial/scientific/medical imaging etc.

Cameras for mobile devices, computers, scanners, faxmachines, bar-code readers, toys, biometrics & vehicles etc.

In short, we can say that CCD has better quality, resolution and light sensitivity, but CMOS is also improving in these terms & is already a faster, smaller, cheaper, simpler and power efficient technology. CCD cameras are usually used as rear-view because they perform better in dark environment, whereas CMOS cameras are used for advanced driver assistance systems because of their higher image rate. The trends in the automotive industry show that CMOS cameras will dominate the market in future

[190] [191].

5.5.2 Working Principles

As we have described earlier, we need to capture the information on speed, distance, relative position, direction of movement, and size & type of the neighboring vehicles or other objects on the road in order to implement ADAS functions. In the following sections, we will show how single camera mounted on a vehicle can be used to measure these parameters. However, before that, we briefly describe some of the important principles using which these measurements are made.

5.5.2.1 Perspective Transformation

The perspective transform method is used for mapping any 3D object to a 2D surface such as paper or monitor. In a perspective view, the parallel lines in the scene that are not parallel to the display plane are projected into converging lines i.e. they

44

converge to a distant point in the background, and distant objects appear smaller than objects closer to the viewing position. This method is used to display a 3D scene on a

2D device without third dimension i.e. depth or distance. Therefore, when we take a picture of the real world in 3D, it is projected to a 2D device. A 2D image of the real world does not have depth. We need to translate it into 3D in order to measure distance of objects appearing in the picture. This calls for a reverse process which is known as

Inverse Perspective Transform (IPT). Using IPT, we can re-project 2D image onto a

3D ground plane which enables us to measure distance of each object in the picture.

5.5.2.2 Camera Parameters

captured by a camera is affected by many parameters which include angle of view, focal length, camera height, total number of pixels, motion blur, and exposure time etc.

A vision-based automotive system uses cameras installed on a vehicle. The image

Figure 5.9: Camera-lens parameters

When we take a picture, the area of scene covered by the picture is determined by the field of view (FOV) which defines the angle of view (α) of a camera lens. A wideangle lens can see wider & larger area but has lower resolution. However, they are also well suited for vision-based automotive applications [195]. A wider angle of view and higher optical power are usually associated with a shorter focal length; focal length (F) is the distance from a lens to its focal point (point of convergence of light). An image produced by a camera lies in the image plane which is perpendicular to the axis of the lens. The total number of pixels of an image in horizontal direction is called as image width. The pixel-width is the breadth of a pixel on a display device. When a picture is taken while moving, a motion-blur takes place in the dynamic region of the image because of the relative motion between the camera and the object. Exposure time or the shutter speed – effective length of time a camera-shutter is kept open in order to let light reach the film/sensor – plays an important role in blurring. The camera is usually installed at a location above the ground; the height from the ground at which a camera is installed is called as camera height. In automotive applications, it is useful to identify the point of contact which is a point where two things meet, e.g. bottom of the wheel where vehicle & the road meet.

5.5.2.3 Monocular vs. Stereovision

A stereovision-based system uses two cameras (tightly coupled) for any function.

Many stereovision-based systems for driver assistance have been proposed recently

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[74] [196] [197] [198]. However, a stereovision system is costly due to an additional camera, higher processing power requirements, and calibration problems

[199] [200] [201]. On the other hand, a monocular vision uses only one camera for its functioning. However, it lacks depth cues and required accuracy for automotive functions [202]. Recently, a number of monocular vision methods have been proposed for distance-measurement with a quite high accuracy [202] [231] [249] [253] [288] [289]

[290] [291]. Most of the modern research is focused on monocular vision, which will help in developing low cost and high performance automotive applications.

5.5.2.4 Image Processing

An image can reveal lot of information when we process it using a digital processor. Pattern matching is one of the most commonly used techniques which stores templates in the form of a hierarchy for efficient searching/matching. The main problem with pattern matching is that one object may have a range of appearances due to viewing angle, lighting conditions, and motion etc. However, we can use rotation, scaling, translation, gray-scale conversion, noise filtration etc in order to improve pattern matching process. Hundreds of image processing techniques have been developed which can be used for transformations, image enhancement, image segmentation, object representation or modeling, feature extraction, object recognition, distance & speed estimation, drowsiness detection, and scene understanding etc [203].

5.5.3 Object Recognition (size & type)

A two-step method is used for recognition of objects such as obstacles,

pedestrians, vehicles, road signs, and lane markings etc. First, a hypothesis is generated – hypothesis generation (HG) and then this hypothesis is verified – hypothesis verification (HV). That is, a supposition is made about the location of some object in the picture first and then the presence of that object is verified. A large number of methods exist for HG & HV which use different kind of knowledge about the object under consideration and are classified accordingly.

We can divide hypothesis generation (HG) methods into six classes:

1. Model-based or knowledge-based methods [204] use specific characteristics of an object such as shadow [205] [206] [207], corners [208], texture [209], color [210], light [211] [212] [213], symmetry [214] [215], and geometrical features [84]. A combination of these is also used for better performance. For example, Collado et al. [216] [217] use shape, symmetry, and shadow; Kate et al. [218] use shadow, entropy, and horizontal symmetry; Liu et al. [219] [220] use shadow, symmetry, and knowledge-based learning; and Hoffman [221] uses shadow, symmetry and

3D road information.

2. Stereo-based methods use dual camera based techniques such as disparity-map

[222] [223], and inverse perspective transform (IPT) [224] [225]. However, stereobased methods have high computational cost and low processing speed [221].

3. Motion-based methods use optical-flow [202] [226] [227] [228] [229] [230] or some other techniques such as Sobel edge-enhancement filter combined with the optical flow [231]. Optical-flow is the change of object position between two pictures.

The main issue with motion-based methods is that they feel difficulty in detecting objects when objects are either still or moving very slowly [226]. The optical-flow can be used for pedestrian detection as well, e.g. Bota and Nedesvchi [312] use motion cues such as walk in order to detect pedestrians.

4. Context-based methods use the relationship between the objects & the scene [232].

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5. Feature fusion-based methods merge two or more of the above methods [233].

6. Adaptive frameworks adjust or choose from many features depending on the situation at hand [11]. Adaptive frameworks prove to be a better approach. The major drawbacks of the previous approaches are that none of them is generic enough to handle all the situations, and change of environment (e.g. lighting, weather, traffic, etc) would significantly change the detection rate and errors.

We can divide hypothesis verification (HV) methods into three classes:

1. Template-based methods make use of correlation with existing patterns such as edges, corners, shapes and wavelet characteristics etc [84]. For example, Betke et al. [234] [235] utilized color, edge, and motion information for vehicle detection.

2. Appearance methods use feature extraction and classification techniques. Feature extraction discovers a set of characteristics of the object class using a set of training images. A number of techniques have been used for feature extraction, such as principal component analysis (PCA) [232] [236], local orientation coding

(LOC) [237], Gabor filter [238], scale invariant feature transform (SIFT) [239], and Haar wavelet [240] etc. For classification, a couple of methods have been used such as neural network [236] [237], statistical model [241], support vector machine

(SVM) [238], and horizontal Sobel filter based boosting method [242] etc. The performance of appearance methods is better than template-based methods.

3. Hybrid techniques combine two or more techniques in order to achieve better performance. For examples, Geismann and Schneider [243] use Haar features for detection, histograms of oriented gradients (HOG) and linear support vector machine (SVM) for classification of objects (pedestrians); Cao et al. [244] extract features such as appearance & motion, and use statistical learning & support vector machine (SVM) for classifying a pedestrians; they also measure speed and direction; Blanc et al. [245] use Support Vector Machine (SVM) and template matching algorithm for vehicle detection.

Object detection can be improved by adding some kind of object tracking mechanism in it. Objects on all the four sides of a vehicle can be detected by using one of the above mentioned methods. However, vehicles on the right & left sides can also be identified by detecting wheels [246] [247] [248]. One example of a complete system for object recognition & tracking is developed by Fritsch et al. [249] who use humanlike attention approach to process only small parts of image, known as Region of

Interest (RoI), apply hierarchical models of invariant object recognition [250] and classify objects on the bases of a confidence value using a threshold for rejection.

Object detection, especially in the blind-spot, is investigated by many researchers, e.g.

