Optimization of ACC using Soft Computing Technique

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

            Optimization of ACC using Soft Computing
                            Technique

                    S.Paul Sathiyan                                                         A.Wisemin Lins
                    EEE Department                                                          EEE Department
                   Karunya University                                                      Karunya University
                    Coimbatore, India                                                       Coimbatore, India
                paul.sathiyan@gmail.com                                                  wisemineee@gmail.com
                                                      Dr. S. Suresh Kumar
                                                        EEE Department
                                                       Karunya University
                                                        Coimbatore, India
                                                    paul.sathiyan@gmail.com

Abstract— The important feature of the Adaptive Cruise                  Control mode and Distance Control mode. In Velocity Control
Control (ACC) system is the ability to maintain a proper                mode ACC maintains the vehicle’s preset velocity set by the
inter-vehicle gap based on the speed of leading vehicle and             driver. The stability of the ACC system is disturbed when a
the desired distance. Adaptive Cruise Control operates in               lead vehicle or an obstacle is present in the way of the vehicle
two modes (i) Velocity Control mode, (ii) Distance Control              fitted with ACC. Such a drawback is rectified by switching
mode. ACC acts like a conventional Cruise Controller                    over to Distance Control. In this mode ACC automatically
(CC) under velocity control mode. In the case of the                    adjusts the host vehicle velocity in order to maintain a safe
distance control mode, the speed of the host vehicle is                 distance between the two vehicles. These systems are
reduced according to the surrounding environment to                     characterized by a moderately low level of throttle and brake
maintain the safe distance between the leading vehicle and              authority. The limitation of conventional ACC systems is that
the host vehicle. 25 rules have been used in Fuzzy logic                they do not manage speeds under 30 km/h and, consequently,
Controller (FLC) with the knowledge base of the system.                 are not useful in traffic jams or urban driving, situation. At
The inputs of the FLC are distance error and the speed                  congested traffic, the ACC system becomes less useful. Now,
error. The host vehicle adapts to the lead vehicle speed                ACC systems are made capable of maintaining controlled
changes and tries to maintain a proper distance between                 vehicle’s position relative to the leading vehicle even in
them. The performance of the FLC based ACC is                           congested traffic by using stop and go features while
improved by Genetic Algorithm to tune the fuzzy rule                    maintaining a safe distance between leading and following
base. Genetic Programming is used to select the best rule               vehicles autonomously. The conventional CC system operates
out of the 25 for a corresponding input. The result showed              only in one mode of control i.e., velocity control mode, on the
a better improvement over the Fuzzy Controlled System.                  other hand, ACC has two both velocity and distance control
                                                                        modes. In this paper the different inter vehicle distances and
   Keywords - Adaptive Cruise Control; Genetic Algorithm; Fuzzy         speed levels have been considered. Simulation results obtained
Logic Control                                                           from ACC system using Fuzzy Logic Controller (FLC) and
                     I.    INTRODUCTION                                 genetically tuned FLC have been compared to validate the
                                                                        objective of this paper.
   Researches on Intelligent Vehicle (IV) Systems have been
devoted to solve problem such as driver burden reduction,
accident prevention, traffic flow smoothing. Mentally, driving                              II.   FLC BASED ACC
is a highly demanding activity - a driver must maintain a high             Fuzzy Logic Controller is designed on the basis of fuzzy
level of concentration for long periods and be ready to react           logic, which does not require any mathematical models but
within a split second to changing situations. In particular,            mainly depends on the experience. Fuzziness describes event
drivers must constantly assess the distance and relative speed          ambiguity. It measures the degree to which an event occurs,
of vehicles in front and adjust their own speed accordingly. A          not whether it occurs. Fuzzy theory is a powerful tool in the
Cruise Control (CC) system has been developed to assist the             exploration of complex problems because of its ability to
driver for driving in long distance on highway when there is            determine outputs for a given set of inputs without using a
no vehicle present before the host vehicle. Adaptive Cruise             conventional, mathematical model. Fuzzy theory becomes
Control (ACC) supports the driver in longitudinal control of            easily understood because it can be made to resemble a high
vehicles by operating in two modes of control, (i.e.,) Velocity         level language instead of a mathematical language. To




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                                                                                                   ISSN 1947-5500
                                                            (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                            Vol. 9, No. 2, February 2011
describe a universe of discourse, fuzzy sets with names such
as “hot” and “cold” are used to create a membership function.
By determining the degree of membership of an input in the
fuzzy sets of this membership function, the role of
membership functions play in decoding the linguistic
terminology to the values a computer can use [7]. Fuzzy Logic
controller is represented by a set of rules represented in the
form of if-then rules [3]. The fuzzy rule consists of antecedent
and consequent. Antecedent is a condition in its application
domain and consequent is a control action for the system
under control. The fuzzy inference engine employs the fuzzy
knowledge base to simulate human decision making and infer
fuzzy control actions. Finally, the defuzzifier module is used
to translate the processed fuzzy data into the crisp data suited                                         (a)
to real world applications [4].
A. Frame work of the Fuzzy Logic Controller




