Mechanism for an Intelligent Neural network based Driving system

Over the past decade, the field of automated intelligent transport systems has been the focus of intensive research. This paper proposes a Mechanism for Intelligent Neural network based Driving system (MIND), an advanced automated transport system with considerable advantages over previous attempts in this field. The system uses a multilayer feedforward neural network with backpropagation learning. In addition, the design of MIND involves the convergence of a plethora of technologies like the Global Positioning System (GPS), a geographic information system (GIS), and laser ranging. MIND can guide a mobile agent through a hostile and unfamiliar domain after being trained by a human user with domain expertise. One of the many areas in which MIND scores against the competition is that the system is completely domain independent and incurs a lot less processor overhead. MIND thus provides more functionality even though it requires a lot less input as compared to other attempts in this field This reduction in the size of the input vector translates into more efficient and faster processing. Another of MIND´s hallmark features is its ability to negotiate turns and implement lane-changing maneuvers with a view to overtaking obstacles. It does this by employing a novel technique, selective net masking. A simulation of MIND´s neural network was performed on a variety of network topologies, and the best network selected.