Mota et al. [251] detect blind spots using Reichardt correlator model, Wang et al. [252] use optical flow for this purpose, and Wu et al. [253] compare the gray intensity with the highway surface and use image coordinate model for distance measurements.

5.5.4 Road Sign Recognition

The road sign recognition also goes through two steps: sign detection, and classification. A sign detection technique identifies all the areas in a picture where some road-sign is expected or present, and then inputs these identified areas to the classification module in order to recognize the type of road signs. A very good recognition rate had been achieved a decade ago [254] and now its accuracy is improved to “almost” 100%.

A number of techniques have been designed for robust sign detection. The input stream can be minimized for fast processing by using a priori assumptions [256] about

47

the picture organization so as to ignore irrelevant parts of the image; for example, supposing that the road is almost straight. To facilitate the search for road signs in only limited part of the picture, we can also use color-segmentation [257] [258] as road signs have a special color, and scene understanding [254] as a road sign is expected on the sides of a road or overhead but not in the sky or on the road itself. Most of the road-sign detection algorithms use their features such as general shape, color, size or position etc. However, detection can be enhanced by identifying Region of Interest

(RoI) using perspective view and 3D modeling [259].

Moreover, for better classification, Fletcher et al. [255] applied super-resolution over multiple images, while others used pattern recognition such as regular polygon detector [48]. Some researchers consider road-signs as normal objects and use twostep method for object recognition – hypothesis generation (HG) & hypothesis verification (HV) – instead of sign detection and classification; they use Region Of

Interest (ROI) for HG and pattern recognition for HV [260] [261].

5.5.5 Lane Detection and Tracking

The lane detection and tracking is another important function in automotive applications such as lane departure warning and environment reconstruction etc. Laneboundaries are indicated by painted-lines, reflectors or magnetic markers embedded into the center of road. However, painted-lanes are more common because they are economical to make; and are easy to detect & track using camera because of their higher intensity. A video camera is installed in the front of a vehicle which can see the road for more then 25 meters depending on the range of camera. However, some old systems used downward-looking video camera [268], while others have used backward-looking camera [201] [219] [220].

Recently, a number of vision-based methods for lane detection & tracking have been developed which can be divided into three categories: feature-based methods, model-based methods, and hybrid methods.

In feature-based methods, a lane boundary is detected by its specific features such as color, contrast, edge, texture, or a combination of these. Examples of feature-based lane detection & tracking include edge-based detection [262] [263], color-based system

[264], multi-scale Gabor wavelets filters for texture-based detection [265], edge and texture based detection [266], and lane detection based on edge, texture, and vehiclestate information [267].

On the other hand, a model-based method represents or matches a lane boundary using some model. For examples, Dickmanns & Mysliwetz [269] find the position & curvature using a Kalman filter; Jochem [270] used neural network to find the lane positions; the RALPH system [271] – rapidly adapting lateral position handler – uses a template-based matching in order to discover parallel image features such as lane boundaries; LeBlanc [272] calculated the gradient of the intensity to find the lane boundaries; Bertozzi et al. [273] and Yong Zhou et al. [274] use inverse perspective transform (IPT) in order to re-project the image onto a ground plane (to make 3D model from 2D image) so as to detect lane boundaries, and GOLD system [275] – generic obstacle and lane-detection – uses inverse perspective transform (IPT) and intensity information. However, the GOLD system gives error when it comes across a zebra crossing. Therefore, it is improved by Kim et al [11] who apply IPT only on the candidates for lane boundary obtained through adjustable template matching (ATM)

[268] and utilize curvature information for robust tracing of the lane boundaries.

Similarly, Tsai [276] proposed a fuzzy inference system to avoid errors due to shadow;

48

Wang et al. [277] [278] used the spline curve to propose CHEVP algorithm –

Canny/Hough Estimation of Vanishing Points – to model the lane boundary points;

Gonzalez and Ozguner [279] used the histogram method; Jung & Kelber [280] and

Lim et al. [281] used linear-parabolic model to find lane boundaries; and Wang et al.

[282] [283] used peak-finding algorithm and Gaussian filter to detect lanes, and computed angle and distance between two lanes.

There are some hybrid approaches which combine many techniques, such as using

Hough Transformation & some road model [284] [285] or using gray scale statistics

(lane markings have higher gray value), dynamic range of interesting (ROI) and lane features for detection of lane boundaries [231]. The ROI-based hybrid approaches are efficient & popular which first find a region of interest (ROI), then find a real midpoint of the road lane to find candidates of lane markings, and finally use a temporal trajectory strategy to improve lane detection [286]. This method is very fast (62 frames per second) and accurate (robust to lighting changes, shadows, & occlusions etc).

Recently, some very light-weight methods have been proposed for lane detection, e.g.

Ren et al [287] have used a Hough transform to detect lanes using an iPhone.

Although object detection & tracking has improved a lot, the road-side structures such as buildings, tunnels, overhead-bridges, and billboards etc yet offer enhanced difficulty for recognition of vehicles, pedestrians, and road-signs.

5.5.6 Distance Measurement

Traditionally, radar, lidar or sonar is used to measure the distance of an object from the vehicle. The use of digital camera for distance measurement is relatively new approach and is getting popularity because of lower cost and multiple applications.

There are many cues that can be used for distance estimation, such as size & position of the objects, lane width, and point of contact of a vehicle & the road etc. The last cue is more useful because other cues have a very large variation, e.g. width of a vehicle may vary from 1.5 to 3 meters. We can also use perspective transform and many other techniques for measuring the approximate distance of an object using single camera.

Stein et al. [202] proposed a single-camera based method for calculating the distance to the vehicle as:

Z

=

fH y

Where H is the camera height in meters, f is the focal length, and y is the height of image-plane onto which the point of contact between the vehicle and the road (i.e. the wheels) is projected as shown in the figure 5.10 below. This gives a quite accurate measurement and gives only 5% error at a distance of 45 meters.

Figure 5.10: Imaging geometry for distance calculation [202]

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Liu et al. [231] use Sobel edge-enhancement filter combined with the optical flow to detect the vehicle in front and find the distance by using headway distance estimation model. The distance between the host and preceding vehicle can be calculated as:

d

2

=

P r

×

(

H

Y

×

HV f

c

Δ

2

R

)

d

1

Where d

2

is the distance to be measured, H is the camera height from ground, f its focal-length, d

1

is its distance to the front-tip of the vehicle hosting camera, P r pixel-width in the monitor or display device, ∆R is image width, and Y

HV c

is

is

is the coordinate of image in row direction of the preceding vehicle bottom end-point as shown in the figure 5.11 below.

Figure 5.11: Distance estimation model [231]

Recently, a number of camera-based methods have been proposed for distance measurement. Lamprecht et al. [291] propose a very simple method for measuring the distance to stationary points from a vehicle by tracking these points three times while considering the velocity of vehicle during a certain period of time. In the same way,

Shibata et al. [288] use only optical flow to measure distance and direction of an object using single camera. Optical-flow is the change of object position between two pictures. Fritsch et al [249] use human-like attention approach and consider only related parts of an image to find all objects of interest, and calculate distance of all the objects on road using EKF-based fusion (Extended Kalman Filter). Dagan et al. [289] have found a method for calculating the distance & relative velocity to the vehicle in front, and have used it for calculating time to collision or contact (TTC) in their collision warning system. Wu et al. [253] compare the gray intensity with the highway surface and use image coordinate model for distance measurements. Goto and

Fujimoto [290] use perspective model in order to find distance by means of square measure of the object in image plane.

The accuracy of distance measured by ordinary camera is not too high to be used in crash avoidance systems. Fortunately, there have been some efforts to incorporate radar capabilities into the CMOS camera so as to add 3D capabilities. For example,

Canesta's CMOS image chip (figure 5.12) automatically finds the distance to every object in a sight at once using time-of-flight calculations on each pixel [292]. The main

50

advantages of this technology are that it is highly accurate and works in all weather conditions.

Figure 5.12: Radar capable CMOS imager chip by Canesta

5.5.7 Speed & Direction (Velocity) Measurement

Speed is defined as the distance traveled per unit time, whereas velocity is the distance traveled per unit time in certain direction. The change of object position between two video-frames is known as optical-flow which is commonly used for measuring speed & direction of moving objects in a scene. Lucas–Kanade Algorithm

[293] is a two-frame differential method for optical flow estimation.

