ADVANCEMENT IN AUTOMOTIVE NIGHT VISION SYSTEM

ADVANCEMENT IN AUTOMOTIVE NIGHT VISION SYSTEM - M42MAE

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ADVANCEMENT IN AUTOMOTIVE NIGHT VISION

SYSTEM

Submitted by,

SHIHABUDEEN RAJELA IMRAN

Student ID-5914621


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SUMMARY

Night vision is one of the major advancement in vehicle safety systems. It enables

the better visibility of the field in which vehicle is driven during the night time. Studies

report that, there is only quarter of the all travel by car drivers is undertaken a night drive,

but still, 40% of the road accidents happens during night time. This makes night vision

system demanding for drivers assist during poor light or during night time.

The major reason for night accidents is poor visibility of the field of driving due to the

limitation in head light range and the dazzling of high beam headlight from the vehicle that

approaches from the opposite direction. Though night vision system available in the market

minimizes the occurrence and consequences of automobile accidents, it is not 100%

efficient for the ease and pleasure of driving for the old aged drivers. Since its display is

limited to a small screen which provides only a monochrome output, the driver doesn't tent

to depend on night vision all the time.

With this paper work we are trying to highlight the advancement of night vision which can

convert the present monochromatic display to a colourised one and help driver with a better

assist.

http://en.wikipedia.org/wiki/Automobile_accident


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ACKNOWLEDGEMENTS

Our endeavour stands incomplete without dedicating our sincere gratitude to

everyone who has helped a lot towards the successful completion of our course work. First

of all we offer our thanks to for their blessings. We are indebted to God Almighty for

blessing us with his grace and taking our dream to a successful completion. We specially

acknowledge our tutor guide Yuri Vershinin for the guidance given to us and steering us to

the successful completion of our coursework in time.


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TABLE OF CONTENTS:

INTRODUCTION ..................................................................................................................6

1.NIGHT VISION SYSYTEM ............................................................................................6

2.NIGHT VISION SYSTEM I AUTOMOBILE.................................................................7

2.1 INFRARED PROJECTORS......................................................................................8

2.2 NIGHT VISION CAMERA.....................................................................................9

2.3 IMAGE INTENSIFIER............................................................................................9

2.4 INFRARED SENSORS..........................................................................................11

2.5 NIGHT VISION PROCESSING UNIT..................................................................12

3. WORKING OF AUTOMOTIVE NIGHT VISION SYSTEM.......................................13

4. ADVANCEMENT IN NIGHT VISION SYSTEM........................................................14

4.1 PEDESTRIAN DETECTION SYSTEM................................................................14

4.1.1 CHARACTERSTICS OF IR DOMAIN...........................................................15

4.1.2 WORKING OF PEDESTRIAN DETECTION SYSTEM...............................15

4.1.3 PEDESTRIAN DETECTION ALGORITHM..................................................17

4.1.3.1 CONTOUR BASED CANDIDATE AREA EXTRACTION...........18

4.1.3.2 CANDIDATE AREA CLASSIFICATION.......................................21

4.1.3.3 CANDIDATE AREA TRACKING...................................................22

4.2 INTELLIGENT VISION FOR AUTOMOBILES AT NIGHT..............................22

4.2.1 WORKING OF IVAN......................................................................................25

4.3 TRUE COLOR NIGHT VISION............................................................................30

4.3.1 DESCRIPTION OF CAMERAS......................................................................32

4.3.1.1 LIQUID CRYSTAL FILTER INTENSIFIED CAMERA................32

4.3.1.2 EMCCD/MOSAIC CAMERA..........................................................34

4.3.2 FUSION FOR TRUE COLOR NIGHT VISION.............................................36

CONCLUSION.....................................................................................................................39

5. DISCUSSION...............................................................................................................39

6. CONCLUSION.............................................................................................................41

7. RECOMENDATIONS..................................................................................................42

SUPPORTING MATERIALS...............................................................................................43

8. REFERENCES..............................................................................................................43

9. BIBLOGRAPHY.........................................................................................................44

10. APPENDIX..................................................................................................................44


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TABLE OF FIGURES:

IMAGE 1: INFRARED PROJECTOR....................................................................................8

IMAGE 2 : NIGHT VISION CAMERA.................................................................................9

IMAGE 3: IMAGE INTENSIFIER.......................................................................................10

IMAGE 4 : PATH OF PHOTON MULTIPLIER..................................................................11

IMAGE 5 : INFRARED SENSORS......................................................................................11

IMAGE 6 : NIGHT VISION PROCESSING UNIT.............................................................11

IMAGE 7 : NIGHT VISION SYSTEM IN A CAR..............................................................13

IMAGE 8 : CIRCUIT DIAGRAM OF NIGHT VISION SYSTEM.....................................13

IMAGE 9 : AUTOMOTIVE PEDESTRIAN DETECTION SYSTEM................................16

IMAGE 10 : FLOW CHART OF PEDESTRIAN DETECTION SYSTEM.........................16

IMAGE 11 : FLOW CHART PEDESTRIAN DETECTION ALGORITHM.......................17

IMAGE 12 : IMAGE EXTRACTED CONTOUR................................................................18

IMAGE 13 : DIVIDE OF CONTOUR GROUP...................................................................19

IMAGE 14 : DIVIDE OF CONTOUR GROUP...................................................................20

IMAGE 15 : GROUPING OF BODY PART........................................................................20

IMAGE 16 : SYSTEM OVERVIEW OF IVAN...................................................................23

IMAGE 17 : IMAGE FROM DIFFERENT CAMERA........................................................24

IMAGE 18 : IVAN SYSTEM................................................................................................25

IMAGE 19 : IVAN ELLIPSE VERIFICATION PROCESS................................................26

IMAGE 20 : DETECTION STABLIZATION ALGORITHM.............................................27

IMAGE 21 : ROAD SIGN IMAGE ENHANCEMENT.......................................................27

IMAGE 22 : ROAD SIGN FEATURE EXTRACTION.......................................................28

IMAGE 23 : ADAPTIVE INFRARED CAMERA...............................................................28

IMAGE 24 : SIMPLE CASE ROAD SIGN DETECTION...................................................29

IMAGE 25 : MONOCHROME &COLOR LOW LIGHT LEVEL IMAGERY...................31

IMAGE 26 : IMAGE TAKEN WITH TCNV CAMERAS...................................................31

IMAGE 27 : LIQUID CRYSTAL FILTER...........................................................................32

IMAGE 28 : TCVN PROTOTYPE WITH LC FILTER.......................................................34

IMAGE 29 : EMCCD/MOSAIC...........................................................................................35

IMAGE 30 : PLOT OF SPECTRAL RESPONSE................................................................38


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INTRODUCTION:

