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Collaboration, Automation, and Cognitive Aids Technologies to Solve Complex Transportation Problems

It has always been the endeavor of scientists and engineers to develop systems that could mimic the human brain and adapt themselves to complex situations in order to arrive at a solution to the problem at hand in an efficient and effective manner. These systems would not only be capable of action but also of thought. Heuristic and algorithmic technologies are needed for the automation of cognitive activities. There is a need to develop tools, which are capable of assessing a situation at hand, alerting the human part of the system to various concerns and providing advice on possible responses, and possibly proceed towards solving the problem. This paper deals with the development of a human-machine optimal automated transportation system. In this paper it is proposed to develop specific algorithms to predict and avoid traffic congestion and collision. Two such tools that can be used with each other to produce optimal results are the Global Positioning System (GPS) and the Kalman filter. The Kalman filter is an algorithm, which can be used to predict the future state of a system based on the past information. This could be useful when we are dealing with typically, the problem of collision. A Kalman filter could be designed to give an accurate estimate of the future state of a highway or an intersection based on the data collected for that particular highway or intersection by GPS. The goal is to successfully use data fusion algorithms specifically like GPS and Kalman filters to improve the safety and efficiency of transportation systems particularly with respect to collision avoidance.