Li et al. [294] measure the speed of a vehicle by capturing two pictures immediately one after the other using a fixed CCD camera, whereas Martinez et al.

[227] use optical flow to find time to collision or contact (TTC) in order to detect head-on collision.

Tracking a moving object over a few seconds can help finding its speed. The speed of an object can be calculated from the discrete differencing of distances at different time instance i.e. we can easily get speed of the vehicle from successive measurements of distance. However, this method is not accurate because when we subtract one inaccurate value from another inaccurate value the result is always inaccurate. Similar approach is used by Stein et al. [202] who use single-camera based method for calculating the speed as:

w

w

Z v

=

Δ

w t

Where Z is the distance of target object, and w & w' are the width or height of the object in pixels at the start and end time respectively for the period ∆t.

Another innovative approach is based on motion blur, also known as smearing effect, which occurs because of the relative motion between the camera and the objects. Lin et al. [295] [296] have proposed a method to calculate object speed using a single motion blurred image. They have developed a mathematical model of linear motion blurring and a method to find the direction & speed of the moving object using single motion blurred picture. The relative speed is calculated using this formula:

v

=

Tf zKs x

cos

θ

Where z is the distance from camera to the object, K is the blur-length in pixels, s x is the width of a camera-pixel, T is the shutter speed & f is the focal length of camera, and θ is the angle when object is not moving parallel to the image plane as shown in the figure 5.13.

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Figure 5.13: Distance estimation using smearing effect [296]

Similarly, the smearing/blurring effect is also used by Cheng et al. [297] for computing the speed of the camera-carrier using a kinetic model to express the movement of the camera, target and the image.

As described in the previous section, radar-enabled CMOS camera can also find speed and direction of every object in a scene at once using time-of-flight calculations on each pixel [292].

5.5.8 Drowsiness Detection

The main signs of fatigue or drowsiness are human head position and eye closure

[298]. However, drivers’ vigilance or attention is different from fatigue; driver may be looking off the road or involved in some other activity while being fully awake. A single camera mounted on the dashboard, for example, can be used to track eye & head in order to find visual attention of the driver [299] [300].

Heitmann et al [301] use facial expression, eye-lid movement, gaze orientation, and head movement for detecting fatigue. A custom-designed hardware system [302] or a simple camera with infrared illuminators for dark environments can be used for this purpose [303] [304]. Flores et al. [305] track face and eyes in order to detect drowsiness. In recent times, Albu et al. [306] have used event detection approach to monitor the eye-state using their template-matching algorithm.

The drowsiness detection methods successfully trigger an alarm for about 90% of the time which is not very much accurate. More research in this area will produce some highly accurate methods in near future.

5.5.9 Environment Reconstruction

The video output of a camera can be directly displayed to drivers, which may not be really useful for them. However, we can reconstruct the environment by processing images form all the cameras around the vehicle and provide a bird’s-eye view which is very much useful for drivers because it provides them a quick overview of the surroundings. Many systems have been introduced which provide an overview of the surrounding area of the vehicle [11] [72] [79] [80] [83] [307] [308] [309] [310] [311].

Our proposed Smart-Dashboard system also provides the bird’s-eye view in addition to other functions of ADAS using monocular cameras.

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5.5.10 Pros and Cons

Camera-based automotive applications have many advantages including low cost, high availability, multiple applications and ability to integrate with other systems.

However, there are some serious drawbacks of camera based solutions such as weather dependency, and lower accuracy. The camera-based automotive applications are still in development phase and will take few more years to gain reliability. A timeline

(table 5.2) provided by one of the vision industry leader – Mobileye [191] – explains it well.

Table 5.2: A timeline for camera-based automotive applications by Mobileye.com

Year Development

Up to now Lane Departure Waning,

Radar-Vision Fusion,

Traffic Sign Recognition.

Late 2009 360 o

Multi-camera View,

Mid 2010 Lane Departure Warning,

Radar-Vision,

Vehicle Detection,

Intelligent High Beam Control,

Pedestrian Detection

Vehicle Detection,

Forward Collision Warning /Mitigation.

2011

2012

2012

Intelligent Headlight Control, Traffic Sign Recognition.

Headway Monitoring.

Fully functional vision-based ADAS

5.6 Conclusions

In this chapter, we provided a brief survey of technologies that can support our proposed Smart-Dashboard system. We have found that ADAS functions can be implemented by capturing the information on speed, distance, relative position,

direction of movement, and size & type of the neighboring vehicles or other objects on the road. For this purpose, many technologies are available in the market, such as

RADAR, LIDAR, Sonar, GPS, and Video-Based Analysis etc.

Our proposed system uses Video-Based Analysis, which requires ordinary CMOS cameras. The camera-based solutions have low cost, high availability, multiple applications and ability to integrate with other systems. We have briefly described in section 5.5 that a large number of camera-based techniques are available for detecting the speed, distance, relative position, direction of movement, and size & type of objects on the road. Depending on the power of digital image processor, more than 30 frames can be processed in one second for automotive applications. We believe that all the required technology is now available for implementing camera-based ADAS.

Camera-based systems are generally considered inaccurate & inappropriate in poor visibility conditions such as fog, dust, rain, and particularly snow. However, many efficient techniques are now available for bad weather conditions. Moreover, infrared or radar enabled CMOS cameras are available now which can better solve these issues.

They are more expensive than ordinary cameras at present, but will become cheaper very soon.

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6 T

HE

S

YSTEM

D

ESIGN

The design of our Smart-Dashboard system is inferred from the statements presented in previous chapters. Our design puts technologies – such as camera, digital image processor, and thin display – into a smart system in order to offer advanced driver assistance functions. Given that drivers may not be well versed in computing skills, we will consider designing an easy to use, smart, and adaptive system that requires minimum input from the user but leaves maximum control in the hands of users. From the drivers’ point of view, the system should provide them almost all the

ADAS functions (see section 1.5.1) in an unobtrusive way. They should be able to get assistance in maintaining a safe speed & safe distance, avoiding any collision, keeping them alert, recognizing road signs, detecting blind-spots, keeping their lane, identifying pedestrians, enhancing their vision at night, and warning them of the dangerous situations.

6.1 Introduction

Driving is a very common activity of our daily life. People drive their cars for travel or pleasure. They make use of all the available technological support in order to avoid any accident. They make best estimate of the position & velocity of other object on the road, and guess the distance of their car from others’ using rear-view & sideview mirrors or any other available technological support such as sonar and rearview camera etc. However, this puts an extra burden on the driver. Our design moves all of these tasks from drivers to the system and minimizes undue burden on humans.

Equipped with the camera technology, our proposed Smart-Dashboard system monitors its surroundings and processes video frames in order to find distance, velocity, position, and size & type of all the neighboring objects. This information is then used by different ADAS modules to assist the drivers and to generate bird’s-eye view of the surroundings. In short, drivers will be provided with all the assistance required for safe & smooth driving.

6.2 Components of the System

Applications. We use five-layered architecture of context-aware systems [315] as shown in the figure 6.1 below.

Smart-Dashboard system has three components: Hardware, Middleware, and the

Application Layer

Context-aware applications & services

Management Layer

Store, Share, Distribute, and Publish context

Semantic & Inference Layer

Preprocessing of context

Data Layer

Raw Data Retrieval and Processing

Physical Layer

Sensors and other objects

Middleware

Figure 6.1: Layered architecture of context-aware systems [315]

54

The image sensors or video cameras are present at Physical Layer. These image sensors capture real-time video of the surrounding environment. The captured video frames are instantly sent to the middleware where they are preprocessed for inferring useful information. The information produced by middleware is then provided to the application modules at uppermost layer.

6.2.1 Hardware (Physical Layer)

There are three major hardware components of the system: five CMOS cameras, a digital processor, and a TFT-LCD display (thin film transistor liquid crystal display).

The system is equipped with four ordinary CMOS cameras installed on all the four sides of vehicle (see figure 6.2), whereas the fifth camera is installed inside the vehicle.