1. NIGHT VISION SYSTEM.

Night vision system is the technology developed for the clear visibility of the field of an

object during the night time or under poor light. Night vision technology was first

developed for the military activities. Later on the technology was adopted in commercial

purpose such as for automobiles and aircrafts.

Night vision system works based on the infrared rays. Infrared rays are a property of light

which falls under the wavelength of 700 nanometre to 1 millimetre. The infrared rays are

divided into three categories; Near Infrared, Mid Infrared and Far Infrared or Thermal

Infrared. The key difference between Thermal-IR and the other two is that thermal-IR is

emitted by an object instead of reflected off it. Infrared light is emitted by an object because

of what is happening at the atomic level. Ever particle reflects light. While considering a

particle in its atomic level, when it is subjected to light, some particle absorb light in the

form of heat and some reflects it back at a varying wavelength. These reflected photons may

not come under the visible range of the humans, as human eyes have its limitations when

compared to animal, but sensors can do. Also, anything that is alive uses energy, and so do

many inanimate items such as engines and rockets. Energy consumption generates heat. In

turn, heat causes the atoms in an object to fire off photons in the thermal-infrared spectrum.

The hotter the object, the shorter the wavelength of the infrared photon it releases. Thermal

imaging takes advantage of this infrared emission. An object that is very hot will even begin

to emit photons in the visible spectrum, glowing red and then moving up through orange,

yellow, blue and eventually white. These reflected and emitted radiations which come under

the infrared regions are detected by IR sensors and cameras to generate a monochromatic

image that gives a better visibility of the field of view during low light.

Presently, there are two types of Night Vision technologies on the market, Far Infrared

(FIR) and Near Infrared (NIR). As stated above, FIR detects the radiation which all objects

emit, while NIR detects the reflected illumination in a frequency just outside the visible

range of a human being. This paper will analyse the requirements of a Night Vision system,

how NIR and FIR today perform under the defined condition and proceed to discuss

directions for future development.


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2. NIGHT VISION SYSTEM IN AUTOMOBILE

All cars today have an acceptable ‘night vision’ system. That is, the high beam headlights of

the vehicle. Even though they could be improved, their performances are at least acceptable.

However, in many areas, high beams are of very limited use due to oncoming traffic. The

insufficient night-time visibility originates in the fact that the high beam headlights are

rarely possible to use. A Night Vision system must therefore be a system that increases

visibility in situations where only low beam headlights can be used. Studies report that,

there is only quarter of the all travel by car drivers is undertaken a night drive, but still, 40%

of the road accidents happens during night time. This makes night vision system demanding

for drivers assist during poor light or during night time.

The major reason for night accidents is poor visibility of the field of driving due to the

limitation in low beam head light range and the dazzling of high beam headlight from the

vehicle that approaches from the opposite direction. Though night vision system available

in the market minimizes the occurrence and consequences of automobile accidents, it is not

100% efficient for the ease and pleasure of driving for the old aged drivers. This condition

thus define the importance and need to implicated technologies for the safety of pedestrians

during the night time, and better aid for the driver to understand his field of view at a

comfortable level.

The short detection distances for especially dark objects under low beam conditions versus

the corresponding situation under high beam condition illustrate the detection distance

deficiency that a Night Vision system should overcome. Safe driving speed should allow the

driver to detect, react and stop in time before any obstacles on the road. However, most

motorists actually drive faster than the visibility range allow with low beam headlights.

http://en.wikipedia.org/wiki/Automobile_accident


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Table 1: Detection distances to dark vertical objects, typical for present realistic night

driving conditions

The present night vision system used is automobile is a combination of NIR with image

intensifier and FIR with thermal imaging. The night vision system uses an infrared

projector, a camera, a processing unit and a display.

2.1 INFRARED PROJECTORS

Night vision system works on the principle of infrared rays. Infrared rays are invisible light

rays which a human eye cannot capture. These infrared rays are generated using infrared

LEDs and infrared laser beams. The LEDs are used for NIR system while the infrared laser

is used for FIR to get a long range view. Now, in modern cars with night vision system, the

infrared projectors are attached along with the head lights rather than being placed

separately.

Image 1: Infrared Projector


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2.2 NIGHT VISION CAMERA

The present night vision cameras used in automobile applications are very compact and

easy to accommodate. Some car manufacturers make a built in night vision system in their

cars. But some offer it as an added choice for the customer.

Image 2: Night vision camera.

Like normal DSLR cameras, the night vision camera consist of a lens part often known as

image intensifier and a photon detecting sensor which can sense IR radiation.

