1. INTRODUCTION

The video and thermo gram analyzer continuously monitor activities outside the car.

Once the driver (disabled) nears the car. The security system of the car is activated. Images as

well as thermo graphic results of the driver are previously fed into the database of the

computer. If the video images match with the database entries then the security system

advances to the next stage. Here the thermo graphic image verification is done with the

database. Once the driver passes this stage the door slides to the sides and a ramp is lowered

from its floor. The ramp has flip actuators in its lower end. Once the driver enters the ramp,

the flip actuates the ramp to be lifted horizontally. Then robotic arms assist the driver to his

seat. As soon as the driver is seated the EEG (electroencephalogram) helmet, attached to the

top of the seat, is lowered and suitably placed on the driver’s head. A wide screen of the

computer is placed at an angle aesthetically suitable to the driver. Each program can be

controlled either directly by a mouse or by a shortcut. For starting the car, the start button is

clicked. Accordingly the computer switches ON the circuit from the battery to the

A.C.Series Induction motors.

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2. ANALYSIS

2.1. BIOCONTROL SYSTEM:

The biocontrol system integrates signals from various other systems and compares

them with originals in the database. It comprises of the following systems:

Brain-computer interface

Automatic security system

Automatic navigation system

Now let us discuss each system in detail.

2.1.1. BRAIN – COMPUTER INTERFACE

Brain-computer interfaces will increase acceptance by offering customized,

intelligent help and training, especially for the non-expert user. Development of such a

flexible interface paradigm raises several challenges in the areas of machine

perception and automatic explanation. The teams doing research in this field have

developed a single-position, brain-controlled switch that responds to specific patterns

detected in spatiotemporal electroencephalograms (EEG) measured from the

human scalp. We refer to this initial design as the Low- Frequency

Asynchronous Switch Design (LF-ASD)

Fig.1 LF-ASD

The EEG is then filtered and run through a fast Fourier transform before being

displayed as a three dimensional graphic. The data can then be piped into MIDI compatible

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music programs. Furthermore, MIDI can be adjusted to control other external processes, such

as robotics. The experimental control system is configured for the particular task being used

in the evaluation. Real Time Workshop generates all the control programs from Simulink

models and C/C++ using MS Visual C++ 6.0. Analysis of data is mostly done within Mat lab

environment. FEATURES OF EEG BAND.

Remote analysis data can be sent and analyzed in real-time over a network or

modem connection. Data can be fully exported in raw data, FFT & average formats.

Ultra low noise balanced DC coupling amplifier.

Max input 100microV p-p, minimum digital resolution is

100 microV p-p / 256 = 0.390625 micro V p-p. FFT point can select from 128 (0.9375 Hz),

256 (0.46875 Hz), 512

(0.234375 Hz resolution).

Support for additional serial ports via plug-in boar; allows extensive serial input & output

control.Infinite real-time data acquisition (dependent upon hard drive size).

Real-time 3-D & 2-D FFT with peak indicator, Raw Data, and Horizontal Bar displays

with Quick Draw mode.Full 24 bit color support; data can be analyzed with any standard or

user.Customized color palettes; color cycling available in 8 bit mode with QuickDrawmode.

Interactive real-time FFT filtering with Quick Draw mode. Real-time 3-D FFT (left,

right, coherence and relative coherence), raw Wave, sphere frequency and six brain wave

switch in one OpenGL display. Full Brainwave driven Quick Time Movie, Quick Time

MIDI control; user configurable.

Full Brain wave driven sound control, support for 16 bit sound; user configurable.Full

image capture and playback control; user configurable.

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Fig. 2: EEG Transmission

Fig. 3 EEG

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TEST RESULTS COMPARING DRIVER ACCURACY

WITH/WITHOUT BCI

1. Able-bodied subjects using imaginary movements could attain equal or better control

accuracies than able-bodied subjects using real movements.

2. Subjects demonstrated activation accuracies in the range of 70-82% with false activations

below 2%.

3. Accuracies using actual finger movements were observed in the range 36-83%

4. The average classification accuracy of imaginary movements was over 99%

Fig.4 Brain-to- Machine Mechanism

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The principle behind the whole mechanism is that the impulse of the human

brain can be tracked and even decoded. The Low-Frequency Asynchronous Switch

Design traces the motor neurons in the brain. When the driver attempts for a physical

movement, he/she sends an impulse to the motor neuron. These motor neurons carry

the signal to the physical components such as hands or legs. Hence we decode

the message at the motor neuron to obtain maximum accuracy. By observing

the sensory neurons we can monitor the eye movement of the driver.