Figure 6.2: Smart-Dashboard system with five cameras

(5

th

camera is inside)

One of the CMOS cameras is installed in the front of vehicle between the rearview mirror and the windscreen. This does not block view of the windscreen as it is attached behind the rear-view mirror. A similar camera is installed in the back of the vehicle. A wide-angle CMOS camera is installed on each of the right & left sides of vehicle. These cameras are attached to the side-view mirrors or somewhere above them. This arrangement not only provides 360 o

or all-around coverage of the surrounding areas but also allows two cameras on each side to see into blind spot simultaneously. A wide-angle camera will enable the system see the lane markings or other objects that are very close to the sides of vehicle. The fifth camera is installed on the dashboard inside the vehicle that will look at driver for drowsiness and attention.

The system uses a digital processor (ordinary computer or digital signal processor chip) for applying image-processing techniques on video frames in order to get required information for ADAS system modules.

The 360 o

or all-around view is then processed for environment reconstruction and is displayed on TFT-LCD display mounted on the dashboard behind the steering at

DIM as shown in the figure 6.3 below.

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Figure 6.3: Preferred places for a display

( www.volvocars.com

, 2009)

There are four preferred places where any display can be mounted [319]: Head Up

Display (HUD), Driver Information Module (DIM), Rear View Mirror (RVM), and

Infotainment Control Module (ICM). We put our display at DIM location. However, it could also be projected on HUD location if some projection device was available. Ours is an adaptive display that can be used for many purposes according to the context.

This display can be used for speedometer, odometer, temperature, time, fuel-gauge, and other vehicle data.

6.2.2 Middleware

A middleware is the software part of a context-aware system that lies between the hardware and the applications. It provides the following functionality in general

[313] [314]:

1. Support of a variety of sensor devices including multimedia devices,

2. Support of the distributed nature of context information,

3. Providing for transparent interpretation of applications,

4. Abstraction of context data,

5. Maintenance of context storage,

6. Control of the context data flow,

7. Providing support for the mobility in presence of different constraints such as lowbandwidth, network partitions, poor coverage, limited resources, asynchronous communication, and dynamic execution environment etc,

8. Providing support for the system adaptability,

9. Using the best available resource such as bandwidth and place of computation etc

The video frames captured by the five cameras at physical layer are instantly sent to the middleware for noise removal, enhancement, transformation, fusion etc. These frames are then processed for calculating the distance, speed, direction, position, and size & type of all the objects appearing in a scene as explained in the previous chapter

(see section 5.5). This processed information is then pushed up to the application modules at application layer.

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6.2.3 Applications

Our Smart-Dashboard system provides a number of application modules for driver assistance. These application modules provide almost all the ADAS functions listed in section 1.5.1. The implementation details of these modules are given in the following sections.

6.3 Design Considerations

Now we describe a number of issues/considerations related to the design of our proposed Smart-Dashboard system.

6.3.1 Information Requirements

Before we can provide any ADAS function, we need to have information on distance, speed, direction, position, and size & type of all the relevant objects appearing in a scene. Based on our argument in previous chapters, we believe that these pieces of information are enough to build a full-fledged camera-based ADAS.

For example, to implement forward collision warning system, we need to know only the relative speed and distance of the vehicle in front.

It is important to note that we do not need to store any information for longer time because a real-time system processes and uses instant information. However, for navigational support (not included in our proposed system), we can save some information on routes, road-sign locations, accidents etc.

6.3.2 Camera Positions

Selecting a proper location for mounting a camera is an important issue in camerabased automotive applications. These cameras should be able to see the environment without any blockage. A camera in front is required to capture the road curvature, lane boundaries, road-signs, vehicles and other objects. A camera in the rear is required to detect lane-boundaries and objects in the blind-spots & behind the vehicle. The cameras on two sides of the vehicle are required to detect objects in the blind-spot and on both sides of the vehicle. In this way, a blind-spot on each side of the vehicle is covered by two cameras with some overlapped view i.e. one camera on the left/right side and another camera in the rear of vehicle.

The front and the rear cameras are mounted on the windscreens inside the vehicle for security and performance reasons. The cameras on two sides are embedded into the side-view mirrors or at some upper location so that they can see the road directly below them. The fifth camera is installed on the dashboard inside the vehicle that will look at the driver for drowsiness and attention.

6.3.3 Issuing an Alert

On detecting some dangerous situation, the system should issue an alert. However, it is important to choose from different types of alerts or warnings. There are four kinds of alerts issued by different automotive applications: auditory, visual, haptic, and automatic (i.e. takeover the control from driver). We will consider visual and auditory alerts only because our system employs only image sensors.

57

close vicinity on the TFT-LCD display. However, in dangerous situations, it will issue auditory alerts using beeps of low or high volume depending on the level of danger.

By default, the system will blink symbols for vehicles and other objects in very

6.3.4 User Interface

While installing a display, it must be a prime consideration that the display is viewable & within the reach of driver. There are two main issues regarding user interface: placement (i.e. where to put it) and the mode (i.e. to use either buttons or touch-screen).

There are four possible locations for mounting the display as for as placement is concerned (see Figure 6.3). However, in our case we can opt from two locations only; on the windscreen (Head-Up Display – HUD), or inside the dashboard behind steering

(Driver Information Module – DIM). As we do not use any kind of projection device,

DIM is the best suitable place for display. It is within drivers’ visual approach and is reusable for displaying other information such as speedometer, tachometer, rear-view, navigation maps, speed or fuel-level etc.

The most appropriate mode of interaction is the touch screen where user can touch the screen in order to make selections. The user interface screen will provide following options to the drivers on starting a vehicle:

1. Change my settings – (it will have 3 sub-options) a. Change level of expertise – learner, beginner, experienced, or expert. b. Type of warnings to be issued – none, auditory, visual, or both audio-visual c. Volume of the sound for auditory alerts – anywhere from silent to loud

2. Remove my settings – (users known by face-recognition; have different settings)

3. Start camera calibration – (required at the time of installation or after a damage)

These options will appear for a few second on the startup and then disappear in favor of default settings. However, on touching the screen, these options will appear again. The default settings are as follows: level of expertise = experienced, type of warning = both audio-visual, volume of the sound = medium.

The system will mange and remember settings for each user by identifying their face through the fifth camera installed on the dashboard. The level of expertise for any user will be automatically raised while she gains experience with the passage of time.

Similarly, if the initial value entered by any user is “expert” but she makes many mistakes on the road, the system will learn that the user is not an expert in fact and will lower her expertise level accordingly.

6.3.5 Human-Machine Interaction

A smart system is required to be context-aware, intelligent, proactive and minimally intrusive. We see our proposed system as a smart system that engages in two-way interaction with the users. We need to regulate the evolving interaction carefully in order to realize unobtrusive and seamless interaction.

We have attempted to give users maximum control over the system. A user can initiate interaction by touching the screen. However, five cameras make the system aware of its users and context. This awareness makes it possible to adapt the system

58

according to the situation and minimize the annoyance by lowering the level of input required of the user. For these reasons, the system should continuously learn from the interactions and use this learning in future decisions. For example, the system should automatically update the expertise level of the driver with the passage of time.

6.4 System Design

The Smart-Dashboard system uses single integrated display (multipurpose & adaptive) instead of several displays (one for each ADAS function). This display is highly adaptive and shows the highest priority information at any instance of time. For example, at startup, it shows options’ screen; on recognizing some traffic signs, it displays them; and for most of the time, it displays reconstructed environment along with speedometer etc. Different modules in the system can be assigned priorities so that the highest priority module will use the display in case there is any contention.

It adjusts the size of each component of the display to fit them all on one screen as shown in the figure 6.4 below.

(a) Options displayed at startup

(b) Traffic signs and the speedometer etc.

(c) Reconstructed environment and the speedometer etc.

Figure 6.4: An integrated and adaptive interface of Smart-Dashboard

The Smart-Dashboard system implements a number of ADAS functions. This section explains how different components of the system work together in order to achieve the design goals. Figure 6.5 provides an overview of the Smart-Dashboard system.

59

Update the Display Issue Respective Warning

Adaptive

Cruise

Control

Traffic Sign

Recognition

Intelligent

Speed

Adaptation

Blind Spot

Detection

Forward

Collision

Warning

Pedestrian

Detection

Lane

Departure

Warning

Parking

Assistance

Adaptive

Light

Control

Night

Vision

Driver

Drowsiness

Detection

Environment

Reconstruction

Distance Speed Direction Position Size & type Etc…

Noise removal

Image enhancement

Transformation Image fusion Etc…

Image Sequence Input

Figure 6.5: Overview of the Smart-Dashboard system

The image sensors at the physical layer of Smart-Dashboard system capture realtime video, the middleware layer performs some pre-processing, and the application layer provides ADAS functions.