2.3 IMAGE INTENSIFIER

Image-enhancement technology is what most people think of when you talk about night

vision. In fact, image-enhancement systems are normally called night-vision

devices (NVDs). NVDs rely on a special tube, called an image-intensifier tube, to collect

and amplify infrared and visible light. In night vision system, a conventional lens, called

the objective lens, captures ambient light and some near-infrared light. The gathered light is

sent to the image-intensifier tube. The image-intensifier tube has a photocathode, which is

used to convert the photons of light energy into electrons. As the electrons pass through the

tube, similar electrons are released from atoms in the tube, multiplying the original number

http://science.howstuffworks.com/light-microscope1.htm


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of electrons by a factor of thousands through the use of a micro channel plate (MCP) in the

tube. A MCP is a tiny glass disc that has millions of microscopic holes (micro channels) in

it, made using fibre-optic technology. The MCP is contained in a vacuum and has metal

electrodes on either side of the disc. Each channel is about 45 times longer than it is wide,

and it works as an electron multiplier. When the electrons from the photo cathode hit the

first electrode of the MCP, they are accelerated into the glass micro channels by the 5,000-

V bursts being sent between the electrode pair. As electrons pass through the micro

channels, they cause thousands of other electrons to be released in each channel using a

process called cascaded secondary emission. Basically, the original electrons collide with

the side of the channel, exciting atoms and causing other electrons to be released. These

new electrons also collide with other atoms, creating a chain reaction that results in

thousands of electrons leaving the channel where only a few entered. An interesting fact is

that the micro channels in the MCP are created at a slight angle (about a 5-degree to 8-

degree bias) to encourage electron collisions and reduce both ion and direct-light feedback

from the phosphors on the output side. At the end of the image-intensifier tube, the

electrons hit a screen coated with phosphors. These electrons maintain their position in

relation to the channel they passed through, which provides a perfect image since the

electrons stay in the same alignment as the original photons. The energy of the electrons

causes the phosphors to reach an excited state and release photons. These phosphors create

the green image on the screen that has come to characterize night vision. The green

phosphor image is viewed through another lens, called the ocular lens, which allows you to

magnify and focus the image. The NVD may be connected to an electronic display, such as

a monitor, or the image may be viewed directly through the ocular lens.

Image 3: Photon multiplying phenomenon of the photon received in image intensifier

http://computer.howstuffworks.com/fiber-optic.htm

http://electronics.howstuffworks.com/tv2.htm

http://computer.howstuffworks.com/monitor.htm


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Image 4: The figure demonstrates the path of one electron multiplying through one of the

millions of channels in the MCP. With each bounce the electron multiplies and accelerates.

2.4 INFRARED SENSORS

Infrared sensor is the device that detects the infrared radiation in the light band. These

sensors are made out of silicon material which gets excited when the infrared rays falls over

it. These sensors can sense the infrared rays ranging from the wavelength 700 nanometre to

1 millimetre. They can sense both NIR and FIR and provide varying electric signal for the

detected photons of varying wavelength. These electric signals are amplified and processed

to generate graphic signals and displayed on an output device.

Image 5: Infrared sensor.


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2.5 NIGHT VISION PROCESSING UNIT

The night vision processing unit is the main part of a night vision system, it process the

signal obtained from the infrared sensors to the digital visual signal. The night vision

processing unit determines the conditions of the field and do the required amplification of

the signal to give a better output. Now there are more complicated control unit which can

perform a verity of functions such as reducing the noise level in the output, spotting height

intensity lights in the field of ride and screening them to not to cause bright spot in the

display screen etc.

The advanced night vision processing unit work along with the other safety and driver assist

systems available in automobiles to provide an intelligent night vision system.

Image 6: Night vision processing unit


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3. WORKING OF AUTOMOTIVE NIGHT VISION SYSTEM

In car night vision system, during low light, the infrared projectors project the IR rays on the

field of driving. The infrared LEDs emits photons towards the field, these rays are reflected

by the surrounding. These reflected rays are captured by the night vision camera in the car

and is detected by IR sensors. The signal is then converted to image signals and which is

displayed through the display unit.

Image 7: Night vision system in a car.

Image 8: Circuit diagram of Night vision system.


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4. ADVANCEMENT IN NIGHT VISION SYSTEM

Over the years the automotive night vision technology has evolved a lot, now the night

vision is an intelligent vision system which can act upon base on the situation and inform

the driver like a co-driver. The influence of power electronics and navigation has made the

night vision system more comfortable for night drive.

4.1 PEDESTRIAN DETECTION SYSTEM

The capability of observing the world through visual information is a strong requirement

for future driver assistance systems since their dues are getting more complex. Especially,

driver assistance systems dedicated to reduce the number of fatalities and severities of

traffic accidents impose several requirements on the sensorial system. One of the major

and challenging tasks is the detection and classification of pedestrians.

Naturally, the use of visual cameras is a promising approach to cope with the demands of

pedestrian detection. Several different image processing methods and systems have been

developed in the last few years, including shape-based methods, texture and template

based methods, stereo, as well as motion clues. But none of this is efficient in detecting

pedestrian during the night time as the works based on the light.

In order to facilitate the recognition process and to enable the detection of pedestrian in

dark environments passive infrared (IR) cameras have come into focus. Some first

pedestrian detection systems for IR images and videos have been developed

demonstrating the potential and benefits that IR cameras can provide.

Pedestrian detection using IR rays uses FIR or thermal infrared detection to identify the

pedestrian or animal in the field. Every living thing and working engines generates energy

in the form of heat radiations. During the night time, the non-living thing in the

surrounding environment stays cool. This provides suitable condition for the thermal

image sensors to detect the source that emit heat radiation.


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4.1.1 CHARACTERIZATION OF IR DOMAIN

Images in the IR domain convey a type of information very different from images in the

visible spectrum. In the IR domain the image of an object relates to its temperature and the

amount of heat it emits but is not affected by illumination changes.

Generally, the temperature of people is higher than the environmental temperature and their

heat radiation is sufficiently high compared to the background. Therefore, in IR images

pedestrians are bright and sufficiently contrasted with respect to the back-ground, thus

making IR imagery suited to their localization. Other objects which actively radiate heat

(cars, trucks etc.) have a similar behaviour; however people can be recognized thanks to

their shape and aspect ratio.

One major point in favour of IR cameras is the independency to lighting changes: IR

cameras can be used in day-time or night-time with no or little difference extending vision

beyond the usual limitations of day-light cameras. Moreover, the absence of colours or

strong textures eases the processing towards interpretation. Furthermore, the problem of

shadows is greatly reduced.

4.1.2. WORKING OF PEDESTRIAN DETECTION SYSTEM

The main deal with the pedestrian detection system is to identify the presence of

pedestrians or animal nearby the field of driving and to predict and inform and warn the

drive based on the behaviour of the identified object. For this, a serious of processing and

calculations are been done by the night vision control unit to determine the position,

behaviour and size of object. All these things are been done with the help of real time image

processing.

