A Fuzzy Approach to Prevent Headlight Glare

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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 2, February 2011

A Fuzzy Approach to Prevent Headlight Glare
Mrs.Niraimathi.S Dr.M.Arthanari
P.G.Department of computer applications Director
N.G.M College Bharathidasan School of computer applications
Pollachi-642001, TamilNadu, India Ellispettai-638116, TamilNadu, India
niraisenthil@yahoo.com arthanarimsvc@gmail.com

Mr.M.Sivakumar
Doctoral Research Scholar
Anna University, Coimbatore, TamilNadu, India
sivala@gmail.com

Abstract: This paper proposes a fuzzy rule based design Headlight glare is the main challenge, when driving at
approach to prevent the Headlight glare emitted by the night to the drivers. During night the drivers are affected by
oncoming vehicles on the Highways. This gradually the dazzling high intensity headlights, which puts off their
reduces accidents on the Highways as the driver of the vision and results in accidents. The blinding effect may be
oncoming vehicle is put on a comfortable zone which nearly total, if the lights have not been switched from high
might otherwise blind the oncoming driver’s visibility. In beam, but even on low beam there is significant discomfort
the conventional vehicles the illumination is adjusted and reduced visibility. This paper proposes a Fuzzy based
manually by the driver. This fuzzy based approach has approach to reduce the headlight glare. The fuzzy sensor and
the fuzzy sensor and the controller embedded inside the the fuzzy controllers embedded in the windshield during its
windshield or fit on to it, generates ambient illumination lamination process or fit on to the windshield, gives a
to the oncoming driver, there by not ruining the vision of solution to the headlight glare. The Sensor includes the
the driver during night. This setup has to be embedded operation of checking the light source, if of over
on to all the vehicles, so that it prevents the happening of tolerance/under tolerance. There by the controller converting
accidents. Fuzzy sensor and the controller makes use of it in to low intensity if of high intensity and vice versa,
the fuzzy rules. The light intensity emitted by the providing ambient light source.
oncoming vehicle received by the fuzzy sensor, is The light intensity(I) measured in Volts and the
fuzzified using triangular membership function and distance(D) in metres are received by the fuzzy sensor. The
checked for the tolerance limit. If not of acceptable limit; input parameters received by the fuzzy sensor are crisp input
the fuzzy sensor forwards it to the fuzzy controller which values (Numerical value). These crisp sets are converted in to
converts the light intensity to an ambient light source fuzzy sets using the process of fuzzification and are
thereby defuzzifying the output. evaluated using the fuzzy rules. The output light intensity(OI)
calculated using the fuzzy rules is checked for the tolerance
Key words- Fuzzy logic; fuzzy rules; fuzzy sensor; fuzzy limit by the fuzzy sensor. If beyond the tolerance limit, the
controllers; fuzzification; defuzzification; Headlight glare fuzzy sensor defuzzifies using Centroid of Area and then
sends it to the fuzzy controller which converts it to ambient
I. INTRODUCTION light source. The process of fuzzification and defuzzification
is also repeated in the fuzzy controller.
Around the world more than 1.2 million people lose their
life in Road Accidents, every year. 3 to 4 % of Gross In [1] a fuzzy controller that controls the brake rate of the
National Product is lost in Road Accidents. One person is vehicle has been stated. The speed of the vehicle for which
killed in Road Accidents, every three minutes in the World. the brake has to be applied and distance of the vehicle from
Total number of annual road accident deaths is more than the the point at which it has to stop are passed as the input
total population of Mauritius. parameters to a fuzzifier. The controller compares these
inputs with the rule base and gives the desired output.

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In [2] automatic fuzzy controller which controls the fuzzification of these parameters, linguistic variables are used
switching of headlight intensity of automobiles has been (Table I, II, III). The input Intensity(I) consists of 6 fuzzy
proposed.. sets, Distance(D) has 10 fuzzy sets and the output parameter
[3,5,6,8,9,11,12] gives basic understanding of Crisp set, its ouput Intensity consists of 6 fuzzysets.
conversion to Fuzzy sets, concepts of fuzzy controller, and
the knowledge about Fuzzy Expert system. This paper gives TABLE I. LINGUISTIC VARIABLES FOR INPUT INTENSITY I(V) AND THEIR
the Methodology used in the fuzzy sensor, fuzzification of NUMERICAL RANGE

input variables, rule evaluation and defuzzification in section
II, Implementation in section III and conclusion in section Linguistic value Notation Numerical
IV. range
JustNoticeable JN 0-3.50
Noticeable N 3.00-6.50
II. METHODOLOGY Satisfactory S 5.00-8.50
JustAcceptable JA 7.00-10.50
The fuzzy sensor with its input parameters I(input Disturbing D 9.00-12.50
intensity), D(distance) and the output parameter OI(Sensor UnBearable UB 11.00-14.50
output) is clearly shown in Fig. 1. The figures below indicate
the demonstrations derived using MATLAB.

