Fuzzy logic Intelligent traffic light control system & Robot car Authors: C.M.Pradesh & K.Ram mohan ECE (III) year. Sri Venkateswara College of Engineering &Technology Thiruvallur-631203 C.M.Pradesh S/o C.Mayan No: 2/1,8 th street, Vaishnavi nagar, Avadi, Chennai-600109. Contact number: 9677248629 Email:cm.pradesh@gmail.com K.Ram mohan S/o R.Kamaraj No: 32/70, Cholembedu road, Thirumullaivoyal, Chennai-600062. Contact number: 9043363789 Email:ram1423@gmail.com ABSTRACT: In this paper we discuss 1. The implementation of an intelligent traffic lights control system using fuzzy logic technology which has the capability of mimicking human intelligence for controlling traffic lights. The control of the traffic lights using both conventional fixed-time and fuzzy logic controllers can be simulated in the software. 2. The implementation of an Artificial intelligence robot car designed with artificial neural network, fuzzy logic controllers and some other peripherals. Keywords- Traffic light control system; Fuzzy logic controller; Artificial neural network; Robot car INTRODUCTION: The monitoring and control of city traffic is becoming a major problem in many countries. With the ever increasing number of vehicles on the road, the Traffic Monitoring. Authority or the Transport Ministry as the authority is known here in India has to find new ways or measures of overcoming such a problem. The measures taken are development of new roads and flyovers in the middle of the city; building of several ring such as the inner ring road, middle ring road and outer ring road; restricting of large vehicles in the city during peak hours; and also development of sophisticated traffic monitoring and control systems. In the city of Mumbai, Chennai, Kolkata, the registration of new vehicles each year increased by about twenty per cent. To overcome this situation introduction of these systems such as intelligent traffic controller system and the AI robot car system should be implemented. TRAFFIC LIGHTS CONTROL SYSTEM Basically, there are two types of conventional traffic lights control system that are in used. One type of control uses a preset cycle time to change the lights. The other type of control combines preset cycle time with proximity sensors which can activate a change in the cycle time or the lights. In the case of a less traveled street which may not need a regular cycle of green lights, proximity sensors will activate a change in the light when cars are present. This type of control depends on having some prior knowledge of traffic flow patterns at the intersection so that signal cycle times and placement of proximity sensors may be customized for the intersection. Fuzzy logic traffic lights control is an alternative to conventional traffic lights control which can be used for a wider array of traffic patterns at an intersection. A fuzzy logic controlled traffic light uses sensors that count cars instead of proximity sensors which only indicate the presence of cars. This provides the controller with traffic densities in the lanes and allows a better assessment of changing traffic patterns. As the traffic distributions fluctuate, the fuzzy controller can change the signal light accordingly. There are two electromagnetic sensors placed on the road for each lane. The first sensor behind each traffic light counts the number of cars passing the traffic lights, and the second sensor which is
located behind the first sensor counts the number of cars coming to the intersection at distance D from the lights. The number of cars between the traffic lights is determined by the difference of the reading between the two sensors. This is in contrast to conventional control systems which place a proximity sensor at the front of each traffic light and can only sense the presence of a car waiting at the junction, not the number of cars waiting at the traffic. The distance between the two sensors D, is determined accordingly following the traffic flow pattern at that particular intersection. The fuzzy logic controller is responsible for controlling the length of the green time according to the traffic conditions. The state machine controls the sequence of states that the fuzzy traffic controller should cycle through. There is one state for each phase of the traffic light. There is one default state which takes place when no incoming traffic is detected. This default state corresponds to the green time for a specific approach, usually to the main approach. In the sequence of states, a state can be skipped if there is no vehicle queues for the corresponding approach. A fuzzy logic controller was designed for an isolated 4-lane traffic intersection: north, south, east and west. In the traffic lights controller two fuzzy input variables are chosen: the quantity of the traffic on the arrival side (Arrival) and the quantity of traffic on the queuing side (Queue). If the north and south side is green then this would be the arrival side while the west and east side would be considered as the queuing side, and vice-versa. The output fuzzy variable would be the extension time needed for the green light on the arrival side (Extension). Thus based on the current traffic conditions the