Since the vehicle is in continuous movement, tracking the position of the detected object

is a bit complicated task. For this the image processing unit uses multiple frames of images

at an in travel of time, and relates with the vehicle seed to determine the relative position of

the object.

The ratios of the polar coordinates of the detected images at consecutive intervals

determine the size and type of the identified object.


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When an object is detected, a bounding box appears on the screen to indicate the position

of it in the output screen.

Image 9: Automotive Pedestrian detection system

Image 10: Flow chart of pedestrian detection system


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4.1.3. PEDESTRIAN DETECTION ALGORITHM

The process flowchart of the newly proposed pedestrian detection algorithm is shown in

Image 11. In many cases of night time pedestrian detection, processing methods based on

image binarization are used because the intensity (i.e. temperature) of pedestrians is higher

than that of background objects. However, at daytime or in bad weather, making

assumptions on the intensity is not always effective because of environmental influences on

FIR images (Table 2).

Image 11: Flow chart of pedestrian detection algorithm

Table 2: Features of FIR images


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4.1.3.1 Contour-based candidate area extraction

The method of contour-based candidate area extraction uses the intensity difference

between a pedestrian and the background and a constrained condition of distances to

pedestrian body parts. The constrained condition is based on the assumption that the

distance between FIR camera and each pedestrian body part (head, arms, torso, and legs) is

the same. However, the contour of a pedestrian is not always a continuous line and is

usually disconnected at every parts of the body. Therefore, the candidate area extraction

method is made of two steps: (1) Extraction of body part areas, and (2) grouping of body

part areas, as shown below.

(a) Extraction of pedestrian body part areas

Image 12: Extracted contour (Prewitt operator)

First, the contours of the pedestrian and the back-ground are extracted from the FIR image

(Image 12). In consideration of the cases of bad weather where blurred images are obtained,

the Prewitt operator is used so that smooth contours can be extracted. Next, neighbouring

contour points are connected and contour groups are constructed. The reason of this process

is to prevent the occurrence of errors at the following disparity segmentation process. The


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constructed contour groups may include the contour points that belong to different objects,

so a contour group is divided into several blocks (ex. 4x4 pixels, Image 13), and then

reconstructed after being judged whether or not these blocks belong to the same object. In

the judgment process, each block’s disparity (depends on the distance from FIR camera, as

shown in Image 14) is calculated by using stereo cameras, and the blocks subjected to

below conditions are classified into the same group.

Disparity difference between the blocks is within a certain range.

Blocks belonged to the same contour group before being divided.

The last step of body part area extraction is the expansion of contour groups. Contour points

may not always be extracted on all boundaries between pedestrian and background, so the

areas of contour groups need to be expanded to the areas that belong to pedestrian and do

not involve contour points. The expansion process has three steps: a) Set the blocks around

the contour group area, b) calculate the disparity of each block, and c) unite the block with

the contour group if the block has the disparity that is nearly equal to that of the contour

group.

Image 13: Divide of contour group


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Image 14: Dividing of contour group

(b) Grouping of pedestrian body part areas

After the pedestrian body part areas were extracted, the body part areas are grouped to

extract the candidate area, which is equal to the entire body of the pedestrian. This process

has two steps: Unitizing and proving. In the unitizing process, the pedestrian body part areas

of equal disparity are unitized and the candidate area is generated. Next, in the proving

process, the spatial disparity among the body part areas in the candidate area are calculated,

and judged whether or not it is equivalent to the spatial disparity of the surrounding areas.

The reason this process is performed is that some candidate areas may consist of “several”

objects which are adjacent to each other and have disparities that are nearly equal.

Therefore, if the spatial disparity of the body part areas is different from that of the

surrounding areas, the candidate area is divided into parts before being unitize.

Image 15: Grouping of body part area using disparity information


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4.1.3.2 Candidate area classification

Candidate area classification is a process of judging whether or not the extracted candidate

area is a pedestrian. In order to reduce the occurrence of classification errors due to

occlusion or video noise, this process consists of two steps: The current frame classification

and the time-series classification, as shown below.

(a) Classification in current frame

First, each candidate area is divided into several body part areas, such as head and legs, and

the “plausibility” is calculated for each body part area. The candidate area is judged whether

or not it is a pedestrian using the calculated plausibility. Then, in consideration of the

variation of images due to weather and time of the day, the judgment is performed in

accordance with environmental conditions. For example, when extracting a head area at

night time or in bad weather, image binarization is used because a head usually has a higher

intensity than the background. However, this method is not useful in the daytime because

the sun heats the background and the intensity of the head area decreases. Therefore, the

head area extraction using image binarization is performed only at night time or in bad

weather, and at daytime the head contour consisting of contour points is used. Parameters

such as binarization threshold are derived statistically in consideration of environmental

conditions.

(b) Time-series classification

This process uses the results of classification in current and past frames, using the tracking

process. A candidate area is judged as a “pedestrian” only when the ratio of the frames the

candidate area is judged as a “pedestrian” in total frames exceeds a certain value.


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4.1.3.3 Candidate area tracking

Candidate areas are tracked over time so that candidate area classification can be performed.

In the candidate area tracking process, the similarity between the candidate areas in the

current and last frames is calculated. If the similarity is larger than a certain level, then these

candidate areas are labelled as the same. In calculating the similarity, parameters such as

candidate area size variation and gravity difference are used. In addition, when the

difference of gravity is calculated, the coordinates of the candidate area are corrected by

calculating the yaw and pitch angles of the car.

4.2 INTELLIGENT VISION FOR AUTOMOBILES AT NIGHT (IVAN)

Car driving is a process of which the safety heavily relies on drivers’ accurate visual

information processing and proper reactions. Objects such as road signs, warnings and

lane lines are critical for aiding drivers to understand the road conditions. Failures in

recognizing these objects may cause serious consequences. Practically, drivers may

experience more difficulties in identifying these objects during the night driving, leading

to a much higher probability of traffic accident. Statistics shows that, more than 20% of

fatal traffic accidents occurred between midnight and 6:00 in the morning, which accounts

for only 2.4% of total traffic volume. Besides the drivers’ lacking of attention, largely

reduced visual acuity and field of vision at night due to low illumination caused by factors

such bad weathers, obscure street lamps and limited range of headlights is also a major

reason for this situation. For example, dipped headlights only illuminate about 56 meters

when the breaking distance at 100 km/h is about 80 meters.

