Maneuver Prediction for Road Vehicles Based on a Neuro-Fuzzy Architecture With a Low-Cost Navigation Unit
作者： Rafael Toledo-Moreo , Miguel Pinzolas-Prado , Jose Manuel Cano-Izquierdo
DOI: 10.1109/TITS.2009.2039011
关键词:
摘要: Collision avoidance is currently one of the main research areas in road intelligent transportation systems. Among different possibilities available literature, prediction abrupt maneuvers has been shown to be useful reducing possibility collisions. A supervised version dynamic Fuzzy Adaptive System ART-based (dFasArt), which a neuronal-architecture-based method that employs activation functions determined by fuzzy sets, used for maneuver predicting and solving problem intervehicle collisions on roads. In this paper, it how character dFasArt minimizes problems caused noise sensors provides stability predicted maneuvers. Several experiments with real data were carried out, SdFasArt results compared those achieved an implementation Incremental Hierarchical Discriminant Regression (IHDR)-based method, showing suitability vehicles.
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