In section 5.5 of the previous chapter, we have already listed a number of camerabased methods for object recognition (i.e. vehicle, pedestrian, and obstacle recognition), road sign recognition, lane detection and tracking, distance measurement, speed & direction (velocity) measurement, driver drowsiness detection, environment reconstruction, and so on. Using these camera-based methods, we provide a system design for individual ADAS functions in this section.

6.4.1 Adaptive Cruise Control (ACC)

Adaptive Cruise Control system automatically slows down the vehicle when it approaches another vehicle in front and accelerates again to achieve the preset speed when traffic allows. Traditional ACC systems use laser or radar technologies to measure the distance and speed of the vehicle in front. However, we have proposed a camera-based implementation of ACC in the figure 6.6.

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Image Sequence Input

Preceding Vehicle Detection

No

Vehicle Found?

Yes

Find Vehicle Speed

Find Local Speed

Find Headway

Distance

Find Braking Time or

Time to Contact (ToC)

Too Close?

Yes

No

Reduce Speed

/ Issue Warning

Figure 6.6: Adaptive Cruise Control system

After finding the speed of vehicle in front, it finds the local speed and the headway distance. It issues a warning and/or reduces the local speed in order to avoid forthcoming collision if Time to Contact (ToC) is too small. Here, we implement vehicle detection using only camera-based methods as shown in the figure 6.7 below.

Image Sequence Input

Lane

Detection

Pre-processing

Candidate

Extraction

Candidate

Validation

Vehicle Tracking

(or other objects)

Vehicle Classification

(or other objects)

Figure 6.7: Vehicle detection

The vehicle detection module extracts some candidates, validates them, and then tracks those candidates for some time by getting help from lane detection module. At the end, it classifies objects as, for example, bicycle, motorcycle, car, bus or truck etc.

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6.4.2 Intelligent Speed Adaptation/Advice (ISA)

Intelligent Speed Adaptation system continuously observes the vehicle speed & the local speed limit on a highway and advises or takes an action when the vehicle exceeds the speed limit. Traditionally, a GPS is used to determine the local speed limit on a road, but we have proposed a camera-based implementation of ISA in the figure

6.8 below.

Image Sequence Input

Detect Speed-limit Sign

No

Sign Found?

Yes

Find Local Speed-limit

Find Vehicle Speed

Too Fast?

Yes

Find Following Distance

No

Reduce Speed to the Limit

& Issue Warning

Figure 6.8: Intelligent Speed Adaptation system

The Intelligent Speed Adaptation system looks for any speed-limit sign on the road, and compares the speed limit with the speed of vehicle. If the vehicle is too fast, it issues a warning and reduces the vehicle speed while keeping an eye on the vehicles behind to avoid rear-collision.

6.4.3 Forward Collision Warning (FCW) or Collision Avoidance

Forward Collision Warning system detects objects on the road that would otherwise go un-noticed and warns its driver of any possible collision with them.

Traditional systems use infrared and radar technologies to detect objects on the road, but we have proposed a camera-based implementation of FCW in the figure 6.9.

Humans are not good at calculating distance and speed of different objects on the road. The FCW system detects any objects in the same lane, calculates distance between the object and the vehicle, and issues a collision warning in order to avoid accident if the distance is quickly becoming shorter than a threshold value. In this way,

FCW will issue collision warning only when it finds that the vehicle will collide with another vehicle or object if it continues to move with the current speed.

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Image Sequence Input

Lane Detection

Lane Info

Preceding Object

Detection

Object Found?

Yes

Identify Object Type

No

Object Tracking

Distance Estimation

Too Close?

Yes

No

Issue Collision Warning

Figure 6.9: Forward Collision Warning system

6.4.4 Lane Departure Warning (LDW)

Lane Departure Warning system constantly observes the lane markings and warns a driver when the vehicle begins to move out of its lane while its turn signal in that direction is off. A similar system, Lane Keeping Assistance (LKA), helps driver in keeping the vehicle inside a proper lane.

Traditional LDW systems use light or magnetic sensors to detect reflections from reflectors or magnetic field produced by the embedded magnetic markers respectively.

However, we have proposed a camera-based implementation of LDW in the figure

6.10.

The LDW system detects and tracks lane markings, predicts lane geometry, finds any deviation from path, and issues lane departure warning or lane keeping action while keeping an eye on the vehicles behind to avoid rear-collision.

This system works even in the absence of lane markings. In this case, it assumes virtual lanes of about three meters width.

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Image Sequence Input

Lane Detection

Assume Virtual Lanes of About 3m Width

No

Markings Found?

Yes

Lane Tracking

Predict Path or Geometry

Find Deviations

Find Corrections Required

Need Corrections?

No

Yes

Find Following Distance

Issue Lane Departure Warning or Lane Keeping Actions

Figure 6.10: Lane Departure Warning system

6.4.5 Adaptive Light Control

Adaptive Light Control system moves or optimizes the headlight beam in response to a number of external factors such as vehicular steering, suspension dynamics, ambient weather, visibility conditions, vehicle speed, road curvature, contour etc.

Traditional ALC uses a large number of electronic sensors, transducers & actuators.

However, we have proposed a camera-based solution for finding environmental factors for ALC as shown in the figure 6.11.

Image Sequence Input

Detect

Approaching

Vehicle

Detect

Environmental

Lighting

Find Local

Speed

Found?

Yes

No

No

Dark?

Yes

Dim Lights Bright Lights

Fast?

Yes

No

Lane Detection

Path Prediction

No

Turning?

Yes

Bend Lights

Light Controller

Figure 6.11: Adaptive Light Control system

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The ALC system determines driving context and sends this information to the headlight controller for adaptive actions. It detects any approaching vehicle, environmental lighting, local speed of the vehicle, and path turnings in order to adapt headlights accordingly.

6.4.6 Parking Assistance

A Parking Assistance system helps drivers avoid any collision while parking their vehicles. Some systems takeover the steering and actively park a vehicle, while others provide a live view of the surroundings and issue a warning in case of forthcoming collision. Such systems are new to automobiles and usually use cameras. We also propose a camera-based PA system as shown in the figure 6.12.

Image Sequence Input

Image Fusion

Live View on Display

Detect Objects All-Around

No

Objects Found?

Yes

Identify Object Type

Object Tracking

Distance Estimation

Too Close?

No

Yes

Issue Collision Warning

Figure 6.12: Parking Assistance system

them to find their distance, and issues a collision warning in order to avoid any collision if the vehicle is very close to the objects.

The PA system identifies any objects in the very close proximity of vehicle, tracks

6.4.7 Traffic Sign Recognition

Traffic Sign Recognition system identifies the road traffic signs and warns the driver to act accordingly. Traditional TSR systems use GPS or radio technologies to determine the traffic signs. However, we have proposed a camera-based implementation of TSR as shown in the figure 6.13.

The TSR system selects a region of interest (RoI), finds & tracks any candidates, extract features, and classify sign. It then shows this sign on the display if valid.

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Image Sequence Input

Select Region of Interest (RoI)

Candidate Detection

No

Any Candidate?

Yes

Track Candidate

Feature Extraction

Sign Classification

Valid Sign?

Yes

No

Show Sign on Display

Figure 6.13: Traffic Sign Recognition system

6.4.8 Blind Spot Detection

Blind Spot Detection system helps avoid accidents when changing lane in presence of other vehicles in the blind spot. It actively detects vehicles in the blind spot and informs the driver before taking a turn. Traditional systems use sonar, radar, or laser to detect vehicles in the blind spot. However, we have proposed a camera-based implementation of BSD as shown in figure 6.14.

Image Sequence Input

Lane Detection

No

Assume Virtual Lanes of About 3m Width

Lane Found?

Yes

Make Region of

Interest (RoI)

Detect Vehicles

Vehicles Found?

No

Yes

Find Distance, speed, direction, size & type

Update Display &

Issue Warning

Figure 6.14: Blind Spot Detection system

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The BSD system first finds the lane markings, detects any vehicles in the blind spots, finds their speed, distance, direction, size and type, and issues warning and updates the display according to the reconstructed environment.