Facing this problem, attentions have been attracted to the research of automobile night

vision systems which help to improve the visibility of objects on the road at night. In

general, such a system is equipped with night visors such as infrared cameras from

which the information of objects presenting on the road, such as bends, poles, pedestrians,

other cars etc. can be extracted.

Then, this system will inform drivers by means of visual, acoustic or other signals about

the obstacles appearing in their way. Some of the research results have been transformed


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into real products installed on high-end automobiles such as BMW 6 Series Coupe and

Mercedes-Benz 2007 S-Class series.

Intelligent Vision for Automobiles at Night (IVAN), is a highly advanced form of night

vision system, which focuses on detecting, illuminating and recognizing road signs at

night. Infrared cameras are adopted to tackle the problem of low visibility at night.

Computer vision techniques, such as image enhancement, object detection and recognition

etc., are used intensively in IVAN to analyse videos captured by the infrared cameras.

Road sign detection and recognition functions are implemented to reduce the probability of

missing traffic signs in dark environments. The system can be operated by the driver

through a touch screen and audio notifications are used for informing the driver of the

possible dangers.

Image 16: System Overview of IVAN

Unlike normal cameras, the infrared cameras are sensitive to infrared and, therefore, it

captures objects that reflect infrared. Figure 2 compares the images captured by an

infrared camera and a common webcam in the same night driving scenario. The

analogue video signals are first encoded using a TV capture card. Then, the video is

enhanced and pre-processed for later stages. The enhanced image is ready for shape

detection which locates possible road signs in the video frames. All the detected shapes

will be sent to road sign recognition module to check whether they correspond to the

known road signs stored in the database. If a road sign is recognized, it will be displayed

on the screen. At the same time, IVAN will alert the driver when an important road sign,


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such as a danger warning, is found. The detected shape will be displayed on the screen

so that the driver will be able to move the spotlight to illuminate the corresponding area.

Image 17: Images from different cameras

To adapt with different driving requirements, there are four major functionalities in

IVAN.

Adaptive night vision – IVAN captures the front view of the vehicle with an infrared

camera and displays the video onto a touch screen. Meanwhile, the infrared camera will

adaptively change direction if the vehicle is turning. The camera automatically adjusts to

the best angle so that it always captures the front view.

Road sign detection and recognition - The detection module firstly detects road signs

by processing the image captured. From these detected signs, the recognition module

recognizes important ones, such as warning signs and regulatory signs. For recognized

signs, a clearer picture will be displayed beside each one to increase the awareness of

drivers.

Spotlight projection - Once a road sign has been detected and selected by the user,

the system immediately finds its corresponding position and projects light onto it using a

spotlight mounted on a rotating platform. Automatic tracking is also implemented in

IVAN. The spotlight will illuminate on the selected road sign while the vehicle is moving.

Scene zooming - The user is able to view road signs at long distance using the zooming

function. The user can control the degree of magnification easily by sliding on the touch

screen.


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4.2.1 WORKING OF IVAN

The road sign detection module locates and segments potential road signs in real-time.

Based on the observation that most of the road signs are in regular geometric shapes, such

as rectangle, triangle and circle, the following steps are used for road sign detection in

IVAN. The input image is first processed to reduce the noise by using a 5x5 Gaussian filter.

Shades of grey are then converted to black and white (binarization) using different

thresholds. For each segmented image thus obtained, contours of the white regions are

extracted. The contours are approximated into polygons by using Douglas-Peucker

algorithm, which recursively find out a subset of vertices that the shape enclosed is similar

to the original one. The resultant polygons approximated are further analysed: In order to

improve detection speed and accuracy, they are classified into “quadrilaterals” and

“triangles” by polygons' vertex number. Their interior angles are then calculated.

Candidate road signs are selected from the detected shape by checking their interior

angles. For quadrilaterals, the interior angles should be within the range 90 degrees; for

triangles, the interior angles should be within the range 60 degrees. The parameters are

constants which are defined to offer tolerances to deal with the perspective distortion

and noises in the frame captured. Shapes will be discarded if they do not have three/four

vertices respectively or their interior angles violate the rules defined above. Consequently,

a set of quadrilaterals and triangles are detected, these shapes are regarded as traffic signs

and recorded by the tracking algorithm of the detection module.

Image 18: The IVAN system


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For round road signs, after the contours are extracted, the program verifies the detected

contours by matching their shapes with the ellipse computed. If more than a half of the

points are matched locally, the candidate ellipse becomes verified. During the process of

extraction, a geometric error is tolerated for each point. The degree of the toleration varies

adaptively on the size of each ellipse. Figure 4 illustrates the ellipse verification process.

Image 19: The ellipse verification process

In order to stabilize the detection result while minimizing the false acceptance rate, a

tracking mechanism is employed to follow the road signs detected in the captured videos.

A circular buffer is created for each traffic sign successfully detected, the bounding

rectangle and center point are recorded in the corresponding circular buffer. In the next

frame, when a shape detected in similar location, the same circular buffer will be used, and

its bounding rectangle and center will be updated. Only the shapes that appear in more than

5 times in 10 consecutive frames are considered as “successful detections” and display

onto the screen. Consequently, erroneous detections will be eliminated, since they cannot

be detected in consecutive frames. Figure 5 shows the flow chart of the stabilization

process.


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Image 20: The detection stabilization algorithm

The road sign recognition function helps user by identifying important signs, and inform

the user via audio notification in real-time. The recognition module is composed of three

parts: road sign image enhancement, feature extraction and recognition.