fuzzy rules can be formulated so that the output of the fuzzy controller will extend or not the current green light time. If there is no extension of the current green time, the state of the traffic lights will immediately change to another state, allowing the traffic from the alternate phase to flow. DESIGN CRITERIA AND CONSTRAINTS: In the development of the fuzzy traffic lights control system the following assumptions are made: i) The junction is an isolated four-way junction with traffic coming from the north, west, south and east directions; ii) When traffic from the north and south moves, traffic from the west and east stops, and vice versa; iii) No right and left turns are considered; v) The fuzzy logic controller will observe the density of the north and south traffic as one side and the west and east traffic as another side; v) The East-West lane is assumed as the main approach; vi) The minimum and maximum time of green light is 2 seconds and 20 seconds respectively. FUZZY LOGIC TRAFFIC LIGHTS CON-TROLLER DESIGN: FUZZY RULE BASE: The inference mechanism in the fuzzy logic controller resembles that of the human reasoning process. This is where fuzzy logic technology is associated with artificial intelligence. Humans unconsciously use rules in implementing their actions. For example, a traffic policeman manning a junction say, one from the north and one from the west; he would use his expert opinion in controlling the traffic more or less in the following way: IF traffic from the north of the city is HEAVY AND traffic from the west is LESS THEN allow movement of traffic from the north LONGER. The beauty of fuzzy logic is the possible utilization of approximate reasoning in the rules such as HEAVY, LESS, AVERAGE, NORMAL, LONGER, etc. Due to the membership assignment techniques as discussed, such linguistic variables, though fuzzy in nature, can be taken care of in the computer through fuzzy logic technology. FUNCTIONING OF THIS FUZZY LOGIC TRAFFIC CONTROLLER:
In a conventional traffic light controller, the lights change at constant cycle time, which is clearly not the optimal solution. Fuzzy logic can be a better method than conventional control methods, especially in the case of highly uneven traffic flow between different directions. It would be more feasible to let more cars pass at the green light if there is less number of cars behind the red lights. A mathematical model for this decision is difficult to find but fuzzy logic simplifies the task. Once well working and appropriate rules are formulated for our four way intersection, it is not too difficult to modify the fuzzy rule base so that the program can be applied to any given intersection. In order to deter mine the traffic flow in each direction, two incremental sensors are to be placed in each direction at a distance of 200 feet from each other, for a total of eight sensors. The number of cars that is accumulated behind a traffic light is calculated by taking the difference between the two sensor readings. In this fuzzy logic traffic signal traffic controller system only the traffic signal is automatically controlled, still there are some people they won't be obeying the traffic rule and traffic signals. For them this type of system is also worthless. The input of this model consists of: 1. Current cycle time of light. 2. Accumulation Of Cars behind The Red Light on more crowded street side. 3. Accumulation Of Cars behind The Green Light on more crowded street side. The output parameter is the probability of change of the current cycle time. The input and the output parameters are defined by overlapping linear membership functions. The knowledge base consists of 110 fuzzy rules, as for example: 1. If traffic accumulation behind red is very minimal and green is very minimal and cycle time is short then change is NO. 2. If traffic accumulation behind red is moderate and green is moderate and cycle time is long then change is PROBABLY YES To overcome this problem, It is necessary to implement AI driver less Robot car... ARTIFICIAL INTELLIGENCE ROBOT CAR: Artificial Intelligence (AI), a term that in its broadest sense would indicate the ability of an artifact to perform the same kinds of functions that characterize human thought. The possibility of developing some such artifact has intrigued human beings since ancient times. With the growth of modern science, the search for AI has taken two major directions: psychological and physiological research into the nature of human thought, and the technological development of increasingly sophisticated computing systems. ARTIFICIAL NEURAL NETWORK: The neural networks that are increasingly being used in computing mimic those found in the nervous systems of vertebrates. The main characteristic of a biological neural network, top, is that each neuron, or nerve cell, receives signals from many other neurons through its branching dendrites. The neuron produces an output signal that depends on the values of all the input signals and passes this output on to many other neurons along a branching fiber called an axon. Human Neural Network
