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Over the past decade, the field of automated intelligent transport systems has been the focus of intensive research. This paper proposes an intelligent neural network based driving system using artificial net extension (INDSANE), an advanced automated transport system with considerable advantages over previous attempts in this field. The system uses a multi-layer feed-forward neural network with back propagation learning. In addition, the design of INDSANE involves the convergence of a plethora of technologies like a global positioning system (GPS), a geographic information system (GIS), and laser ranging. INDSANE can guide 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 INDSANE scores against the competition is that the system is completely domain independent and incurs a lot less processor overhead. INDSANE 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 INDSANE'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. INDSANE also employs a technique called artificial net extension for negotiating traffic signals. A simulation of INDSANE's neural network was performed on a variety of network topologies, and the best network selected.