Fig.5 Eyeball Tracking

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As the eye moves, the cursor on the screen also moves and is also brightened when the driver concentrates on one particular point in his environment. The sensors, which are placed at the front and rear ends of the car, send a live feedback of the environment to the computer. The steering wheel is turned through a specific angle by electromechanical actuators. The angle of turn is calibrated from the distance moved by the dot on the screen.

Fig.6 Electromechanical Control Unit

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Fig.7 Sensors and Their Range

2.1.2. AUTOMATIC SECURITY SYSTEM

The EEG of the driver is monitored continually. When it drops less than

4 Hz then the driver is in an unstable state. A message is given to the driver for

confirmation to continue the drive. A confirmed reply activates the program

automatic drive. The computer prompts the driver for the destination before the drive.

2.1.3. AUTOMATIC NAVIGATION SYSTEM

As the computer is based on artificial intelligence it automatically monitors

every route the car travels and stores it in its map database for future use. The map

database is analyzed and the shortest route to the destination is chosen. With traffic

monitoring system provided by xm satellite radio the computer drives the car automatically.

Video and anti-collision sensors mainly assist this drive by providing continuous live feed of

the environment up to 180 m, which is sufficient for the purpose.

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Fig.8 EEG Analysis Window

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3. APPLICATIONS

Artificial Intelligence in the form of expert systems and neural networks have

applications in every field of human endeavor. They combine precision and computational power

with pure logic, to solve problems and reduce error in operation. Already, robot expert systems

are taking over many jobs in industries that are dangerous for or beyond human ability. Some of

the applications divided by domains are as follows:

Heavy Industries and Space: Robotics and cybernetics have taken a leap combined with

artificially intelligent expert systems. An entire manufacturing process is now totally controlled

and maintained by a computer system in car manufacture, machine tool production, computer

chip production and almost every high-tech process. They carry out dangerous tasks like

handling hazardous radioactive materials. Robotic pilots carry out complex maneuvering

techniques of unmanned spacecrafts sent in space. Japan is the leading country in the world in

terms of robotics research and use.

Finance: Banks use intelligent software applications to screen and analyze financial data.

Softwares that can predict trends in the stock market have been created which have been known

to beat humans in predictive power.

Computer Science: Researchers in quest of artificial intelligence have created spin offs like

dynamic programming, object oriented programming, symbolic programming, intelligent storage

management systems and many more such tools. The primary goal of creating an artificial

intelligence still remains a distant dream but people are getting an idea of the ultimate path which

could lead to it.

Aviation: Air lines use expert systems in planes to monitor atmospheric conditions and system

status. The plane can be put on auto pilot once a course is set for the destination.

Weather Forecast: Neural networks are used for predicting weather conditions. Previous data is

fed to a neural network which learns the pattern and uses that knowledge to predict weather

patterns.

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Swarm Intelligence: This is an approach to, as well as application of artificial intelligence

similar to a neural network. Here, programmers study how intelligence emerges in natural

systems like swarms of bees even though on an individual level, a bee just follows simple rules.

They study relationships in nature like the prey-predator relationships that give an insight into

how intelligence emerges in a swarm or collection from simple rules at an individual level. They

develop intelligent systems by creating agent programs that mimic the behavior of these natural

systems.

Game playing: You can buy machines that can play master level chess for a few hundred

dollars. There is some AI in them, but they play well against people mainly through brute force

computation--looking at hundreds of thousands of positions. To beat a world champion by brute

force and known reliable heuristics requires being able to look at 200 million positions per

second.

Speech recognition: In the 1990s, computer speech recognition reached a practical level for

limited purposes. Thus United Airlines has replaced its keyboard tree for flight information by a

system using speech recognition of flight numbers and city names. It is quite convenient. On the

other hand, while it is possible to instruct some computers using speech, most users have gone

back to the keyboard and the mouse as still more convenient.

Understanding natural language: Just getting a sequence of words into a computer is not

enough. Parsing sentences is not enough either. The computer has to be provided with an

understanding of the domain the text is about, and this is presently possible only for very limited

domains.

Computer vision: The world is composed of three-dimensional objects, but the inputs to the

human eye and computers' TV cameras are two dimensional. Some useful programs can work

solely in two dimensions, but full computer vision requires partial three-dimensional information

that is not just a set of two-dimensional views. At present there are only limited ways of

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representing three-dimensional information directly, and they are not as good as what humans

evidently use.