In order to provide good input data for the next recognition stage, the traffic signs are

first rectified in shape and normalized in colour to remove possible illumination

variations. A bounding rectangle is calculated from each sign detected, according to their

shapes. A sub-image is cropped from the frame using the bounding rectangle; four points

from both source image and rectified image are selected from the cropped traffic sign. A

transformation matrix C, which maps points from source image to the rectified image, is

computed using singular value decomposition (SVD). After the transformation matrix C is

obtained, the cropped traffic signs are rectified. Afterwards, the colour of traffic signs

cropped from the video is normalized using histogram equalization. Figure 6 shows

examples of road signs enhancement.

Image 21: Road sign image enhancement


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The enhanced road signs are to be identified by the recognition module. Features of road

signs are represented by histograms of gradients in four regions. After a cropped road sign

has been rectified and enhanced, its x-derivative (x) and y-derivate ( y) are computed

by using the Sobel operator. For each edge pixel detected by the Canny operator, its

gradient is computed by the equation: G(i,j) = tan-1( y(i,j)/ x(i,j)).

Subsequently, the cropped image is divided into four regions; a histogram of gradient

is calculated in each region. There are eight bins in a histogram (45 degrees for one

bin); therefore 32 features are used to describe a road sign. A road sign and its

corresponding histograms are shown in Figure 7.

Image 22: Road sign feature extraction

Each road sign will be passed to the recognition module for calculating its edge

gradient histogram. Totally 32 values will be used to represent its features. A list of

standard image’s histogram will be evaluated in advance and stored in the system to

represent different road signs. For each recognition case, the edge gradient histogram of

the target image will be calculated. The Euclidean distance between the histogram of the

image and that of standard road signs image will be evaluated. A similar image should

result in lowest Euclidean distance. The distance should be lower than a certain threshold

so that an image that does not similar to any predefined image also can separate out.

Therefore, the target image can be classified to particular road sign.

The Spotlight Projection module is aimed at projecting light onto specified spots

accurately. As soon as users give commands by touching a spot on the touch screen, the

software automatically turns the spot light in the direction of the spot and project onto it.

This step refers to the mapping between the video captured and the rotating platform of

the spotlight. This process determines the degree that the spotlight turns horizontally

and vertically respectively, when the user touches on the screen. The light projection will


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last for one second and the spot will track the detected road sign within this time interval.

Hence, the calculation process needs to continuously generate control signals in real-

time. The projection will terminate if the specified spot moves out of the image or a new

command is given. Meanwhile, to ensure that the light will not glare drivers in the

opposite direction, tracking instruction is not allowed if the angle of elevation for the

spotlight is too low.

Image 23: Adaptive infrared camera

The night vision feature is implemented by utilizing an infrared camera to capture the

front view. Since infrared camera has strong sensibility against infrared, the captured

images enable drivers to see the road conditions and identify road signs or other objects at

night. Inspired by BMW 7 Series’ Adaptive Headlights System, an adaptive control

mechanism is implemented by estimating an adjustment angle from the vehicle’s speed

and turning angles. Figure 8 illustrates the usage of camera adjustment.

Image 24: Sample cases of road sign detection


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4.3. TRUE-COLOUR NIGHT VISION

Numerous studies have shown that scene understanding, reaction time, and object

identification is faster and more accurate with colour imagery than with monochrome

imagery. Considering surveillance, reconnaissance, and security applications, colour

imagery has two main benefits over monochrome imagery. The first is that colour improves

contrast, which allows for better scene segmentation and object detection. This contrast

improvement can apply to both true-colour and false-colour images, where false-colour

imagery can be formed by the fusion of images from cameras with different spectral

sensitivity (e.g., image intensified with thermal IR). The second benefit of colour is that it

provides more information. Access to stored colour knowledge in the brain or a computer

database can be utilized to enable better object identification and scene understanding. This

second improvement applies primarily to true-colour images, since false-colour images do

not necessarily match the stored colour information, and may in fact be detrimental in this

regard.

General benefits and drawbacks of true-colour night vision (TCNV) systems are listed in

Table 1, and examples of the utility of true-colour information are shown in Figure 1. For

example, Figure 1 demonstrates that successfully finding the man with the orange shirt,

determining the difference between flags, or being able to pick out the blue car are all tasks

that benefit greatly from the additional information that true-colour imagery provides.

To obtain true-colour images a camera must be sensitive to the visible portion of the

electromagnetic spectrum and there must be a mechanism to filter or split the different parts

(i.e., colours) of the visible spectrum so that colour information can be extracted. This need

to filter the input has the consequence of reducing the available signal to a detector, which

is the primary drawback of a true-colour system intended for use in low-light situations.

Furthermore, standard monochrome image-intensified systems are typically designed to

take advantage of the relatively high near-infrared (NIR) signal available from the night

sky. To mitigate the inherent reduction in signal due to filtering, a true-colour system should


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also be able to utilize this NIR light. In addition, sensitivity to NIR is also needed for

viewing of IR laser aiming devices, as demonstrated in Figure 2. The ability to produce

true-colour content, while maintaining sensitivity to NIR is one of the inherent challenges in

making a viable true-colour night vision camera.

New camera technology and image processing routines have been developed to enable the

use of true-colour information from the visible portion of the spectrum while utilizing the

full visible to near infrared (V-NIR) range (roughly 400 to 1000 nm in wavelength) for the

brightness information. Two different types of TCNV cameras are there; one camera uses a

liquid crystal filter in front of an image intensified detector and the other uses a mosaic filter

deposited on the pixels of an EMCCD detector. Both cameras are based on new

technologies: the liquid crystal camera uses fast switching filters with optimized

transmission bands, and the mosaic filter camera relies on recent advances in CCD

technology

True Colour Night Vision (TCNV)

Benefits (compared to monochrome) Drawbacks (compared to monochrome)

More information – better object

recognition, better scene

understanding.

Improved contrast – better object

detection, better scene

segmentation.

Reduced signal.

Increased cost.

Table 3: General benefits and drawbacks of true-colour night vision cameras as compared

to standard monochrome low-light-level or image intensified cameras.


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Image 25: monochrome and colour low light level imagery. The images illustrate the

additional information that is available with the inclusion of colour.

