fuzzy logic in car safty
1.0 INTRODUCTION
Car safety issues are wide-reaching problem. This problem is mainly due to human driving which involves reaction times, delays, and judgment errors that may affect traffic flow and cause accidents. In some cases, the cause of the accident is distraction on the part of the driver and failure to react in time. Even in some cases, it could be cause by environmental factors (Song, 2005). Advanced system of auxiliary functions has been developed to help avoid such accident and minimize the effects of collision should one occur. Fuzzy logic provides tools for dealing with imprecision, which is fundamental to many engineering problems.
The level of safety in our society could be archived by applying fuzzy logic control system. Fuzzy logic control technique has become an active area of research in the application of industrial processes, which are not friendly to straight control techniques. It attempts to emulate human mind for checking the processes parameters and to take decisions regarding the control action (Sugeno, 1985). Fuzzy control become a huge industry in Japan and other countries where it was adapted into home appliances such as vacuum cleaners, microwaves ovens, video cameras, washing machines, etc. a fuzzy controller acts or regulates by means of rules in a more or less natural language, based on the distinguishing feature: fuzzy logic.
On the other hand, to reduce car accidents we are going to examine a system, which makes the drivers, pay more attention and alert them before an accident takes place. The system consists of three common circuits, which work hand to hands with each other. The main circuit consists of microcontroller, power source, speaker and switches needed. The next circuit, the ultrasonic transmitter and receiver circuit which measures the distance between the car and anything in front of it. Lastly, is the opto-coupler circuit, its function is to determine the current speed of the car.
2.0 PROBLEM
Car safety issues are wide-reaching problem. This problem is mainly due to human driving which involves reaction times, delays, and judgment errors that may affect traffic flow and cause accidents. In some cases, the cause of the accident is distraction on the part of the driver and failure to react in time. Even in some cases, it could be cause by environmental factors (Song, 2005). Advanced system of auxiliary functions has been developed to help avoid such accident and minimize the effects of collision should one occur. Fuzzy logic provides tools for dealing with imprecision, which is fundamental to many engineering problems.
The level of safety in our society could be archived by applying fuzzy logic control system. Fuzzy logic control technique has become an active area of research in the application of industrial processes, which are not friendly to straight control techniques. It attempts to emulate human mind for checking the processes parameters and to take decisions regarding the control action (Sugeno, 1985). Fuzzy control become a huge industry in Japan and other countries where it was adapted into home appliances such as vacuum cleaners, microwaves ovens, video cameras, washing machines, etc. a fuzzy controller acts or regulates by means of rules in a more or less natural language, based on the distinguishing feature: fuzzy logic.
On the other hand, to reduce car accidents we are going to examine a system, which makes the drivers, pay more attention and alert them before an accident takes place. The system consists of three common circuits, which work hand to hands with each other. The main circuit consists of microcontroller, power source, speaker and switches needed. The next circuit, the ultrasonic transmitter and receiver circuit which measures the distance between the car and anything in front of it. Lastly, is the opto-coupler circuit, its function is to determine the current speed of the car.
2.0 PROBLEM
References: Chung, S. (2003). Development of Risk Evaluation Model for Traffic Flow on the Base of Microscopic Driving Behavior, Doctoral Dissertation, Seoul National University. Fuiler, R. (2005). Towards a General Theory of Driver Behaviors. Accident Analysis and Prevention, vol. 37, no.3 (May 2005), pp.461-472, ISSN 0001- 4575 Kononov, J., & Allery, B Mazin, A., Yasir K., Ibrahim, Y., Adnan, I. (2008). Car Safety System Using Fuzzy Logic: Journal of Computer Science 4 (12): 1061-1063, 2008 ISSN 1549-3636 Mamat, M., and Ghani, N.M Mehmood, A., Saccomanno, F., Hellinga, B. (2001). Evaluation of a car-following model using systems dynamics. Proceedings of the 19th International Conference of the System Dynamics Society, Atlanta, Georgia, USA, July 23-27, 2001. SONG, K. (2005). Proceedings of the Eastern Asia Society for Transportation Studies, Vol. 5, pp. 2075 – 2090, 2005 Sugeno, M Zheng, P., McDonald, M., Wu, J. (2006). Evaluation of collision warning-collision avoidance systems using empirical driving data No.1944, (2006), pp. 1-7, ISSN 0361-198 http://www.sensorytools.com/ FEBRUARY, 2014.