BIO INSPIRED NEURAL NETWORKING AMONG
Transportation is one of the most important economic activities of any country. Among the various forms of transport, road transport is one of the most popular means of transportation. Transportation has an element of danger attached to it in the form of vehicle crashes. Road crashes not only cause death and injury, but they also bring along an immeasurable amount of agony to the people involved. Efforts to improve traffic safety to date have concentrated on the occupant protection, which had improved the vehicle crash worthiness. The other important area where research is currently being done is collision avoidance. Technological innovations have given the traffic engineer an option of improving traffic safety by utilizing the available communication tools and sophisticated instruments. Using sensors and digital maps for increasing traffic safety is in its infancy. Systems are being developed to utilize the available state of the art facilities to reduce or possibly prevent the occurrence of crashes. Total prevention of crashes might not be possible for now, but the reduction of crashes could easily be achieved by using the collision avoidance systems.
1.1 NEED FOR COLLISION AVOIDANCE
The development of collision avoidance systems is motivated by their potential for increased vehicle safety. Half of the more than 1.5 million rear-end crashes that occurred in 1994 could have been prevented by collision avoidance systems .Collision avoidance systems can react to situations that humans cannot or do not, due to driver error. Therefore, they are able to reduce the severity of accidents. Figure 1 below indicates that about 45 percent of the crashes that occur are caused by human errors. Human errors that cause crashes include failure to keep in proper lane, failure to yield right of way, inattentive, failure to obey traffic control devices, operating vehicle in negligent manner, drowsy driving, over correcting, driving wrong way and making improper turns. Some of these crashes may have been possibly avoided if the driver was provided with the real time information.
Figure 1.1 Statistical Survey Results
1.2 HISTORICAL BACKGROUND IN DETAIL
The history of neural networks that was described above can be divided into several periods.
1.2.1 First Attempt
There were some initial simulations using formal logic. McCulloch and Pitts (1943) developed models of neural networks based on their understanding of neurology. These models made several assumptions about how neurons worked. Their networks were based on simple neurons which were considered to be binary devices with fixed thresholds. The results of their model were simple logic functions such as "a or b" and "a and b". Another attempt was by using computer simulations. Two groups (Farley and Clark, 1954; Rochester, Holland, Haibit and Duda, 1956). The first group (IBM researchers) maintained closed contact with neuroscientists at McGill University. So whenever their models did not work, they consulted the neuroscientists. This interaction established a multi-disciplinary trend which continues to the present day. 1.2.2 Promising & Emerging Technology
Not only was neuro-science influential in the development of neural networks, but psychologists and engineers also contributed to the progress of neural network simulations. Rosenblatt (1958) stirred considerable interest and activity in the field when he designed and developed the Perceptron. The Perceptron had three layers with the middle layer known as the association layer. This system could learn to connect or associate a given input to a random output unit. Another system was the ADALINE (ADAptive LInear Element) which was developed in 1960 by Widrow and Hoff (of Stanford University). The ADALINE was an analogue electronic device made from simple components. The method used for learning was different to that of the...
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