Image 26: Image taken with TCNV camera demonstrating the ability to produce colour

imagery while utilizing both visible and NIR signal for brightness. The bright spot on the

red car is from an NIR laser aiming device.

4.3.1. DESCRIPTION OF CAMERAS

4.3.1.1 LIQUID CRYSTAL FILTER INTENSIFIED CAMERA

Liquid crystal (LC) filters consist of stacks of polarizing, bi-refrainment, and variable

retardance substrates. With applied voltages, the transmission of the stack can be

electronically switched to a different band pass or “colour” state (see Figure 3). A full

colour image is constructed by using separate images taken in 3 or 4 different colour

states and then mixing them with appropriate weights to form an RGB output image.

Although the colour information is built up over multiple exposures, the image is updated


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with each captured frame, rather than waiting until a complete set of 3 or 4 frames is

captured.

In addition to the visible wavelengths, the LC filters also pass NIR radiation to increase

the available signal and to enable viewing of IR laser aiming devices. With the use of

specifically tailored band pass states and optimized colour mixing algorithms, the NIR

signal contributes to the brightness of an image without destroying the true colour

information.

Image 27: A liquid crystal filter shown in 3 different colour states. The colour is switched

by changing the applied voltages.

Fast Switching Liquid Crystal Filter Camera

Benefits Drawbacks

Full colour resolution at each pixel.

Filter can be positioned out of

optical path for full detector

sensitivity at lowest light levels.

Versatile: filter can be used with

any type of VNIR low-light

detector.

Low power.

No moving parts.

Fast-switching LC: no “dead-time”

Time-sequential image capture: it

takes multiple frames to produce a

full colour image.

Reduced signal: filters rely on

polarization, which leads to an

overall reduction in signal of

approximately 50% — (the average

transmission is less than 50% in the

visible, but higher than 50% in the

NIR)

Table 4: lists the main benefits and drawbacks of a night vision cameras that use such LC

filters.


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The latest LC filters are extremely fast switching taking less than 1ms to switch between

any two states. Fast switching enables the camera to operate without “dead-time” and the

associated light loss while the filter is in an undefined state. With typical LC filters it is

impractical to operate at video rates, i.e., 30 frames/second (fps), since the dead-time is on

the same order as the frame period. However, with the fast-switching filter, rates as high as

180 fps are routinely used.

True colour night vision cameras use an image intensified CMOS detector with a “smart

camera” digital media processor (DMP).

The image intensifier is a Gen III blue-enhanced tube, which is bonded to the sensor via a

2:1 fibre-optic reducer. The CMOS array is a ½” format 640×480 pixel detector capable of

200 fps at full resolution. A high frame rate detector is used to enable a reduction in the

image blur associated with time-sequential image capture; however, at the lowest light

settings, longer exposure times (and thus lower frame rates ~ 30 fps) are used.

Image 28: TCNV prototypes with LC filter and image intensified CMOS

4.3.1.2 EMCCD/MOSAIC CAMERA

Electron multiplication CCD (EMCCD) technology has been steadily advancing over the

last several years. These low-light sensors are basically ultra-sensitive CCD chips (there is

no image intensifier tube). High amplification is achieved by employing a specialized

multiplication register, where the gain occurs in the charge domain before readout and

digitization by the A to D converter. Since these detectors do not use an image-intensifier

tube they are not prone to burn-out/damage from bright lights. A colour EMCCD is made

by using a mosaic filter similar to the standard “Bayer” pattern used by commercial colour

CCD and CMOS sensors. With a mosaic filter, a group of neighbouring pixels each “sees” a


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different colour and interpolation is used to determine the appropriate Red, Green, Blue

(RGB) values at each pixel. For the tests presented in this paper a camera using a

complimentary colour pattern was used (see Figure 5).

Image 29: EMCCD/mosaic

EMCCD/ Mosaic filter

Benefits Drawbacks

Full colour with each image

frame:-reduced motion blur.

No intensifier tube: - so No

potential damage due to bright

lights.

Anti-bloom features: - can

capture images with bright

lights in scene.

Higher power requirements: -

about 15 Watts needed to cool

detector.

EMCCD sensors currently have

lower sensitivity than Gen III

image intensifiers.

Reduced spatial colour

resolution: - need to interpolate

among pixels.

Table 5: Summary of the primary benefits and drawbacks of an EMCCD/mosaic colour

camera


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A summary of the major benefits and drawback of an EMCCD/mosaic camera are listed

in Table 3. Most of the benefits are related to the fact that an intensifier tube is not used;

however, there are three main drawbacks to this scheme: (1) the sensitivity currently does

not match that achieved by a Gen III image intensifier tube, (2) cooling is needed to

reduce the dark current on the chip, which equates to higher power consumption; and (3)

interpolation is needed to construct (“de-mosaic”) an image, resulting in reduced colour

resolution.

Image 30: Plot of spectral response of the mosaic filters on the colour EMCCD chip.

4.3.2. FUSION FOR TRUE COLOR NIGHT VISION

Typically, the goal of image fusion algorithms is to effectively combine the information

in the separate input images to the fused output image in a manner that maximizes the

overall information content. For night vision applications, the success of such algorithms

is best measured with human performance task experiments such as scene understanding,

object detection/ recognition/ identification tests. For the special case of true colour night

vision, the objective assessment of colour fidelity is an important metric measuring the

ability to convey the additional information inherent with true colour.


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To render appropriate colour fusion output it is beneficial to separate the colour values

(i.e., chroma) from the brightness values (i.e., luma) using a colour space such as La*b*

or YUV instead of the Red Green and Blue (RGB) colour space. For the present system

the chroma values are obtained directly from the colour EMCCD camera, using several

proprietary algorithms to improve the colour content, and the luma values are determined

using an algorithm that fuses the brightness of the two cameras.

Example images illustrating true colour fusion are shown in Figure 4. In this figure (a)

and (b) are input images and (c) is the final fused output, the image in (d) is simply

shown for comparison. The fusion algorithm used to generate the image in Figure 4 (c)

was developed to display the significant thermal information (e.g., the man in background

and the hot car) while maintaining the true-colour information (e.g., the colour of the car

and presence of a red gas cylinder).