6.4.9 Driver Drowsiness Detection

Driver Drowsiness Detection system detects a drowsy or sleeping driver and awakes him to avoid any accident. Traditional systems use a number of sensors such as stress sensor for finding grip on steering, sensors to find heart-beet, blood pressure, and temperature of the driver. However, we have proposed a simple camera-based implementation of DDD that detects eye closure as shown in the figure 6.15.

Image Sequence Input

Face Detection

Eye Detection

Eye State

Tracking

Eye Closed for n Frames?

No

Yes

Issue Warning

Figure 6.15: Driver Drowsiness Detection system

The DDD system first detects human face, then eyes, and then tracks eye state. It issues a warning if it finds closed eyes in more than n consecutive frames (n is usually near 10).

6.4.10 Pedestrian Detection

A Pedestrian Detection system identifies any human walking on or near the road and alerts the driver to avoid any collision. Traditional systems use sonar, radar, or laser technology. However, we have proposed a camera-based implementation of PD as shown in the figure 6.16.

Humans are the most valuable assets on the road and the governments in near future will enforce pedestrian detection systems. Therefore, future cars will have PD as compulsory module.

Our proposed PD system works like a TSR system. It detects human by symmetry or motion. However, it also issues warning and highlights pedestrian symbol on the display if someone is very close to the vehicle.

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Image Sequence Input

Select Region of Interest (RoI)

Candidate Detection

No

Any Candidate?

Yes

Track Candidate

Validate Pedestrians

Find Distance etc

Very Close?

Yes

No

Issue Warning and

Show on Display

Figure 6.16: Pedestrian Detection system

6.4.11 Night Vision

Night Vision system helps a driver in seeing objects on the road during night or poor weather. Traditional systems use infrared or radar technology to detect objects on the road and use a projector for head-up-display (HUD).

However, we have proposed a camera-based implementation of NV as shown in the figure 6.17. We use ordinary CMOS camera for object detection and a TFT-LCD display for showing these objects.

Image Sequence Input

Lane Detection

Vehicle Detection

(& other objects)

Find distance, speed, direction and size & type of each object

Highlight the Objects on Display

Figure 6.17: Night Vision system

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6.4.12 Environment Reconstruction

Environment Reconstruction system identifies all the neighboring objects on the road including lane markings, and vehicles, and finds their speed, distance, direction, size & type. It then reconstructs the environment and draws it on the display. The idea of environment-reconstruction is very new and fuses camera and some other type of sensors such as infrared, radar and laser. We have proposed a camera-based implementation of ER in the figure 6.18 (a), and figure 6.18(b) shows the sample output. The ER system identifies lanes and other objects around the user (encircled vehicle) and reconstructs the environment on a display.

Image Sequence Input

Lane Detection

Vehicle Detection

(& other objects)

Find distance, speed, direction and size & type of each object

Reconstruct the Environment

Show on the Display

(a) Environment Reconstruction system (b) The reconstructed environment

Figure 6.18: Environment Reconstruction system and the Display

6.5 Implementation

A full-fledge implementation of the proposed system is out of the scope of this thesis. However, we show a basic prototype using ordinary CMOS cameras (8 megapixels) and a laptop (P-4, 2 GHZ, and dual-core). We use the built-in models available in “Video and Image Processing Blockset” [317] provided by MATLAB (R2007a or newer versions). We demonstrate only three functions: Human/Pedestrian Detection,

Traffic Sign Recognition, and Lane Departure Warning.

In Pedestrian Detection system, the input video is processed to identify background, detect humans, and track these humans as shown in the figure 6.19 below.

(a) Input Video (b) Background (c) Human Detected (d) Human Tracked

Figure 6.19: Pedestrian Detection using built-in MATLAB model [317]

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is recognized. The “Stop” sign is highlighted in the video and a text message is displayed as shown in the figure 6.20 below.

In Traffic Sign Recognition system, the input video is processed and a “Stop” sign

(a) Input Video (b) “Stop” Traffic Sign Recognized

Figure 6.20: Traffic Sign Recognition using built-in MATLAB model [317]

In Lane Departure Warning system, a departure on left or right lane markings is detected and an audio-visual warning is issued. This is done by continuously observing the distance of vehicle from the center of a lane and is also plotted on a graph as shown in the figure 6.21.

(a) Lane Departure on Left Side (b) Lane Distance Signal

Figure 6.21: Pedestrian Detection using built-in MATLAB model [317]

The objective of this basic prototype is only to demonstrate that a full-fledged camera-based ADAS system can be implemented using MATLAB or any other programming tools available. However, implementation of the system is out of the scope of this thesis.

6.6 Conclusions

In this chapter, we explained the design of our proposed Smart-Dashboard system, which uses layered architecture of context-aware systems. The system is not really a calm technology; however, it serves the user silently until there is a warning to be issued. This system is hackable in the sense that many innovative uses can be found, capturing on video the beautiful landscape of a hill station or creating a 3D view of the streets you visited. We also provided the detailed design of each of the ADAS module.

Finally, we demonstrated a very basic prototype using built-in MATLAB models to show how fast & easy it is to implement any camera-based ADAS function.

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7 C

ONCLUSIONS

Road accidents cause a great loss to human lives and assets. Most of the accidents occur due to human errors such as bad awareness, distraction, drowsiness, low training, fatigue etc. An advanced driver assistance system (ADAS) can help drivers avoid accidents by minimizing these human errors. An ADAS actively monitors the driving environment and produces a warning or takes over the control in dangerous situations. The main features of ADAS include parking assistance, forward collision warning, lane departure warning, adaptive cruise control, driver drowsiness detection, and traffic sign recognition etc. Unfortunately, these features are provided only with modern luxury cars because of their high cost. These systems use numerous sensors that make them complex and costly. Therefore, researchers have shifted their attention to camera-based ADAS functions nowadays. Aiming at developing a camera-based

ADAS system, we carried out an ethnographic study of how people drive their vehicles and the factors affecting their actions while driving. We observed drivers’ activities while driving, engaged them in discussions, and sent out questionnaires to selected people all over the world. We were particularly interested in finding answers to our research questions (section 1.3); and here is a brief description of the answers:

1. Contextual information for drivers’ awareness: We found that it would be very useful for drivers to avoid accidents if we would provide them with the information on speed, distance, relative position, direction, and size & type of the vehicles or other objects around them. These five pieces of information are enough to build a full-fledged camera-based ADAS and can be captured using different technologies. We did a survey of all the supporting technologies including radar, sonar, lidar, GPS, and video-based analysis. We found that video-based analysis is the most suitable technology for this purpose because it provides all the required support for implementing ADAS functions in a simple way and at a very low-cost.

2. Distraction-free presentation of the information: For the presentation of information to the drivers in a distraction-free way, we process this information to reconstruct the environment and draw the birds’-eye view on a display mounted on the dashboard, behind the steering, and just in front of the driver.

3. User-interface and human-machine interaction: To ensure simple and easy user interface, we make our system context-aware and hence adaptive. It requires minimal input from the users, but gives maximum control over the system. The system uses a touch-screen display and issues only audio-visual alerts to make the interaction simple and easy for drivers.

In this thesis, we have proposed a camera-based ADAS (i.e. Smart-Dashboard system) using layered architecture of context-aware systems. This chapter identifies some of the strengths, weaknesses, and future enhancements in our proposed system.

7.1.1 Strengths

Our proposed Smart-Dashboard system is a camera-based system, which has a number of strong points.

First, the proposed Smart-Dashboard system provides almost all the functions of

ADAS entirely based on five cameras installed on a vehicle. Many innovations are

71

being introduced in cameras everyday. For examples, infrared enabled cameras can see at night as well; cameras with microphone can listen as well; and radar-enabled cameras can generate 3D pictures of the environment [292]. These innovative cameras will soon become cheaper like an ordinary camera. In addition, we have seen many innovative applications of a camera in the last few decades. Having installed cameras on a vehicle opens doors to many innovative applications to come in the future.

Second, the cost of a camera-based ADAS is much lower than other technologies.

The cost of a CMOS camera is only a few dollars, starting from US$ 15 for ordinary camera and US$ 20 for an infrared-enable camera. The popularity of cameras in mobile phones and other handheld devices has encouraged producers to design cheaper, smaller and efficient cameras.

Third, camera-based ADAS systems had poor performance few years ago.