Expert systems: A ``knowledge engineer'' interviews experts in a certain domain and tries to

embody their knowledge in a computer program for carrying out some task. How well this works

depends on whether the intellectual mechanisms required for the task are within the present state

of AI. When this turned out not to be so, there were many disappointing results. One of the first

expert systems was MYCIN in 1974, which diagnosed bacterial infections of the blood and

suggested treatments. It did better than Medical students or practicing doctors provided its

limitations were observed. Namely, its ontology included bacteria, symptoms, and treatments

and did not include patients, doctors, hospitals, death, recovery, and events occurring in time. Its

interactions depended on a single patient being considered. Since the experts consulted by the

knowledge engineers knew about patients, doctors, death, recovery, etc., it is clear that the

knowledge engineers forced what the experts told them into a predetermined framework. In the

present state of AI, this has to be true. The usefulness of current expert systems depends on their

users having common sense.

Heuristic classification: One of the most feasible kinds of expert system given the present

knowledge of AI is to put some information in one of a fixed set of categories using several

sources of information. An example is advising whether to accept a proposed credit card

purchase. Information is available about the owner of the credit card, his record of payment and

also about the item he is buying and about the establishment from which he is buying it (e.g.,

about whether there have been previous credit card frauds at this establishment).

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4. LIMITATIONS

The ultimate goal of research in AI and Robotics is to produce an android which can

interact meaningfully with human beings. A huge amount of research effort is being exerted in

order to achieve this aim and a lot of progress has already been made. Researchers have

manufactured androids that can walk on two legs, that can climb stairs, that can grasp objects

without breaking or dropping them, that can recognize faces and a variety of physical objects,

that can imitate what they see human beings doing and so on. It is hard to make robots that can

do these things and I have no desire to belittle the scientific achievements that have already been

made, but even if a robot succeeds in doing all these things as well as a human being it will still

lack at least one essential human ability, namely that of learning from other people by accepting

what they say and by believing what they have written. The ultimate goal of AI cannot be

achieved until we have implemented in a computer system the ability to acquire information

from testimony.A number of people, who should know better, make predictions about when AI

will achieve its ultimate goal. There is no possibility of AI succeeding in the foreseeable future.

People who say otherwise are simply ignorant of the state of research into testimony. AI cannot

succeed until an android (or computer program) can evaluate testimony in a similar way to that

in which a human being can. We are decades, at best, from achieving that as hardly anybody, at

present, is even studying testimony from an AI perspective, let alone building computer systems

to emulate the human ability to learn from testimony.

Limitations of AI Approaches:

Data-specific and method-specific

Single software environment

Real-world domain complexities prevent these applications “scaling-up”

Most remain within the research community

Still need greater software flexibility

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5. CONCLUSION

When the above requirements are satisfied and if this car becomes cost effective

then we shall witness a revolutionary change in the society where the demarcation between

the abler and the disabled vanishes. Thus the integration of bioelectronics with automotive

systems is essential to develop efficient and futuristic vehicles, which shall be witnessed soon

helping the disabled in every manner in the field of transportation.

Fig 9. Brain controlled car view

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6. FUTURE TECHNOLOGIES

There are other means of reading brain activity than direct neural contact via pins. The

first and most common is electroencephalography (EEG) where electrodes are placed against

the scalp are used to pick up brain signals. However, this approach is not nearly as accurate as

direct neural contact and can only pickup blurry, weak readings. The other, much newer, and

much more accurate non-invasive technology is magnetoencephalography (MEG) but is also

more equipment intensive. Using MEG requires a room filled with super-conducting magnets

and giant super-cooling helium tanks surrounded by shielded walls. This technology, while

providing the speed and accuracy needed for a successful non-invasive BMI, will require

significant improvement of technology in order to be realistic for everyday use.Early BCI/BMI:

signal splicing into human sensory nerve pathways, most importantly the visual nerve.

Mid-term BCI/BMI: more direct links into the brain with the ability to read certain

thoughts and copy a wide range of data and information into various parts of the brain. Final

BCI/BMI: direct control over the activities of all individual neurons by means of nanorobots.

Arbitrary read/write access to the whole brain. The line between the mind and the computer is

blurred. Partial or full uploading is possible and inevitable.

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7. REFERENCES

1. 'Off-line Classification of EEG from the "New York Brain- Computer Interface (BCI)"

Flotzinger, D.,Kalcher, J., wolpaw, J.R., McFarland, J.J., and Pfurtscheller, G., Report

#378, IIG-Report Series, IIG: Institutes for Information Processing, Graz University

of Technology, Austria 1993.

2. "Man-Machine Communications through Brain-Wave Processing" Keirn, Z.A. and Aunon,

J.I., IEEE Engineering in Medicine and Biology Magazine, March 1990.

3. Automotive engineering, SAE, June 2005

4. Automotive mechanics, Crouse, tenth edition, 1993

5. "The brain response interface: communication through visually-induced electrical brain

responses" Sutter,E.E., Journal of Microcomputer Applications, 1992, 15: 31-45.

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