custom developed computer system and logic algorithms, objects and motion are discerned. These are applied to the AI which then determines what is traffic, what is the road and what legal conditions there are around the car. This new legal stipulation exists because Junior must be able to complete a course with not only moving traffic, but it also must obey traffic lights. Artificial Neural Network ROBOT CAR: It has four doors, a hatchback, is diesel powered and, it got an artificial intelligence (AI) controlling it. The throttle, brakes, steering, ignition, everything is controlled by the AI. This special vehicle is part of a research project with goals to develop the ultimate "smart car" for automated driving. While previous attempts have navigated similar vehicles around fixed obstacle courses without obstructions in motion, this new challenge take everything to a whole new level. Parts of this Robot car Sensor at the bottom Camera at the top Global Processing System Spinning 360 rotating range finding laser Numerous processors Special type of Navigation system Functions of those parts Scan for small obstacles such as stones, speed brakes, etc... Scan where is road is going, checks the traffic light signals. Detect the route map for the destination to be reached. provide an accurate topography of the surrounding environment. Process the collected data and programs the car to run. Search the route for the destination. FUNCTIONING OF THIS ROBOT CAR: PARTS: Mounted all around the car are all kinds of sensors and control input devices, cameras, etc.. a spinning, 360 degree range-finding laser which can provide an accurate topography of the surrounding environment. That data is augmented with specially mounted cameras which also provide a full 360 degree view. And there are still more lasers and radar units mounted at various points around the vehicle. All of it is employed to make sure it can "see" everything necessary to complete the course. FUNCTIONS: The main point to be noted is, this Robot car is not preprogrammed. When all of this data is processed through a
ADVANTAGES OF THIS ROBOT CAR: While the death toll is already sufficiently compelling, cars come with many other costs. Traffic congestion, caused by more than just accidents, wastes from 4 to 8 billion hours of people's time each year, and results in the burning of 6 billion gallons of extra fuel. Robocars will not eliminate congestion immediately, but as they grow to become a majority of cars on the road, they can do this in several ways I will outline below. In this method robot car can determine its original position and orientation from the signal provided by transmitter that are placed in some characteristic point of the manufacturing hall, then comparing its actual position to the ground plan of the hall and indicated route, it determine the required route. The advantages of this method are its flexibility. CONCLUSION: 1. The fuzzy logic traffic lights controller performed better than the fixed-time controller or even vehicle actuated controllers due to its flexibility. The flexibility here involves the number of vehicles sensed at the incoming junction and the extension of the green time. In the fixed-time controller, being an open-loop system, the green time is not extended whatever the density of cars at the junction. For vehicle actuated traffic light controllers, which is an enhanced version of fixed-time controller, the green time is extended whenever there is a presence of a vehicle. Since with this fuzzy logic traffic lights controller alone one can't overcome the traffic problem since new model of Artificial intelligence controlled robot car should be implemented. 2. This AI robot car will surely overcome the traffic crisis since this robot won't violate the traffic rules, they won't park the car in the traffic area. We could expect the pollution free, traffic free, accidents less environment in the forth coming future. REFERENCES: 1. Technical paper on FCPLL by A.B.KULKARNI and S.V.HALSE from Gulbarga Universit. 2. Electronics Principles by Malvino from Tata McGraw Hill. Parking is another bane of driving yourself. You must find and pay for parking, or rely on the tremendous subsidy of free parking given by many urban districts. Robocars need to stop somewhere when not in use, but they won't need parking right at your destination -- they will drop you off and then go elsewhere to work, refuel/recharge or park. When they do park they will be very efficient, like a tight valet parking lot. They may even block driveways as they could quickly move on request. Single person robocars will park even more densely. Custom garages could do even better. Robocars could also refuel and recharge on their own. This turns out to be a remarkable enabler, because refueling stations need no longer be all that numerous or conveniently located. This enables new fuel types to be tried out in the market very quickly. 3. Fuzzy Logic with Engineering Applications by Timothy Ross. 4. Frederick J. Hill & Gerald R. Peterson. (1981) - Switching Theory & Logical Design Wiley. 5. J.R. Circuits. Arnold. 6. Ian Gibson. (1979)- Electronic logic Graham &Peter Llewelyn Jones. (1988) - Expert Systems, Knowledge Uncertainty and Decision, Chapman and Hall.
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