TABLE II. LINGUISTIC VARIABLES FOR DISTANCE D(MTS) AND ITS
NUMERICAL RANGE

Linguistic value Notation Numerical
range
VeryClose VC 0-25
Close CL 12-50
VeryNear VN 37-75
Near N 62-100
ModeratelyNear MN 87-125
ModeratelyFar MF 110-150
Far F 135-175
Fig. 1. The structure of the fuzzy sensor VeryFar VF 160-200
PrettyVeryFar PVF 185-225
A. Fuzzy inference process BoundaryZone BZ 210-250

Fuzzy inference process defines a set of fuzzy “if – then
“rules. Most fuzzy logic based system uses rule bases to TABLE III. LINGUISTIC VARIABLES FOR SENSOR OUTPUT LIGHT SOURCE
represent the relation among the linguistic variables and to OI(V) AND ITS NUMERICAL RANGE
derive actions from sensor input.
The fuzzy inference process is performed in four steps: Linguistic value Notation Numerical
range
1. Fuzzification of the input variables. JustNoticeable JN 0-3.50
2. Defining Membership functions. Noticeable N 3.00-6.50
3. Rule evaluation. Satisfactory S 5.00-8.50
4. Defuzzification. JustAcceptable JA 7.00-10.50
Disturbing D 9.00-12.50
UnBearable UB 11.00-14.50
1) Fuzzification: Fuzzification is the process of
converting the crisp input variables to fuzzy variables. It is
the mapping of the range of input to set membership values
of each fuzzy variable. The crisp values got for the input 2) Defining Membership functions: After fuzzification
parameters I and D are converted in to fuzzy sets. For is done, the next process is to define the membership

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functions in the fuzzy sets for the input and output
parameters. The Triangular membership function is used
for constructing the fuzzy sets. The membership
function of the input parameters is shown by the figures
(2-3). The membership function of the output parameter
µS
is shown in figure.4. Fuzzy membership expressions for
the input Intensity(I) and Distance(D) is given by
(Eq.(1-2)).

µJA

Fig.2. The membership function of I(inputIntensity) µD

µJN

µUB

µN (1)

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µN

Fig. 3. The membership function of D(Distance)

µMN

µVC
µMF

µCL
µF

µVN
µVF

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TABLE IV. RULE MATRIX REPRESENTATION

D/I JN N S JA D UB
µPVF
BZ JN JN JN S JA D
PVF JN JN N S D D
VF JN JN N S D D
F JN N S S D D
MF JN N S JA D D
MN N N S JA D D
N S S S JA D UB
VN S S S D D UB
CL JA JA D D UB UB
µBZ VC JA D D UB UB UB

From the rule matrix we are able to arrive at the rule bases.
We have 10*6 rules for the fuzzy sensor. The rule base
consists of antecedent part and the consequent part.
(2) Antecedent part consists of input linguistic variables that may
be combined using AND operators. Consequent part contains
the output of the fuzzy rule. The figure below (Fig. 5) shows
the rule base for the sensor. In the figure below, the value of
I=13.9, D=250 and OI=7.25. This implies that the output
light intensity is moderate; the sensor judges it to be of the
acceptable limit and it need not send it to the controller.

Fig. 4. The membership function of OI(Output Intensity of sensor )

3) Rule evaluation: The fuzzy input values are processed
using the set of rules. The rules in fuzzy control consist of a
condition, IF, followed by a control action, THEN. Each rule
processes the information using different input parameters;
the output of each rule is different. In order to construct the
fuzzy rules we construct rule matrix (Table IV) and rule
bases. Fig. 5. Computing the value of OI for I=13.9 and D=250

If the output light intensity I is higher(9.00 and above), the
sensor sends it to the controller and the fuzzy controller (Fig.
6) converts it into an ambient light source.

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COI=7.45 . This implies that the output light intensity is
moderate; the sensor judges it not to be of the acceptable
limit and it sends it to the controller.

Fig. 6. The structure of the Fuzzy Controller

The Fuzzy controller accepts the OutputIntensity(OI), if it
is of either Disturbing(D),or UNBEARABLE(UB) it
converts it to an ambient light source. The Fuzzy controller’s
ouput is the ControllerOutputIntensity(COI)(Table V)

TABLE V. THE LINGUISTIC VARIABLES FOR COI AND ITS NUMERICAL RANGE

Linguistic value Notation Numerical
range Fig. 8. Computing the value of COI for OI=9.5
ReduceLightSource RLS 9.00-14.50
AmbientLightSource ALS 0-9.00
4) Defuzzification: The fuzzy sets are converted to crisp
values. Here the fuzzy sets represented by OI(Sensors output)
and ols(fuzzy controllers output) are converted to crisp
sets(Numerical values). Centre of Area method has been
used. General formula for COA is (Eq:3)
z* = ∫µc(z)zdz
________
∫µc(z)dz (3)

Fig. 7. The membership function for the ControllerOutputIntensity(COI) III. IMPLEMENTATION

The rule bases for the fuzzy controller(Table VI) is as The MATLAB Fuzzy Logic Toolbox has been used to
shown below. encode fuzzy sets, membership functions, fuzzy rules and to
perform inference process for both the fuzzy sensor and the
TABLE VI. THE RULE BASES FOR THE FUZZY CONTROLLER fuzzy controller.

