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The International Journal of Computer Science and Information Security (IJCSIS Vol. 9 No. 2) is a reputable venue for publishing novel ideas, state-of-the-art research results and fundamental advances in all aspects of computer science and information & communication security. IJCSIS is a peer reviewed international journal with a key objective to provide the academic and industrial community a medium for presenting original research and applications related to Computer Science and Information Security.
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IJCSIS invites authors to submit their original and unpublished work that communicates current research on information assurance and security regarding both the theoretical and methodological aspects, as well as various applications in solving real world information security problems.
The International Journal of Computer Science and Information Security (IJCSIS Vol. 9 No. 2) is a reputable venue for publishing novel ideas, state-of-the-art research results and fundamental advances in all aspects of computer science and information & communication security. IJCSIS is a peer reviewed international journal with a key objective to provide the academic and industrial community a medium for presenting original research and applications related to Computer Science and Information Security. . The core vision of IJCSIS is to disseminate new knowledge and technology for the benefit of everyone ranging from the academic and professional research communities to industry practitioners in a range of topics in computer science & engineering in general and information & communication security, mobile & wireless networking, and wireless communication systems. It also provides a venue for high-calibre researchers, PhD students and professionals to submit on-going research and developments in these areas. . IJCSIS invites authors to submit their original and unpublished work that communicates current research on information assurance and security regarding both the theoretical and methodological aspects, as well as various applications in solving real world information security problems.
(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 email@example.com firstname.lastname@example.org Dr. S. Suresh Kumar EEE Department Karunya University Coimbatore, India email@example.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 150 http://sites.google.com/site/ijcsis/ 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 . Fuzzy Logic controller is represented by a set of rules represented in the form of if-then rules . 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 . 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. 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). 151 http://sites.google.com/site/ijcsis/ 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 . 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 152 http://sites.google.com/site/ijcsis/ 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 . 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 . 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 . 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. Parent=10000100 offspring=10000110 (c) 153 http://sites.google.com/site/ijcsis/ 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  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  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  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.  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