Additional fused output images are shown in Figure 5 with the same chroma values as

Figure 4 (c), but with different luma values. Specifically, in Figure 5 (a) the thermal

image alone is used for the luma channel (with no VNIR brightness) and in Figure 5 (b)

a simple 50/50 blending of the VNIR and thermal brightness is used. These images

highlight more of the thermal information; however, the colour content suffers. The

figures illustrate various fusion schemes that can be controlled by the user to aid scene

understanding under various conditions.

Other more complicated fusion algorithms, such as PCA or wavelet based algorithms, can

also be used to combine the thermal and VNIR brightness information into a single luma

image. However, many of these algorithms produce output that is not conducive for true-

colour display.


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Image 31: Example images illustrating true-colour fusion. (a) and (b) are the input

images, (c) is the fused image with thermal information highlighted by red rectangles and

colour information highlighted by green ovals. (d) is simply shown for comparison

Image 32: Figure 5. Example fused images using alternative methods for producing the

luma component of the fused image. Note: the true-colour fidelity suffers in the above

examples and has lower colour fidelity compared to the image in Figure 4 (c).


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5. DISCUSSION

The scope of this study is to combine the present technologies available in night vision

system such as pedestrian detection, intelligent vision of automobiles during night (IVAN)

and True Colour Night Vision and to display this information from the night vision system

directly to the windscreen rather than to a small centre console display. Head up display is

one of the prominent and simple techniques which are used for windscreen display.

Head up display consists of small display unit that generates the image which is being

reflected on the windscreen using a partial reflecting film. In present HUD no image is

projected on the screen. Instead of projection technology, the image formed on the display

is reflected to generate an image on the windscreen. But this kind of windscreen has its own

draw backs. It cannot be prominently visible during day time and also can't be projected on

a wider area of windscreen as the image is formed in a small display unit which is being

reflected.

The data provided by the IVAN and the pedestrian detection system when combined with

vehicle navigations and other information, it gets congested and confusing for the driver

when displayed on small screen. IVAN and pedestrian detection system is used only during

night time, so it is well suited for windscreen projection since there is no background with

high intensity lights. Hence, the use of projection technology is been adopted to use the

windscreen itself as a projecting screen.

Windscreen projection uses holographic LASER projection technology. In this technology

high intensity light beams is used to display the notification information on the windscreen.

The colours like blue, red and green has the capacity of being projected by itself on the

windscreen. The image projection unit can be mounted above the hood to provide projection

of the image at an angle of 120 degree. This setup provides the light beam to fall at a critical

angle which intern causes TOTAL INTERNAL REFLECTION of light beam in the

windscreen.


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Image 33: Figure showing different parts of projecting units.

Image 34: Figure showing windscreen projection


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6. CONCLUSION

Automotive Head up display is an emerging technology which has many advantages on

the ergonomic aspects as well as for the comfort of driver. Researches are going on for the

development of HUD to minimise the space of the central console and displaying all the

necessary information in the windshield itself. But HUD has some limitations, that for

HUD, it requires a partial reflecting element to reflect the projected image in the

windscreen to act as a screen, also the projector must be arranged with projection angle

above the critical angle of the glass to reflect it image. Another, one main problem is that

HUD cannot provide a better display during the day time. The background light is so high

that the projected image won't be properly seen. This become challenging and limits the

display area of HUD to a small portion of the windshield.

But, HUD is more preferable for the night drive. During night, except the high beam of

approaching vehicles, high intensity lights are lesser. So HUD can work well for the night

vision. Presently, in night vision technology, after spotting a human or animal in the field

of driving, the information is displayed on the small screen on the central consol. This is

ergonomically, not completely satisfactory for the drive, as he need to take away his eyes

from the road to have a look at the screen. So, mostly drivers won't relay on the night

vision all the time.

With the use of holographic glass projection technology the vision system can be

developed to a next generation, with the combination of IVAN technology and pedestrian

detection with holographic projection the exact position, size and type of the object

detected can be show directly on the windshield glass were the driver see through. The

high intensity holographic laser projection can display the symbols detected by the IVAN

as well as the road markings on the wind screen. Also by using this projection the

boundary box of the human or animal can be shown in the wind screen at the right

position of the object which the driver could see through the windscreen.


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RECOMENDATION

With this study we have identified the flaws in present night vision system. We are

providing supporting documents that can improve the present intelligent night vision

system with true colour output and windscreen projection which can be considered for the

future development and research in ITS. If this system comes into existence the use of

night beam head lights can be avoided to a greater extent.


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REFERENCES

1. K. Rumar, Adaptive illumination systems for motor vehicles: Towards a more

intelligent headlighting system, Report no.UMTRI-97-7. Ann Arbor, MI: The

University of Michigan Transport Research Institute, 1997.

2. P.A. Thompson, Daytime running lamps (DRLs) for pedestrian protection ,

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BIBLIOGRAPHY

http://www.pspc.dibe.unige.it/~drivsco

http://www.bmw.com/com/en/newvehicles/6series/coupe/2007/allfacts/ergonomics_nightv

ision.html

http://www.mercedesforum.com/m_35841/tm.htm

http://www.gps4us.com/news/post/Windshield-projection-technology-renders-GPS-

navigation-route-for-safer-driving-20111221.aspx

APPENDIX

Killed Injured Total Car passengers 75,615 3’751,024 3’826,639 Pedestrians 39,670 436,422 476,092 Bicycles 6,872 236,027 242,899 Mopeds 3,151 163,854 167,005 Motor Cycles 10,972 227,946 238,918 Other 28,397 1’303,571 1’331,968 Total 161,677 6’118,844 6’283,521

Table 6: Road Traffic Accidents 1997 – Figures for UN-ECE Countries (Accident Source: UN-ECE).

http://www.pspc.dibe.unige.it/~drivsco

http://www.bmw.com/com/en/newvehicles/6series/coupe/2

http://www.mercedesforum.com/m_35841/tm.htm

ADVANCEMENT IN AUTOMOTIVE NIGHT VISION SYSTEM

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