Rule OI COI IV. CONCLUSION
1 JN ALS
2 N ALS This paper has proposed a fuzzy rule based approach to
3 S ALS
prevent the headlight glare which in turn minimizes the
4 JA ALS
Accidents. The fuzzy sensor and the controller uses the
5 D RLS
6 UB RLS fuzzy rule bases to control the intensity of light. The
conventional controllers would not be very efficient in
controlling the headlight glare as there would be discrete
The figure below (Fig. 8) shows the rule base for the values either high/low beam but the fuzzy controller has the
sensor. In the figure below, the value of OI=9.5 and that of continuous light intensities rather than high/low beam. The

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fuzzy sensor and the controller has to be embedded inside the
windshield or fit on to it, during the windshield lamination
process. This fuzzy system comprising the sensor and the
controller reduces the headlight glare and therefore reduces
the accidents on the highways during the travel at night. This AUTHORS PROFILE
fuzzy system would be of greater boon to the drivers as the
driving becomes comfortable without ruining the vision of
Niraimathi.S (niraisenthil@hotmail.com) is a second year
the driver at both the ends. This setup comprising the fuzzy Doctoral Research Scholar at Mother Teresa women’s
sensor and the fuzzy controller, has to be put up on all the University. She holds M.Phil in Computer Science from
the Bharathiar University. She had her Bachelor’s degree
vehicles in order to prevent the happening of accidents. in Computer Science and Master’s degree in Computer
Applications from the Bharathiar University, India. She has
9+ years of experience in teaching. She is at present
working as an Assistant Professor in Nallamuthu Gounder Mahalingam
College, Pollachi, India. Her area of expertise includes Fuzzy systems,
OOAD and compiler design.
REFERENCES
Dr. M. Arthanari holds a Ph.D. in Mathematics from
[1] Nikunja K. Swain, “A Survey of Application of Fuzzy Logic in Madras University as well as Masters Degree in Computer
Intelligent Transportation Systems (ITS) and Rural ITS”- Southeast Con, Science from BITS, Pilani. He was the professor and Head
Proceedings of IEEE, 2006, pp 85-89. of Computer Science and IT Department at Tejaa Shakthi
Institute of Technology for Women, Coimbatore, India. At
[2] Kher.S, Bajaj .P, “Fuzzy control of head-light intensity of automobiles: present he is the Director, Bharathidhasan School of
design approach “proceedings of 37th SICE annual conference international Computer Applications, Ellispettai, Erode, Tamilnadu. He
session papers, July 1988, pp 1047-1050. holds a patent issued by the Govt. of India for his invention in the field of
Computer Science. He has directed teams of Ph.D. researchers and industry
[3]George J. Klir, Bo Yuan, “Fuzzy Sets and Fuzzy logic theory and experts for developing patentable products. He teaches strategy, project
applications”, PHI Learning Private Ltd. management, creative problem solving, innovation and integrated new
product development for last 36 years.
[4] T.M. Husain, T. Saadawi, S. Ahmed, “Overhead infrared sensor for
monitoring vehicular traffic,” IEEE Ttrans. On vehicular Tech, vol. 42, No. 4 Sivakumar.M (sivala@gmail.com) has 10+ years of
pp.477-482, Nov 1993.
experience in the software industry including Oracle
[5] R. Kruse, J. Gebhardt, F. Klawon, ”Foundations of Fuzzy Systems”, Corporation. He received his Bachelor degree in Physics
Wiley, Chichester 1994. and Masters in Computer Applications from the Bharathiar
University, India. He holds a patent for the invention in
[6] Gerla G., Fuzzy Logic Programming and fuzzy control, Studia Logica, embedded technology. He is technically certified by various professional
79 (2005) 231-254. bodies like PRINCE2, ITIL, IBM Rational Clearcase Administrator, OCP -
Oracle Certified Professional 10G and ISTQB.
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[8] Lennart Ljung," An Introduction to Fuzzy Control”, NHI, 1992.

[9] F. Martin Mcheill, Thro, Yager," Fuzzy Logic- A Practlcal Approach',
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[10] Das, S. and Bowles, B.A., “Simulations of highway chaos using fuzzy
logic” 18th International Conference of the North American Fuzzy
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[11] Zimmermann, H.J.: Fuzzy Set Theory And Its Applications. 2nd ed.,
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[12] Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy
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