Hybrid neural-based decision level fusion architecture: application to road traffic collision avoidance
Kurosh Madani · Optical Engineering · 1998
A hybrid decision level architecture for a road collision risks avoidance system is presented. The goal of the decision level is to clas- sify the behavior of the vehicles observed by a smart system or vehicle. The knowledge of vehicle behavior enables the best management of the smart system resources. The association of a model to each observed vehicle mainly enables the limitation of inference and of the set of actions 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 within the framework of fuzzy theory. An optimal fusion policy is reached using the functional neural network tool. © 1998 Society of Photo-Optical Instrumentation Engineers. (S0091-3286(98)01202-1)