HYBRID NEURAL-BASED DECISION LEVEL FUSION ARCHITECTURE - APPLICATION TO ROAD TRAFFIC COLLISION-AVOIDANCE

A hybrid decision level architecture for a road collision risks avoida nce system is presented. The goal of the decision level is to classify the behavior of the vehicles observed by a smart system or vehicle. T he knowledge of vehicle behavior enables the best management of the sm art system resources. The association of a model to each observed vehi cle mainly enables the limitation of inference and of the set of actio ns to be activated; thus the interactions between system levels can be more intelligent. The decision level of this architecture is composed of a neural classifier, which is associated to a numerical classifier . Each of these classifiers provides decisions that are expressed with in the framework of fuzzy theory. An optimal fusion policy is reached using the functional neural network tool. (C) 1998 Society of Photo-Op tical instrumentation Engineers.