                                                                                                         (b)
          Figure. 1. Framework of Fuzzy Logic Controller

     Fuzzify the inputs according to the input membership
Functions. The rule strength is found out by combining the
fuzzified inputs according to the Fuzzy rules. The
consequence of the rule is found out by combining the rule
strength and the output membership function. The Fuzzified
output has to defuzzified to convert the Fuzzified value to a
crisp value. Defuzzifying method is the weighted average of
all rule outputs[8].
B. Inputs of the Fuzzy logic controller
    Two input and a single output Fuzzy logic controller is
used. The inputs of the Fuzzy Logic Controller are distance                                              (c)
error (Xerror) and the speed error (Serror). The distance error                               Figure 2. Actual Distance
(1) is the difference between the actual distance (Inter-vehicle
Distance, Xactual) and the desired distance (Xdesired). Three            The Speed error is obtained by the difference
different distance levels are considered for simulation purpose          between the leading vehicle speed (Slead) and the
which is shown in Fig.2. (Fig 2 (a) – distance varies from 7 to          host vehicle speed (Shost) according to (4).
13.3m, Fig 2 (b) – distance varies from 5 to 6m, Fig 2 (c) –
distance varies from 2 to 4.4m) The actual distance can be
measured using an ultrasonic sensor. The desired distance is the
distance which required to be maintained between the vehicles
to avoid the rear end collision. Desired distance changes in
direct proportion to the vehicle speed.


                                                                         The velocity of the leading vehicle is found out by sum of the
                                                                         host vehicle and the distance error according to (5).




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                                                                                                     ISSN 1947-5500
                                                                 (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                 Vol. 9, No. 2, February 2011
                                                                           D. Output of FLC Based ACC



The output of the Fuzzy Controller determines acceleration or
braking which drives the vehicle.
C. Fuzzification of Inputs
Mamdani Fuzzy inference method is used in this case. The
Fuzzy sets are represented by using the linguistic variables
namely (i) Negative Medium (NM) (ii) Negative Small (NS)
(iii) Zero Error (iv) Positive Small (v) Positive Medium [10].
Xerror and Serror are the two inputs for the Fuzzy Logic
Controller. The output is the firing on ACC which gives the                                                   (a)
desired braking and acceleration. The input and output of the
Fuzzy Logic Controller are represented triangular membership
functions. The output is the acceleration or Braking command
according to the current input. The positive side of the output
represents the acceleration command and the negative side
represents the braking command. 25 rules have been generated
with the knowledge base of the system. Table 1 gives the
relation between the input and the fuzzy output

                            TABLE.1. FUZZY RULE BASE


                                    Speed error
                                                                                                             (b)
                               NM       NS        ZE   PS   PM

                       NM      PM       PM        PM   PS   PS
      Distance error




                       NS      PS       PM        PS   ZE   PS

                       ZE      PM       ZE        ZE   NS   NM

                       PS      PM        PS       NS   NS   NS

                       PM      PS       NS        NS   NM   NS


   This system is modeled based upon the equations. The value                                                  (c)
                                                                                Figure 3. Output of the fuzzy controlled ACC for the given conditions
of X desired depends on the Speed of the host vehicle. The
desired distance varies proportional to the speed of the host
vehicle. The value of X error is negative when X actual is less            The host vehicle is getting adapted to the lead vehicle for
than the X desired, therefore the Speed of the host vehicle has            various inter-vehicle distances considered and shown in Fig 2.
to be reduced. The value of X error is positive when the X                 For the third case the level of adaptation was very poor.
actual is greater than the X desired; therefore the speed of the
host vehicle has to be increased. Thus this controls the output                      III.   GA BASED FUZZY CONTROLLED ACC
of the ACC vehicle. The host vehicle is adapted to the lead                   Genetic Algorithms are computing algorithms to solve
vehicle with minimum error.                                                optimization problems by making use of evolutionary
                                                                           principles as known from biology. Evolution is a process that
                                                                           operates on chromosomes (organic devices for encoding the
                                                                           structure of living beings) rather than on living beings. The
                                                                           processes of natural selection cause those chromosomes that
                                                                           encode successful structures to reproduce more often than
                                                                           those that do not. Recombination processes create different
                                                                           chromosomes in offspring by combining genes from the
                                                                           chromosomes of the two parents. Mutation may cause the