However, camera technology has significantly improved nowadays which makes it possible to design high performance automotive functions.

Fourth, a camera-based ADAS system does not depend on any infrastructure outside the vehicle. For example, lane departure warning can work even if there are no visible lane markings on the road or magnetic markers embedded. Additionally, it can be used with new as well as old vehicles having no support for infrastructure.

Fifth, a camera-based ADAS is very simple to implement i.e. to develop, install & maintain. This is because the area of video & image processing has been around us for decades; and the proposed algorithms are much accurate and faster. These algorithms, with slight modifications, can be used in the development of camera-based ADAS.

Today, a large number of camera-based techniques are available for detecting the speed, distance, relative position, direction of movement, and size & type of objects on the road.

Sixth, a camera-based ADAS is more intelligent than a system based on radar, sonar or lidar. It can distinguish between the relevant traffic from irrelevant things such as obstacles and tin cans etc. It is also possible to incorporate learning & prediction capability using techniques such as scene analysis. Moreover, a camerabased system can host multiple applications and has ability to integrate with other systems as well.

Seventh, the camera-based ADAS system has very high availability. It uses cameras, processor, and a display. The CMOS cameras are readily available in the market and are very easy to install & operate. Similarly, processors and displays are also easily available at lower costs.

Eighth, a camera can scan much wider area as compared to other technologies. For example, radar has a very limited field-of-view (about 16 has 46 o o

), whereas a normal camera

field-of-view and a wide-angle camera has a field-of-view as wider as 180 o

.

Moreover, a camera faces no interference from absorbents like a radar or sonar.

However, it is interfered by lighting conditions, such as reflections, like a lidar. Poor lighting, bad weather, and high illuminations etc also affect its performance.

Nowadays, to overcome these issues, we have cameras with wide dynamic range

(WDR) [316] of more than 120dB that can handle both bright and dark environments by automatic adjustments. For example, the picture in figure 7.1(a) is not very clear because it was taken in a very bright environment using an ordinary camera. However, the same picture in figure 7.1(b) is very much clear when taken by a wide dynamic range camera. Likewise, the figure 7.1(c) is also taken by a wide dynamic range camera, which shows a road scene at night where everything can be seen very clearly.

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(a) Image captured without WDR (b) Image captured with WDR (c) A night scene captured with WDR

Figure 7.1: Imaging without (a) & with (b, c) wide dynamic range (WDR) [316].

Nine, the capabilities of a CMOS camera can be increased by fusing some other kind of sensing into it. For example, radar-enabled CMOS camera can also find speed and direction of every object in a scene at once using time-of-flight calculations on each pixel [292].

Finally, a camera-based system can easily be integrated with other systems.

However, this integration becomes smoother & easier if all components are camerabased. For example, a camera-based lane departure warning system can be integrated with traffic sign recognition system to share the same camera between them.

7.1.2 Weaknesses

Although the Smart-Dashboard system is designed very carefully, it has many weaknesses. Some of these weaknesses are inherited from the technology while others are inherited from design itself.

First, the performance of Smart-Dashboard system is affected by the bad weather conditions such as fog, dust, rain, and particularly snow. This is because the visibility is severely reduced during bad weather. However, infrared-enabled or radar-enabled cameras will remove this weakness in future.

Second, the proposed system has lower accuracy of speed & distance measurements when compared to radar, sonar or lidar. This is because a camera cannot accurately measure the speed & distance of an object if it is too slow or too close to the camera. Again, infrared-enabled or radar-enabled cameras will improve accuracy of the measurements.

Third, the Smart-Dashboard system uses LCD display to show reconstructed environment. It is important to note that a driver can pay only a little attention to the displayed information while driving. A driver may be distracted while looking at the display for more than a few seconds. To avoid this problem, we can use a projector for head-up-display (HUD), but it will significantly increase the cost of our proposed system.

Four, as the proposed Smart-Dashboard system uses five cameras, the issue of privacy cannot be overlooked. A camera inside the vehicle might be invasive for driver, and the cameras outside the vehicle might be invasive for neighboring travelers on the road. Furthermore, the possibility of adding new applications into the system can make it possible to record every movement of the neighbors.

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Finally, the Smart-Dashboard system requires some tuning in the beginning (at the time of installation only) because the camera parameters should be determined for accurate measurements of speed, distance etc.

7.1.3 Future Enhancements

Since we spent limited time and resources on Smart-Dashboard project, many deficiencies are present in it. Therefore, a number of future enhancements are possible in the system.

First, we have described the design of all ADAS functions for Smart-Dashboard system. However, our prototype implements only three of them, namely

Human/Pedestrian Detection, Traffic Sign Recognition, and Lane Departure Warning.

We can implement all the remaining functions in future.

Second, the Smart-Dashboard system does not learn from user actions at present.

However, we can incorporate learning in its future version.

Third, with few enhancements, the Smart-Dashboard system is useable for training a new driver. This requires some more functions in order to advise a new driver and control the vehicle in case of emergency.

Finally, camera-based automotive systems are still in the development phase and will take few more years to have reliable applications for automobiles. Recently, infrared-enabled & radar-enabled CMOS cameras with high accuracy and reliability are available. Fusing radar and vision sensing will make Smart-Dashboard system very much accurate and reliable in the future.

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A

PPENDIX

A

A1 – Questionnaire

The following survey was put online and could be accessed through public

URL: http://www.surveygizmo.com/s/124388/aug-drive during the period it was active.

Augmenting safe and smooth driving

Introduction:

• Annually, road accidents cause about 1.2 million deaths, over 50 million injuries, and global economic cost of over US$ 518 billion.

• I'm conducting this survey for my research on "Augmenting safe and smooth driving". The data collected will be used only for academic purpose and will not be given to any third party.

• Please answer all of these questions carefully. This will take only a few minutes but will be a valuable contribution to save lives on road. I'll be extremely thankful to you for your extended cooperation ... (Muhammad Akhlaq)

1. What kind of car do you own or drive (now or in the past)?

Latest car with many safety features (2008 or above model)

Relatively new car (1996-2007 model)

Old car (1985-1995 model)

Very old car (before 1985 model)

2. Does your car have any modern safety features? (e.g. Night Vision, Parking

Assistant, Traffic Sign Recognition, or Blind Spot Detection etc)

Yes

No

3. Is there any kind of video display mounted on your car’s dashboard

(e.g. for GPS navigation or CD/DVD, or inside your car’s speedometer etc)?

Yes

No

4. For how long can you drive continuously without taking any rest or break?

Less than 1 hour

1 – 2 hours

2 – 4 hours

More than 4 hours

5. Do you use mobile phone, laptop or other hand-held computers while driving?

Never (i.e. I keep it switched off)

Sometimes

Often

More than often

75

6. For what purpose do you use mobile phone while driving, if needed?

(Select one or more options)

Messages ( i.e. SMS or MMS)

Phone calls

Games and playing audio/video clips

Photography and audio/video recording

Others (Please specify …)

7. Reading an SMS while driving requires how much of your attention?

No attention

Very less attention

High attention

A very high attention

8. Which of the following MOSTLY distracts you from driving (i.e. draws your attention away from driving)?

Things outside the car (such as too much or fast traffic, police, sun, animals, accident, construction, bad-road etc )

Things inside the car (such as adjusting radio, cassette, CD, mirrors, AC, & wipers etc )

Personal state (such as thinking, tiredness, sleepy, being depressed, happy, upset or relationship problem etc )

Activity (such as eating, drinking, smoking, talking etc )

9. While driving, what makes you more worried / upset about the surroundings?

(Select one or more options)

Too much traffic

Uneven, curvy and damaged roads

Too many crossings, bridges and footpaths

Vehicles which are too close to me

Vehicles which are too fast

Heavy traffic such as trucks and busses around me

Motorcycles and bicycles

People and animals crossing the road

Others (Please specify …)

10. In your opinion, what is the most common reason for road accidents?

Human factors (such as inattention, over-speeding, drinking, drowsiness, tiredness, violation of traffic laws etc…)

Road defects (such as potholes, narrow lanes, bridges, crossings, sudden turns, slipperyroad and too much traffic i.e. rush etc)

Vehicle Defects (such as break-failure, steering-failure, tire-burst, headlights-failure etc)

Others (please specify…)

11. What was the reason for road accident you have recently faced or seen?

Driver was distracted from the road i.e. his attention was dispersed.

