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.