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                                                                                                         ISSN 1947-5500
                                                           (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                           Vol. 9, No. 2, February 2011
chromosomes of children to be different from those of their             D. Simulation output
parents [7]. Genetic algorithms are used to maximize the                  The new generation has been formed after crossover and
performance of a fuzzy logic controller through the search of a         mutation. The generated 25 rules have been reduced to 2 rules
rule from a given knowledge base to achieve the goal of                 using the Genetic Algorithm. The surface view of the
minimizing the number of rules required.GA will eliminate all           optimized rule is shown in Fig.4
the unnecessary rules which have no significant contribution
to improve the system performance [5].
A. Optimization of Fuzzy rule base
    X actual is the difference between the leading vehicle and
the host vehicle for which the simulation is done. Xerror and S
error are the two inputs for the GA based fuzzy controlled
ACC. Fuzzification is a process which converts the crisp value
into a fuzzy value. X error and Serror are the two inputs
given.25 rules have been generated with the knowledge base of
the system. The membership function of the linguistic
statement is converted to a binary string by assigning a binary
number [12].
                                                                                             Figure. 4. Surface view of optimized rules
Negative medium    :000
Negative small     :001                                                         30

Zero error         :010
Positive small     :011                                                         25

Positive medium    :100
                                                                                20
10 random rules are obtained from the fuzzy rule base. The rule
                                                                        Km/hr




strength is calculated with respect to the antecedent and
                                                                                15
consequent of the fuzzy rule. The selection of the chromosome
is done and the GA operators such as Crossover and Mutation
take place and the next generation is formed                                    10
                                                                                                                                            Lead Vehicle
                                                                                                                                            Host vehicle
B. Crossover                                                                    5
                                                                                     0   5       10       15          20       25      30          35      40
     Crossover is a process by which the systematic information                                                    time(sec)

exchanges between two chromosomes and is implemented by                                                            (a)
using probabilistic decisions. Cross over is done with
crossover probability. Two parents are randomly selected and
let the parents be 1 & 3. A random number is generated to split
the chromosome and to form the next generation. If the
generated random number is less than the crossover
probability Crossover has to be done by selecting the
crossover site randomly by Interchange the bits
                   Parent=10000010
                           00110100
     Crossover offspring= 10000100
                            00110010
C. Mutation
         Mutation is a process in which the occasional                                                             (b)
   alteration of a value at a randomly selected bit
   position.Mutation is done with mutation probability
   (pc=0.6).Two parents are randomly selected and let the
   parent be 3. A random number is generated and if the
   generated random number is less than the mutation
   probability mutation has to be done by selecting the
   mutation site randomly by flipping the bit[11].
            Parent=10000100
         offspring=10000110

                                                                                                                   (c)




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                                                                                                               ISSN 1947-5500
                                                                         (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                         Vol. 9, No. 2, February 2011
        Figure. 5. GA based Fuzzy Controller Output for the given scenarios           [6]    Bin-Da Liu,Chuen-Yau Chen, and Ju-Ying Tsao,” Design of Adaptive
                                                                                             Fuzzy Logic Controller Based on Linguistic-Hedge Concepts and
                          IV.CONCLUSION                                                      Genetic Algorithms” IEEE Trans. Systems, Man, And Cybernetics—Part
                                                                                             B: Cybernetics, VOL. 31, NO. 1, pp.32-52, February 2001
Adaptive Cruise Control has been designed using Fuzzy Logic                           [7]    Abdollah Homaifar ,Ed McCormick,” Simultaneous Design of
Control. 25 rules have been generated with the knowledge                                     Membership Functions and Rule Sets for Fuzzy Controllers Using
base of the system. The host vehicle tried to maintain the                                   Genetic Algorithms” IEEE Trans .Fuzzy systems, Vol. 3, No. 2,pp.129-
distance so that the Xerror remains almost zero. FLC based                                   138, May 1995.
ACC system developed much error when the distance was less                            [8]    José E. Naranjo, Carlos González, Member, IEEE, Ricardo García, and
                                                                                             Teresa de Pedro “ACC+Stop&Go Maneuvers With Throttle and Brake
than 5m. In order to reduce this error, Genetic Algorithm is                                 Fuzzy Control”, IEEE Trans.Intelligent Transportation Systems, vol. 7,
used to optimize the Fuzzy rule base. Host Vehicle adapts to                                 no. 2,pp 213-225 June 2006.
the change in lead vehicle speed more efficiently.                                    [9]    Shuqing Zeng, Yongbao He(1994),”Learning and Tuning Fuzzy Logic
                                                                                             Controllers Through Genetic Algorithm”,Department of Computer
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