DocumentCode :
1388959
Title :
Maneuver Prediction for Road Vehicles Based on a Neuro-Fuzzy Architecture With a Low-Cost Navigation Unit
Author :
Toledo-Moreo, Rafael ; Pinzolas-Prado, Miguel ; Cano-Izquierdo, Jose Manuel
Author_Institution :
Dept. of Electron., Comput. Technol. & Projects, Tech. Univ. of Cartagena, Cartagena, Spain
Volume :
11
Issue :
2
fYear :
2010
fDate :
6/1/2010 12:00:00 AM
Firstpage :
498
Lastpage :
504
Abstract :
Collision avoidance is currently one of the main research areas in road intelligent transportation systems. Among the different possibilities available in the literature, the prediction of abrupt maneuvers has been shown to be useful in reducing the possibility of collisions. A supervised version of dynamic Fuzzy Adaptive System ART-based (dFasArt), which is a neuronal-architecture-based method that employs dynamic activation functions determined by fuzzy sets, is used for maneuver predicting and solving the problem of intervehicle collisions on roads. In this paper, it is shown how the dynamic character of dFasArt minimizes problems caused by noise in the sensors and provides stability on the predicted maneuvers. Several experiments with real data were carried out, and the SdFasArt results were compared with those achieved by an implementation of the Incremental Hierarchical Discriminant Regression (IHDR)-based method, showing the suitability of SdFasArt for maneuver prediction of road vehicles.
Keywords :
adaptive systems; collision avoidance; fuzzy neural nets; fuzzy set theory; regression analysis; road vehicles; traffic engineering computing; SdFasArt; collision avoidance; dynamic fuzzy adaptive system ART; fuzzy sets; incremental hierarchical discriminant regression method; low-cost navigation unit; maneuver prediction; neuro-fuzzy architecture; road intelligent transportation systems; road vehicles; Collision-avoidance support; inertial sensors; maneuver prediction; neuro-fuzzy;
fLanguage :
English
Journal_Title :
Intelligent Transportation Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
1524-9050
Type :
jour
DOI :
10.1109/TITS.2009.2039011
Filename :
5393029
Link To Document :
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