Gene libraries for a next generation warning system in Intelligent Transportation

Driver warning systems are the first step towards Intelligent Transportation System. There is a need for next generation warning systems that can integrate the information that is currently available in the vehicles with the information about the environment that the vehicles are operating in order to make more informed and accurate decisions. Integration of data from such different sources implies higher complexity of computation which is difficult to implement in real-time. Thus it is necessary to develop new methods that can integrate huge amount of data while meeting the hard real-time constraints of Intelligent Transportation Systems. In this paper, we introduce gene libraries that are based on the processes involved in gene expression. It is shown that gene libraries are capable of reducing the complexity of the problem by storing only the relevant information. A formulation for next generation warning system within the framework of gene libraries is proposed and simulations are presented that compare this approach with fuzzy inference system. Results show that gene library based approach is at least 23 times faster and 3.85 times more space efficient than fuzzy inference systems based approach.