Sentient autonomous vehicle using advanced neural net technology

Over the past decade, the field of automated intelligent transport systems has been the focus of rigorous research. This paper proposes sentient autonomous vehicle using advanced neural net technology (SAVANT), an automated transport system with significant 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 SAVANT involves the convergence of a plethora of technologies like a global positioning system (GPS), a geographic information system (GIS), and laser ranging. SAVANT 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 SAVANT scores against the competition is that the system is completely domain independent and incurs substantially less processor overhead. SAVANT thus provides more functionality even though it requires considerably 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 SAVANT'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 SAVANT's neural network was performed on a variety of network topologies, and the best network selected