76

Driver felt sleepy or tired

Driver changed the lane without taking care of traffic on the road

Driver could not recognize a road sign such as speed limit or no overtaking

Another vehicle or person suddenly appeared

Something wrong with the car (such as failure of breaks, tire-burst etc)

Others (please specify…)

12. After windscreen, which of the following locations is easiest to see while driving?

Speedometer

Button/control area in the middle of dashboard

Side mirrors

Back-view mirror

Other (Please specify ... )

13. In your opinion, what information about other vehicles on the road can be helpful for drivers to avoid accidents? (Select one or more options)

Distance in meters

Speed

Direction

Size and Type (i.e. human/animal, bicycle/motorcycle, car/van, bus/truck etc)

Relative position

Others (please specify …)

14. In time of danger, what kind of alert should be issued? (Select one or more options)

Auditory

Textual or Visual

Haptic (e.g. shake the driver seat if sleeping)

Automatic or takeover the control from driver (e.g. automatically apply brakes to avoid collision etc)

Others (please specify …)

15. WRITE YOUR COMMENTS HERE (If any):

(Note: Include your Email address if you are interested in results)

Submit this Survey

Online Survey powered by SurveyGizmo.com

77

A2 – Response Summary Report

The results of the above survey was collected by using the same online tool i.e.

SurveyGizmo.com. Here is a brief report of the results.

Report: Response Summary Report

Survey: Augmenting safe and smooth driving

Compiled: 04/26/2009

1. What kind of car do you own or drive (now or in the past)?

2. Does your car have any modern safety features?

(e.g. Night Vision, Parking Assistant, Sign Recognition, Blind Spot Detection etc)

3. Is there any kind of video display mounted on your car’s dashboard

(e.g. for GPS navigation or CD/DVD, or inside your car’s speedometer etc)?

4. For how long can you drive continuously without taking any rest or break?

78

5. Do you use mobile phone, laptop or other hand-held computers while driving?

6. For what purpose do you use mobile phone while driving, if needed?

(Select one or more options)

7. Reading an SMS while driving requires how much of your attention?

8. Which of the following MOSTLY distracts you from driving (i.e. draws your attention away from driving)?

79

9. While driving, what makes you more worried / upset about the surroundings?

(Select one or more options)

10. In your opinion, what is the most common reason for road accidents?

11. What was the reason for road accident you have recently faced or seen?

12. After windscreen, which of the following locations is easiest to see while driving?

80

13. In your opinion, what information about other vehicles on the road can be helpful for drivers to avoid accidents? (Select one or more options)

14. In time of danger, what kind of alert should be issued?

(Select one or more options)

15. WRITE YOUR COMMENTS HERE (If any):

(Note: Include your Email address if you are interested in results)

ID Comments

27977884

Everyone believes he/she is a better driver than they actually are. Notice that everyone driving slower than you is an idiot and everyone faster is a maniac. please share the results of the survey with me [email protected]

27978649

Nice survey. Hope you can get it published in soft or hard media and distribute it to spread awareness among common people

27990528 I have no car but I have bicycle but the rules are same for every body, [email protected]

27995563 In addition, drivers need to be taught the importance of patience. Thanks, My email: [email protected]

27995908 Something is missing in the questionnaire. It needs customization to the local Saudi Arabia.

28002714 Good luck, excellent subject matter and hoping that you may contribute to this society. e-mail:[email protected]

28009536 please send me the results when the survey is complete [email protected]

28010774

In my opinion in one should have a fully fit vehicle, one should leave early so not to drive fast, be in a comfortable state of mind and physique, should not use mobile phone while driving, watch out for others making mistakes and constantly keep looking in side and back view mirrors. [email protected]

28021300

Some points from above can themselves be dangerous in many cases, e.g. in Q20 if automatically brakes are applied, car can slip if speed is over or car can be hit from back. … Best way is that drivers keep control and stay focused and technology may be introduced for better results simultaneously. Best of luck.

28041273

Talking about Pakistan is a very different matter. Here motorways/highways are built without any planning. In

Islamabad, a network of highways has been built but there is no underpass for the pedestrians. on some places, there

81

ID Comments

are overhead bridges but less than 5 percent of the pedestrians use it and you can see them coming on the highway and creating problem. Further more these overhead bridges have been built ignoring the people on cycle or people who are handicap. Some people may think that it may be cost a lot if people are told to get driving classes from a training school would be expensive but I think it must be implemented as 90% of the people driving don’t have traffic sense.

28070733

Knowing about your surroundings, e.g. person standing back of car while you are driving back will be helpful.

However, at the same time please note that only give information which is needed and only when it is needed.

28066469

In my personal opinion, most accident happens when driver is assuming something and it didn't happen. Like Cars in front stopping suddenly, or Car doesn't start moving, ( e.g. on yield or stop sign car at front doesn't start moving as expected). Volvo has already introduced features like alerts and automatic car stopping (city safety) and I think these are good features. For long drives, on highways, I would like the car to maintain the lane automatically. Some solution to blind spot or if I am changing lane without knowing that other car is too close. Email address: [email protected]

28076690 this survey contains too many same sort of questions.... ask things like..... 1)seat belts 2)do u follow traffic rules

3)loud music can be a factor of negligence etc

28069362

There should be lot of safety step to avoid "Accident or Collision", it is a serious Problem & we should take it [email protected]

28213734

This online survey is not applicable for a professional driver ( e.g. taxi driver, truck driver, goods transporter driver etc) The question of this survey are specific to normal/family driver, can not be applicable to heavy traffic driver .

(the have different driving parameters such as load etc. ) --Ishtiaq ([email protected])

28209423

As I mentioned above, an automatic control can be made to alert all drivers to apply brakes in a circle of an expected accident in order to avoid any accident by means of some wireless/radio transmission of information between the vehicles etc.

28223473

It was nice and short survey, Drivers in Pakistan never use low beams at night, and I saw many accidents because of it. Good luck, looking forward for results. e-mail is: [email protected]

28242874 do send me the results, [email protected]

28254131

Very interesting and useful research. Please send me result after completion. My e-mail address is: [email protected]

28256098 a very nice survey... briefly covered almost all the things in the topic

28260232 What u asked is really good but few things are missing.....

28261026

I am professor of computer sciences, I will be interested in segmentation of the people based on their different driving behaviors. [email protected]

28264337 I would like to see the results Thanks - from Pakistan [email protected]

28264631 It’s all about the traffic controller if they enforced the people to follow the laws.

28271339

This is a good topic it may be helpful in controlling traffic and reducing accidents in our country. [email protected]

28334937 Akhlaq sahib, wish u best of luck. hanso, mazay karo aur khush raho. my email: [email protected]

28375823

The main issue with people is lack of education and or caring for rules to avoid accidents. Ego, carelessness, ignorance etc cause most accidents. At least over here in Pakistan you always have to drive with the supposition that your neighboring drivers are reckless and will suddenly make a mistake - endangering you or others around you - therefore you are able to react quickly and avoid damage. More helpful (in my opinion) questions that should have been included are: 1. What is your age-group? 2. Do you posses a driving license? 3. Education level? 4. Does your car have seatbelts and airbags? 5. Do you always put on a seatbelt? I am interested in getting the results. My email

Address is: [email protected]

28436009

Question 18 is difficult to understand. Kindly update so that it could be interpenetrated easily. My email is: [email protected]

28527882 have fun please :) and don’t drive fast; drive slowly and nicely :)

28527927 thanks for this survey I think it will be helpful in future

28527901 Thanks 4 the survey .. it's really interesting

28528047 BE CAREFUL

28527934 well, thanks and I hope every thing be good for me and family and my friends.

28532998 Several questions need selection of multiple choices, whereas only Radio Button is used. [email protected]

28533204 I would see the results please contact me on : [email protected] thanks

28533829 the accident still with the people it is will not done maybe decreased [email protected]

28822198

Making drivers aware of their environment can significantly reduce chances of accidents. Accidents mostly occur due to negligence of drivers in one or the other way.

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B

IBLIOGRAPHY

[1]

[2]

[3]

[4]

Peden, M., Scurfield, R., et al, eds, “World report on road traffic injury prevention”,

World Health Organization, Geneva, 2004, http://www.who.int/violence_injury_prevention/publications/road_traffic/world_rep ort/en/index.html

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