Identification and application of neural operator models in a car driving situation

Summary form only given, as follows. The authors examined whether neural networks are applicable as operator models in man-machine systems. A two-lane car driving task was used as an experimental paradigm. Various network architectures were tested. In particular a combination of functional link and backpropagation was proposed as a novel, rapidly trainable structure. It was shown experimentally that individual human driving characteristics are indeed identifiable from the input/output relations of the trained networks. Neural nets are therefore candidates for operator models. The applicability of such models as an information source for driver assistant systems was demonstrated